# Solargis > Solargis provides solar and meteorological data, PV engineering software, and consultancy services covering the entire solar power plant lifecycle — from site screening and energy yield assessment through operational monitoring and forecasting. Last reviewed: 2026-07-03 Key facts: - Solar and meteo data covers 99% of the world's population with 30+ years of history, at up to 250 m spatial and 1-minute temporal resolution. - Data accuracy is validated against 320+ ground measurement stations worldwide; methodology is peer-reviewed and published. - Used by 1,200+ organizations, supporting roughly 9,000+ large solar projects per year. - Products: Evaluate (PV design and energy yield simulation), Prospect (site screening and pre-feasibility), Monitor (performance monitoring), Forecast (power forecasting), Analyst (data QC and analysis), Solarmaps (free interactive maps). - Audience: solar developers and IPPs, asset managers and O&M teams, financial institutions and lenders, EPC contractors, grid operators, and solar data analysts. - Global Solar Atlas, a free public solar-resource mapping tool built with the World Bank/ESMAP, is a related Solargis initiative on a separate domain (globalsolaratlas.info) and is not covered by this file. This file (llms-full.txt) contains the full text of every page linked from llms.txt, concatenated in the same order, for use as complete inline context. ================================================================================ # SECTION: Products -------------------------------------------------------------------------------- ## Solar & meteo data and analysis software | Solargis Source: https://solargis.com/products # Products ### [Solargis Prospect](https://solargis.com/products/prospect) - Reliable and accurate solar data - Fast sites comparison - Comprehensive environmental overview - Analytics and solar power calculator - Collaboration and multilingual support ### [Solargis Evaluate](https://solargis.com/products/evaluate) - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Unmatched level of detail and accuracy - PV simulation based on ray tracing and Perez all-weather sky model ### [Solargis Monitor](https://solargis.com/products/monitor) - One solar data source for all sites - Gap- and error-free solar radiation data - Minimize meteo station inputs uncertainty - Benchmark planned performance with reality - Near real-time PV output assessment ### [Solargis Forecast](https://solargis.com/products/forecast) - Solar power output forecast for up to 14 days - Nowcasting every 15 minutes up to 3 hours - Minimize penalties from grid operators - Schedule maintenance based on the forecast - Manage the variability of your PV plant portfolio ### [Solargis Analyst](https://solargis.com/products/analyst) - Visualize complex and big solar datasets - Compare measured data to model outputs - Identify and clean errors from measurements - Harmonize multisource input streams - Streamline solar data management ### [Solargis Integrations](https://solargis.com/products/integration) - Long-term averages API - Typical Meteorological Year API - Historical Time Series API - Solargis Monitor API - Solargis Forecast API - Solargis data via SFTP ### [Solargis Solarmaps](https://solargis.com/products/solarmaps) - Monthly weather variation compared to LTAs - Harmonized last month’s summaries of key PV performance indicators - Weather variability in geographical context - Effective communication with stakeholders - Independent 3rd party assessment of PV performance -------------------------------------------------------------------------------- ## Solargis Evaluate: PV Simulation with Bankable Solar Data Source: https://solargis.com/products/evaluate # Solargis Evaluate: PV design and simulation Solar and meteo data, PV design & energy yield simulation in one cloud-based solution ## What is Solargis Evaluate? Solargis Evaluate combines PV design, energy yield simulation, and [bankable solar data](https://solargis.com/technology/accuracy-and-validation) in one cloud-based platform. Developers and engineers use Evaluate to design utility-scale projects, validate energy production estimates, and [secure financing](https://solargis.com/solutions/energy-yield-simulation) with reports trusted by investors. - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Bankable simulation results for project financing - PV simulation based on ray tracing and Perez all-weather sky model ## [Cutting-edge PV design capabilities](https://solargis.com/products/evaluate/features#pv-design-energy-system-designer) Design your solar power plant with precision using our advanced [3D Energy System Designer](https://solargis.com/solutions/optimizing-power-plant-design). Tailor every aspect with over 150 customizable parameters, adapting PV module layout to terrain conditions and row spacing to ensure the optimal PV layout for maximum efficiency. Define additional elements like buildings, trees, and hedges to accurately replicate the physical world in the 3D scene. Quickly create reliable PV designs, selecting from our catalog of verified PV components to optimize energy production. ## [Evaluate 30+ years of data through charts and visualizations](https://solargis.com/products/evaluate/features#data-analysis) Assess PV energy yield potential efficiently through detailed charts, tables, and automated reports. Visualizations allow for easy interpretation of interannual variability, monthly, daily, hourly, and [sub-hourly views of the data](https://solargis.com/resources/blog/best-practices/the-pros-and-cons-of-1-minute-15-minute-and-60-minute-solar-data). Generate a comprehensive report that evaluates the site’s solar resource, meteorological conditions, PV energy yield potential, estimated losses, and [long-term PV production forecast](https://solargis.com/resources/blog/best-practices/from-time-series-to-tmy-when-to-use-each). [More about data visualizations](https://solargis.com/products/evaluate/features#data-analysis) ## [Address weather extremes and variability early on](https://solargis.com/products/evaluate/data-specs) [Sub-hourly Time Series data](https://solargis.com/resources/blog/best-practices/the-pros-and-cons-of-1-minute-15-minute-and-60-minute-solar-data) offers valuable insights into unusual weather conditions such as extreme air temperature, wind gusts and heavy snowfalls. This detailed information allows for the optimization of power plant designs to meet specific business objectives, such as return on investment (ROI) and [levelized cost of energy](https://solargis.com/solutions/energy-yield-simulation) (LCOE), while ensuring the power plants can withstand extreme weather events and maintain performance efficiency. ## [15-minute PV simulation with physics-based models](https://solargis.com/products/evaluate/resources) Leverage 15-minute Time Series data spanning more than 30 years to simulate PV performance with unparalleled accuracy. [The PV simulation engine](https://solargis.com/technology/expertise) considers verified PV component specifications, shading, high-resolution terrain, and local solar, weather, and environmental conditions, including ground surface albedo, soiling and snow. Each simulation’s results can be turned into a report that provides complete information for effective communication with stakeholders and securing financing for the project. ## [Data trusted by financial stakeholders](https://solargis.com/technology/expertise) Solargis Evaluate provides bankable data accepted by [banks, investors, and other stakeholders](https://solargis.com/services/pv-energy-yield-assessment). Our algorithms based on real-world physics, grounded in the latest peer-reviewed scientific literature and built on transparent, traceable, and validated models, leave no room for ambiguities in any simulation step. Our scientific, rigorously documented approach – along with numerous independent [validation studies](https://solargis.com/technology/accuracy-and-validation) – has earned Solargis its reputation as the most reliable source of solar resource data on the market. [Technology behind bankable data](https://solargis.com/technology/expertise) ## [Sample data](https://solargis.com/products/evaluate/resources) Download sample data and reports from a specific site. You can also check technical documentation or learn about the technology behind this solution. ## Related products and services ### [Solargis TMY API](https://solargis.com/products/integration/solargis-api-typical-meteorological-year-tmy) - Asynchronous API - Computed on-demand - Bankable data validated against ground measurements - API key-based, userless tokens available - Easy integration with custom tools and PV software ### [Solargis Time Series API](https://solargis.com/products/integration/solargis-time-series-api) - Asynchronous API - Supports high-volume requests - Validates inputs before charging credits - API key-based, userless tokens available - Easy integration with custom tools and PV software ### [Solar Resource & Meteo Assessment](https://solargis.com/services/solar-resource-assessment) For larger and utility-scale solar projects, you need long-term solar and meteorological data to be regionally validated with the right uncertainty estimates. We can provide you with a detailed solar resource validation and assessment report as an add-on alongside standard data delivery. ### [Site Adaptation of Solargis Models](https://solargis.com/services/site-adaptation-of-solargis-models) Combine satellite data with on-site measurements to reduce the uncertainty of estimated energy output and achieve more accurate financial estimates. The Site Adaptation of Solargis Models service will give you locally enhanced solar and meteo parameters, enabling you to reduce uncertainty of power plant design and energy yield simulations. ### [PV Energy Yield Assessment](https://solargis.com/services/pv-energy-yield-assessment) For financial risk assessment, investors and developers require reports of expected energy production, including uncertainties, as well as related solar and meteorological data inputs. We offer an independent and impartial evaluation of PV yield assessment with our proprietary simulation tools and models. These are based on 15+ years of experience working on large and medium-scale PV power plants around the world. ### [PV Performance Assessment](https://solargis.com/services/pv-performance-assessment) Data-driven insights from our PV Performance Assessment report conducted after months or years of the plant’s operation will help you optimize its performance. The report also provides a revised and more accurate long-term energy yield estimate for refinancing or new asset acquisition purposes. ### [PV Variability & Storage Optimization Study](https://solargis.com/services/pv-variability-and-grid-integration-study) The PV Variability & Storage Optimization Study delivers statistical data and insights to project developers needed for designing and managing PV-plus-storage systems. The study provides the most realistic data on PV power generation for grid integration analysis. -------------------------------------------------------------------------------- ## Solargis Evaluate features: 3D design, simulation & reports Source: https://solargis.com/products/evaluate/features # Solargis Evaluate features ## What makes Solargis Evaluate different? - [Find out](https://solargis.com/resources/solargis-vs-alternatives) - See how Solargis Evaluate compares to other tools solar developers use today. ## One integrated system for solar project development Solargis Evaluate addresses the existing norms of industry-adopted practices by consolidating them into a single cloud-based software solution. ### Unified platform The Solargis platform brings all Solargis products under one roof, offering one environment for solar project development. Solargis Prospect and Solargis Evaluate, both part of the Solargis platform, ensure reliable decision-making from early screening to securing project financing. In Prospect, you can explore and quickly assess and compare multiple locations to identify the most promising ones using reliable solar resource data. Once a location is selected, you can proceed to Evaluate, where high-resolution data is used for detailed project analysis and bankability assessments - all without needing to leave the Solargis platform. ### User & project management in one place Access Solargis Evaluate alongside other Solargis solutions through a single, streamlined interface. Manage users and team collaboration seamlessly in one platform. Easily add or remove team members, and onboard new users with just a few clicks, ensuring everyone has the access they need to work efficiently. Keep track of all your projects through Solargis Home. View your complete project list, see where each project is assigned, track the latest activity, and access everything from a central platform. Filter out your most recent projects, restore archived projects and manage your labels. ### Flexible credit-based system Within your Solargis Evaluate tier subscription, the purchased credits give you flexibility in your spending. If you are unsure whether you want to use TMY or Time Series data at the moment of purchase, you can decide what dataset you want to use at the time of project activation. Once you run out of credits, you can buy additional ones, or if you are left with remaining credits at the end of your subscription period, you can opt to transfer them into your next subscription. This is valid only on condition that you remain in the same subscription plan.​ ### Early stage or full evaluation When assessing a location, you can choose to what extent you want to analyze the site conditions. With Solargis Evaluate, you can select between a full evaluation of your project with access to all Solargis Evaluate features, including Time Series data, or get a simple view at your project with a TMY P50 dataset. **Early-stage evaluation — **As you enter the initial stages of PV project assessment, you can initiate the “Early stage” phase. This phase marks the beginning of the evaluation process, allowing for preliminary planning to ensure the project’s viability. TMY P50 data allows for a balanced view of expected performance without the need for Time Series data, making it an efficient choice for preliminary decision-making before moving into more detailed, high-resolution analysis. **Full evaluation — **When you decide that it’s time to move the project further, you can upgrade to full evaluation any time. In this phase you can work with sub-hourly Time Series data and gain access to the full suite of Solargis Evaluate features, such as generating bankable reports that help with securing project financing. ### Request data and consultancy services directly from the platform Within our platform, you can now request consultancy services or additional data services directly, saving time on email communication and file exchanges as the project data is already included in the request. You can directly request - Data services: High temporal resolution data: 1-, 5-, 10-minute data - TMY data (Pxx): P99, P95, P90, P75, P10 - GHI/DNI uncertainty - Consultancy services: Enhanced Solar Resource & Meteo Assessment - Site Adaptation of Solargis Model Time Series Data - Ground Surface Albedo Time Series - Enhanced PV Energy Yield Assessment - Ground Surface Albedo Evaluation Based on Site-Adapted Data Additional services reduce the uncertainty of PV power plant design and increase your confidence in the project’s long-term viability. Attract investments by achieving more accurate financial estimates and increase the credibility of project evaluation. ## Solar, meteorological and environmental data Use the finest data available for building sound financial plans. Solargis Evaluate works with data always ready to provide accurate irradiance inputs for your project sites. ### Global coverage and latest data availability Solargis provides solar and meteorological data within latitudes 60 S and 65 N. All land is covered except polar areas. At the moment of project activation, Solargis Evaluate generates Time Series for the available historical period up to last month, so that you are working with the latest data available. Long history of data includes typical weather as well as anomalies. Every new data request benefits from the latest model improvements in accuracy. ### Extensive ground validation Solargis offers the most extensive validation based on publicly available solar data coming from high-end, quality-controlled pyranometers and pyrheliometers. We are committed to transparency by publicly disclosing the detailed results of the validation in the Solargis Validation Report. — At the same time, we are continuously adding new validation sites all around the world to make sure that our solar models perform according to our high standards globally. ### Uncertainty estimates and Pxx values You can now directly request GHI and DNI model uncertainty in Solargis Evaluate. We estimate and present the GHI and DNI uncertainty by combining the uncertainty of the Solargis model and the uncertainty due to interannual variability, helping you to assess minimum expected values and probability of exceedance scenarios based on the available Solargis historical data. Besides uncertainty estimates you can now also directly request Pxx values (P10, P50, P75, P90, P95, P99) in Solargis Evaluate. Probabilistic scenarios are critical for quantifying and communicating the uncertainty in energy production forecasts, allowing stakeholders to balance risk and reliability in decision-making. Both uncertainty estimates and Pxx values are included for projects activated in full evaluation stage. ### Weather variability and extreme conditions Enhance the resilience of your PV design by accounting for extreme weather with 15-minute data and optimize for the best PV performance. By default, Solargis Evaluate works with 15-minute Time Series data referring to a history of more than 30 years, including all meteorological and environmental factors relevant for PV energy simulation and analysis. Using 15-minute Time Series data for PV power plant design captures high-frequency variations in solar and weather conditions. This leads to more accurate modeling of power output and helps optimize PV power plant technical components. As a result, the simulation accounts for both short-term fluctuations and long-term variability, optimizing the power plant design to meet the business model requirements and withstand extreme weather. By using the full history of data, you can adapt the PV design to account for short-term variability and long-term trends, while grasping the effects on future conditions, ensuring the system is resilient to potential shifts in temperature and other conditions. ## PV design with Energy System Designer Create a digital twin of the future power plant using the most advanced and detailed PV energy system designer available. ### Multiple terrain models and horizon shading When designing a PV power plant, it is possible to choose from multiple terrain models that lead to different layouts: - Copernicus in 30m resolution (1 arcsec | global | 2011-2015 | surface model - including trees and buildings) - SRTM (Google Earth) in 30m resolution (1 arcsec | global | 2000 | terrain model - excluding trees and buildings) - SRTM (Solargis) in 90m resolution (3 arcsec | global | 2000 - 2017 | terrain model - excluding trees and buildings) - Upload your own terrain elevation data in GeoTIFF format if you want to override the terrain model with higher-resolution or project-specific data To support more effective site planning, you can highlight areas that exceed a specified slope threshold, making it easier to identify zones that may require fixed-mount structures or more precise tracker placement. Experimenting with different elevation data results in different energy yields, but doesn’t affect solar and meteorological data. Horizon shading is calculated from the environmental data and depicts the terrain elevation and shading in the location’s surroundings. You can modify and enter your own horizon data if needed. ### Terrain-adapted PV layout Dealing with undulating terrain? Design PV systems that conform to the natural contours of the land and mitigate many of the risks associated with erosion, ground disturbances and construction. In the 3D Energy System Designer you can design fixed-mount and tracker PV systems that adapt to the landscape, following real-world terrain undulations without flattening the site. Intelligent table placement delivers accurate layout and shading-loss calculations, even on challenging orography, all while respecting clearance and orientation. The PV simulation is performed at the cell level, ensuring precise inter-row shading accuracy across irregular terrain. ### Draw service lines, restricted areas, and line objects The 3D interactive scene enables viewing the system from various angles and perspectives, providing a clearer understanding of the layout in a geographical context and potential issues. You can define different types of objects, such as buildings, trees, hedges, and other physical elements that might affect the solar power plant layout. By using restricted line tool you can draw the service line and define its width, which allows for the placement of inverters into these safe zones. Line objects can be used to draw any objects that impact the shading of the PV power plant and their height can be defined up to 500m. If you have defined these objects using a different tool, you can simply import them in a KML format. It is possible to import segments (non-overlapping polygons), restricted areas (overlapping polygons), and shading line objects (polylines). ### Electrical layout The capabilities of the energy system designer go beyond a simple 3D design and selection of PV hardware. Our PV design incorporates advanced settings for electrical layout, including inverter setup, placement of transformers, and defining cabling losses. The system automatically selects the most appropriate generic PV modules and inverters and intelligently places transformers. It lets you manually define factors like cabling, degradation, and environmental losses. If you have a preferred model of a commercial PV modules or inverters, you can directly pick specific models from the PV Components Catalog. You can then optimize the position of the inverter according to the limitations of the location. You can tailor it to your project’s power output, efficiency, module compatibility, and unique requirements of your project. Detailed information about each inverter option is provided, including technical specifications and performance data. ### Smart collision detection Each time you adjust the design, the system immediately evaluates interactions between PV tables, terrain, and inverter units. Potential collisions are detected right after every action, highlighting the problematic area in the scene. ### C&I rooftop systems Rooftop PV comes with its own set of constraints: slopes, orientations, obstacles, and limited space. The Energy System Designer handles all of it. Define the roof plane by slope and azimuth, or upload your drone elevation model for a precise fit. Draw your segments, place your arrays, and run the simulation - the same path you use for ground-mount, now adapted for rooftops. Plan around obstacles directly in the Energy System Designer. Use restricted areas and lines to mark access routes, HVAC units, skylights, and elevator shafts, with configurable buffers wherever clearance is needed. On tight rooftops, string overflow mode lets strings flow across rows without table boundaries, so you can pack more capacity into the space you actually have. ### Cable design Cable routes and cable sizing now belong together. When editing the route, the sizing table calculates cable types based on distance and electrical requirements. Cable length respects terrain shape and burial depth, giving you a design aligned with European technical norms (IEC 60287). A robust cable sizing table evaluates optimal cable types based on spatial distance and electrical characteristics of connected equipment. The calculation follows the norm IEC 60287 helping you select a cable that meets 3 essential criteria: - current rating - acceptable voltage drop - short-circuit withstand capability ### Interactive bill of materials The in-app bill of materials lets you estimate project costs as you design. Assign unit prices and spare quantities directly to components. Prices and quantities update in real-time as you modify the layout, with the total CAPEX always visible to reflect the cost impact of your design decisions. You can add custom items that are not predefined in the bill of materials for more accurate financial planning. ### Energy system validation As you design, the validator runs in real time and flags issues the moment they appear: voltage mismatches, incorrect inverter settings, grid connection issues, and more. No need to wait until simulation to find out something is wrong. Each failed check explains the rule in plain engineering terms and points you to the exact setting to fix. Where a quick resolution is possible, you can apply a suggested fix directly from the notification, without hunting through the configuration yourself. All validation checks must pass before you can run a PV simulation. By default you see only what needs attention, with the option to review all checks at any time. ## PV Components Catalog Solargis Evaluate is integrated with a detailed, collaborative, and searchable platform of verified PV components from manufacturers all around the globe. It lets you source reliable specifications for PV components to ensure accurate layout and energy yield simulations. ### How does it work? The PV Components Catalog is a reliable source of technical specifications, verified by Solargis experts and algorithms, adhering to a transparent set of verification steps. A dedicated team maintains and organizes the catalog through close collaboration with manufacturers, covering both current component specifications and historical data for previous models. The catalog serves as a platform for real-time updates, ensuring you always have access to the latest product information. The PV Components Catalog integrates with Solargis Evaluate for immediate use of publicly listed components in energy yield simulations and PV designs. You can also connect your Solargis Evaluate account via API to add your own components. Learn more about PV Components Catalog ## PV energy yield simulation Our PV simulator utilizes a real-world model and ray tracing simulation, ensuring reliable data and accurate results. It’s an essential tool for solar engineers seeking optimal PV energy performance. ### Scalable technology The Solargis PV simulator is built on scalable cloud infrastructure with intelligent preprocessing capabilities. The simulation engine delivers high precision in computation, while maintaining high accuracy of the results. Additionally, it supports parallel simulation requests, enabling multiple scenarios to run simultaneously. Most importantly, Solargis Evaluate does not block your hardware resources, allowing you to work simultaneously on multiple tasks without interruption. ### Ray tracing technology Solargis PV simulation uses ray tracing technology and Perez all-weather sky model. It considers all kinds of shading defined by both horizon data in the Solargis data model and shading objects present in the physical world model. The precise path of sunlight is simulated by tracing individual rays between the sky and solar cell surfaces as it travels through the 3D environment. The ray tracing algorithm is based on Monte Carlo backward path-tracing, where we trace back the source of light to each cell and take into account multiple bounces until the source of light is reached. This is validated with bifacial_radiance. 3D ray tracing-based simulation allows us to calculate for hilly terrains and bifacial panels with no limitation. Ray tracing delivers more accurate results by leveraging detailed geometric information about the scene and precise calculations for each ray. This provides exceptional precision in simulations, although with greater computational effort compared to the faster but less detailed view factor method. ### Run PV simulations using 1-minute TMY data For projects in the Full evaluation stage, you can now request, export, and run a PV simulation using 1-minute TMY data. Considering high-resolution 1-minute data from the initial design phase is essential for achieving higher accuracy in performance forecasts, optimizing system configuration for local conditions, and maintaining compliance with evolving technical and grid requirements. This results in more robust, efficient, and bankable solar power projects. Why 1-minute resolution matters in PV power plant design: - **Refine DC/AC ratio optimization:**Capture real-time dynamics between PV array sizing and inverter capacity. This minimizes inverter clipping losses and supports more cost-effective system designs. - **Reveal short-term power variability:**Accurately reflect rapid irradiance changes, such as cloud edge effects and intermittent cloud cover that are typically smoothed out in 15- or 60-minute datasets. This leads to more accurate production estimates and improved system performance. - **Gain site-specific meteorological insights:**Analyze local weather patterns, temperature fluctuations, and humidity impacts in greater detail, enabling more precise site suitability assessments. - **Enhance energy storage and power smoothing:**Better represent real-world fluctuations for improved storage system sizing and support for advanced grid compliance and power quality requirements. - **Strengthen financial modeling and risk assessment:**Reduce the systematic overestimation of energy yield and improve long-term performance predictions, resulting in more reliable and bankable project outcomes. Adopting 1-minute resolution data represents a significant advancement in PV system design methodology. It provides more accurate performance predictions and enables better-optimized installations across diverse geographical locations and climate conditions. Using 15-minute data instead of 60-minute data typically reduces production overestimation by approximately 50%, though significant differences from 1-minute data remain. ### Modeling snow and soiling losses using the Solargis model Simulate snow and soiling losses directly in the Energy System Designer using the Solargis model. Both models use high-resolution solar resource data tailored to your location and energy system configuration, accounting for variables like panel tilt, temperature, wind, and geographic conditions to deliver precise, site-specific results. Define your cleaning schedule the way your O&M contract actually works: set specific dates, choose from manual or automated cleaning method presets, or configure your own efficiency and speed. The system calculates how long each campaign takes based on your plant capacity and flags any scheduling conflicts before you run the simulation. For tracker arrays, set the night stowing angle and a separate snow stowing angle that activates automatically when a snow event is detected; both stored per array and copied with the rest of your array configuration. Accounting for snow and soiling losses has a direct impact on forecasted energy production, reducing the risk of overestimations in your bankable report and financial assessments. ### TMY vs. Time Series **Early-stage evaluation with TMY:** TMY data aggregates historical weather patterns into a "typical" year, providing a reliable representation of average solar radiation, air temperature, and, to a lesser extent, wind speed. For early-stage project evaluation, TMY data offers a quick and cost-effective means of estimating solar and PV potential. This makes it useful during the pre-feasibility phase, where developers need to perform high-level assessments to determine the viability of a project. However, TMY data is far from perfect. It smooths out significant weather fluctuations, and its low granularity—typically in hourly time step —fails to capture critical types of variability. These include: - Short-term variability (intra-hourly fluctuations) - Interannual variability and seasonal changes - Long-term variability and climate change The primary limitation of TMY data is its failure to capture extreme weather events like storms or high winds, which can disrupt solar generation. By averaging these extremes into typical patterns, TMY underestimates real-world variability and leads to overly optimistic performance predictions. **Full evaluation with Time Series data:** As a project moves from the early stage into full evaluation, it is crucial to transition from TMY data to high resolution Time Series data. Unlike TMY, Time Series data is typically available in 15-minute intervals, covering periods of more than 30 years. This resolution provides developers with a much more granular, accurate representation of weather patterns and variability, allowing them to simulate solar power generation in real-world conditions with far greater precision. For example, high resolution data captures the impacts of extreme weather events like cloud cover, high winds, air pollution, or snow. These events, which are often overlooked in TMY-based simulations, can have significant consequences for energy generation, system performance, and project financials. The contrast in data volume between TMY and Time Series is evident: - TMY typically offers around 8,760 data points per year (one per hour) - High resolution data, by comparison, provides over 1 million data points per year (at 15-minute intervals) ### Bifacial PV simulation & in-house calculated albedo data The ground’s surface albedo influences the amount of sunlight that reaches the solar modules, particularly in utility-scale solar installations, where the spacing between modules allows sunlight to hit the ground. Ground surface albedo becomes especially important when bifacial PV systems are installed. Understanding albedo can lead to higher energy yields, making it an important consideration in the design of bifacial PV systems. Our approach uses a sub-hourly Time Series of solar radiation and in-house calculated albedo data, making it possible to capture the short-term variability in front and rear-side solar radiation received by PV modules throughout the day. This approach, combined with ray tracing technology that accounts for factors such as shading from torque tubes, provides a more accurate simulation of how reflected light interacts with bifacial modules. The energy system designer allows for adjustments to ground surface albedo settings to reflect ground reflectivity as a whole, or separately per every segment with monthly granularity. ### PV system losses In Solargis Evaluate we approach PV system losses in greater detail. Our systematic approach to PV system losses starts by dividing them into optical and electrical parts. By methodically addressing each type of loss and employing robust calculation methods, we provide a comprehensive and reliable estimate of the expected energy output based on the actual power plant definition. We provide monthly breakdowns of PV system losses by category, providing seasonal and interannual variability of monthly PV losses. This helps in making informed decisions about component sizing (e.g., inverter capacities) and PV system configurations to accommodate seasonal peaks and decrease stakeholders’ financial risks. Some examples of the PV losses we take into account include: - Shading losses - Spectral losses - Inverter losses - Conversion losses - Angular losses - Auxiliary losses - AC cable losses ## Solar and PV data analysis with charts and visualizations No need to perform your own data analysis or visualizations in 3rd party tools. With our extensive data analytics section, you will gain access to a collection of various charts and tables providing a great overview of the site’s solar, meteorological, and PV power potential. ### Detailed solar resource data visualizations Deep dive into Global horizontal irradiation, Global tilted irradiation, Direct normal irradiation, and Diffuse horizontal irradiation, presented in charts and tables in yearly, daily, hourly, and sub-hourly resolutions. Understand your project’s solar data and analyze the variability of the solar resource over the full period of satellite data retrieval. ### Meteorological and environmental variables The charts detailing parameters such as air temperature, precipitation, ground surface albedo, and precipitable water, with data presented in graphs and tables at yearly, monthly, and hourly resolutions provides insights into the site local conditions. For example, to help identify strong winds that may limit operation or threaten the structural integrity of the PV system, wind parameters, including wind direction and wind gusts, are provided within the delivered data. ### Interannual variability Thanks to the fact that model inputs start from 1994, there is enough data to calculate the expected interannual variability for the site conditions. Accounting for interannual variability lets you create designs that can withstand extreme weather. Moreover, it will significantly mitigate inaccurate estimates, enhancing the resilience of design and lowering the risk of overestimated financial returns. ### Performance Ratio calculation Besides the theoretical specific and total photovoltaic power output, we calculate the Performance Ratio (PR), which is based on the EIC 61724-1 standard. This is to help you understand the maximum potential of your PV design and set reasonable expectations for operational targets. The PV statistics section includes charts and tables for theoretical photovoltaic power output values, which can be found in yearly, daily, hourly, and sub-hourly resolutions. This helps with informed decision-making, accurate budgeting, and proactive management to optimize energy production and mitigate risks. ### Inverter statistics Understanding how your inverters operate over time reveals where energy is maximized and where performance is being left on the table. The PV analytics section shows the share of time the system spends in each operating mode, giving a clear picture of efficiency, constraints, and optimization opportunities. We track four key operating states: - **Optimum operation:** Your inverter is running in its ideal voltage range, exactly as designed by the manufacturer. This is where performance, efficiency, and reliability are at their best. - **Standby:** The inverter is available but not actively converting energy. - **Self-clipping:** Energy is available and can be converted, but the chosen working point is not ideal. Adjustments here may unlock additional production. - **Grid power limitation:** Production is capped because the grid cannot accept more power. This highlights potential opportunities for storage, grid upgrades, or smarter energy management. By seeing how often each of these states occurs, you can quickly identify bottlenecks, quantify losses, and make informed decisions to increase yield and return on investment. ### PV long-term degradation and production forecast PV performance degrades by some percentage every year. The degradation rate depends on factors such as module type, system design, environmental conditions, and maintenance practices. We provide a year-by-year breakdown that enables you to assess your system’s performance over its lifetime. The calculations incorporate the user-provided degradation rate, offering a more accurate estimate of the system’s PV energy yield. ### Sub-hourly variability histograms For key parameters such as GHI, DNI, GTI, and PVOUT we provide monthly histograms in up to 15-minute resolution. The combination of histograms offers a richer, multi-dimensional perspective on solar system performance, leading to improved long-term estimates, performance optimization, and variability insights of a PV energy system. **Monthly histograms** offer valuable insights into the high-frequency fluctuations of solar radiation throughout the day, providing insights into the variability of solar power delivered to the grid. This analysis helps manage the risks associated with limited capacity of grid to absorb this variation. Frequent variability of PV output may also result in higher energy losses due to the design of inverters and strings. High-frequency variability and technical limitations of PV components are considered in the PV simulation and reflected in the section on PV losses. ## Bankable report and customizable exports With Solargis Evaluate you can generate reports and store them in one place. Get consultancy-grade reports for your site - making it easier than ever to assess PV potential and secure project financing. ### PV simulation technical note A technical note is designed to be only a preliminary report. It is based on the TMY P50 data and is typically generated during the early stage of the project development. The PV simulation technical note includes a yearly and monthly analysis of solar energy potential and climate parameters, based on TMY data. ### PV energy yield assessment This extensive report is based on a Time Series energy system data, primarily calculated by models using satellite, meteorological, and environmental input data. This report evaluates the long-term power production potential for any solar power plant design. ### Vector‑based 2D PV power plant layout The visual layout helps quickly verify site assumptions and component placement, making it easier to explain project intent to permitting authorities, investors, EPC partners, and landowners. Project reports include a vector‑based 2D layout of the power plant, bringing Solargis cartographic standards into Evaluate reports. The map supports deep zoom for detail inspection and contains key site context such as coordinates, north arrow, legend, and base map. The layout is an integral part of the PV report, tied to a unique report ID and digitally signed. Any third-party can verify that the vector drawing corresponds exactly to the calculated yield, quantities used in Bill of Materials, and CAPEX assumptions. ### Energy system summary The energy system designer helps you prepare alternative designs for a PV energy system. The energy system summary provides a comprehensive view of the technical details of a PV configuration. ### Supported formats for 3rd party tools Solargis Evaluate works with data representing a history of data available form meteorological satellites and synchronized with data from meteorological models. The data is delivered in industry standard formats, which are used by PV energy yield simulators. Apart from Solargis standard formats in JSON and CSV, we support commercial formats, such as PVsyst, NREL SAM, and HelioScope. -------------------------------------------------------------------------------- ## Solargis Evaluate data specs: Solar, meteo & PV parameters Source: https://solargis.com/products/evaluate/data-specs # Solargis Evaluate data specifications Solargis Evaluate provides comprehensive solar radiation, meteorological, and environmental data for accurate PV simulation. All parameters are available in 15-minute Time Series and TMY formats. Explore the complete data specification below. ## Solar, meteorological, and PV output parameters #### Solargis Evaluate includes these solar radiation parameters for PV energy yield simulation: | GHI | Global horizontal irradiation * | kWh/m2 ** | | --- | --- | --- | | DNI | Direct normal irradiation * | kWh/m2 ** | | D2G | Ratio of diffuse to global irradiation * | | | GTI | Global tilted irradiation | kWh/m2 ** | * including values not considering terrain shading ** for sub-hourly time resolutions, the parameter is provided as irradiance (W/m2) #### Meteorological data helps assess site conditions and optimize PV plant design for local weather: | TEMP | Air temperature at 2 meters | °C | | --- | --- | --- | | RH | Relative humidity at 2 meters above ground | % | | TD | Dew point temperature | °C | | WBT | Wet bulb temperature | °C | | WS | Wind speed m/s at 10 meters above ground | m/s | | WD | Wind direction m/s at 10 meters above ground | ° | | WG | Wind gusts at 10 meters above ground | m/s | | AP | Atmospheric pressure | hPa | | PREC | Precipitation (rainfall) | mm | | SDWE | Water equivalent of accumulated snow depth | kg/m2 | #### Environmental factors affect PV performance and long-term energy production: | ALB | Ground surface albedo | | | --- | --- | --- | | PWAT | Precipitable water | kg/m2 | #### Simulated PV output parameters provide energy yield estimates and performance metrics: | PVOUT specific | Specific photovoltaic power output | kWh/kWp ** | | --- | --- | --- | | PVOUT total | Total photovoltaic power output | kWh ** | | PR | Performance Ratio | | ** for sub-hourly time resolutions, the parameter is provided as power (kW or W/kWp) #### Other data | SE | Sun elevation | ° | | --- | --- | --- | | SA | Sun azimuth | ° | ## Spatial resolution Spatial resolution Data parameters 90 m x 90 m GHI, DNI, GTI, PVOUT (considering terrain shading effects) 1 km x 1 km TEMP, AP, ALB 11 km x 11 km PREC, SDWE 25 km x 25 km RH, TD, WBT, WS, WD, WG, PWAT ## Geographical and temporal coverage Solar and meteorological data is available from years as indicated on the map above. — Long-term average ground surface albedo represents a period from 2006 to 2015. Learn more about [how we validate our solar radiation models](https://solargis.com/technology/accuracy-and-validation) and our data methodology. ## Available data export formats #### Data formats Solargis JSON Solargis CSV NREL SAM weather file - only for TMY PVsyst standard format - only for TMY HelioScope weather file - only for TMY #### Other available formats KML boundaries (.kml) AutoCAD (.dxf) GeoJSON (.geojson) Collada 3D (.dae) PVsyst (.pvc) Bill of materials (.xlsx) See [sample data and reports](https://solargis.com/products/evaluate/resources) or learn about [integrating Solargis data via API](https://solargis.com/products/integration). -------------------------------------------------------------------------------- ## Solargis Prospect: Solar Site Screening & Pre-Feasibility Source: https://solargis.com/products/prospect # Solargis Prospect: Screen solar sites for pre-feasibility Compare sites worldwide with 30+ validated map layers. Calculate yield estimates remotely. ## What is Solargis Prospect? Solargis Prospect provides access to [solar, meteorological, and environmental data](https://solargis.com/technology/expertise) for sites all around the world. It helps you calculate solar yield estimates and potential gains and losses during the [pre-feasibility phase](https://solargis.com/solutions/site-selection) of your PV project. - Reliable and accurate solar data - Fast site comparisons - Comprehensive environmental overview - Solar power calculator and analytics - Collaboration and multilingual support ## [High-resolution solar resource maps and environmental data](https://solargis.com/products/prospect/features#high-resolution-solar-resource-maps) With Solargis Prospect, you don’t need to be present at the site nor rely on local third parties during the [pre-feasibility stages](https://solargis.com/solutions/site-selection). Get the real look & feel of your potential site with meteo, solar, and environmental parameters – visualized on more than 30 map layers. [More about map layers](https://solargis.com/products/prospect/features#high-resolution-solar-resource-maps) ## [Compare potential PV sites and configurations](https://solargis.com/products/prospect/features) Feel comfortable when deciding which site to [evaluate in more depth](https://solargis.com/products/evaluate). Solargis Prospect allows you to compare multiple sites and pick the one with the highest potential yield. The solution also offers fast screening of various [PV configurations](https://solargis.com/solutions/optimizing-power-plant-design) and technologies. ## [Validated long-term solar data for pre-feasibility decisions](https://solargis.com/products/prospect/data-specs) Don’t make compromises. Start as you mean to go on, from the very first decisions in the prospecting phase, using accurate and validated data that are [compatible with later project stages](https://solargis.com/products/evaluate). [Validated by independent studies](https://solargis.com/technology/accuracy-and-validation), Solargis Prospect delivers the most accurate long-term average (LTA) data on the market, with data history spanning up to 30 years - the longest in the industry. Key parameters include solar irradiation, precipitation, air temperature, wind speed, air pollution, albedo, snow, and others. [Solargis Prospect data specs](https://solargis.com/products/prospect/data-specs) ## [Download sample reports and technical documentation](https://solargis.com/products/prospect/resources) Download sample data or a report from a specific site. You can also check technical documentation or learn about the technology behind this solution. ## Related products and services ### [Solargis Evaluate](https://solargis.com/products/evaluate) - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Unmatched level of detail and accuracy - PV simulation based on ray tracing and Perez all-weather sky model ### [Solargis LTA API](https://solargis.com/products/integration/solargis-long-term-averages-api) - XML format via REST API - Automatically updated global solar database - Estimate PV sites' solar potential - Accurate and validated data compatible with later project stages - Key solar and meteorological parameters ### [Customized GIS Data](https://solargis.com/services/customized-gis-data) Explore solar, meteorological, and PV potential GIS data in your own applications. You can use solar resource, PV, climate, and other geo data for analysis and visualization in all generally available GIS software (GeoTIFF, NetCDF, and others) with raster data processing capabilities or numerical implementation. ### [Regional Solar Energy Potential Study](https://solargis.com/services/regional-solar-energy-potential-study) In the Regional Solar Energy Potential Study, we analyze not only solar resource information but also meteorological and geographic data. The analysis considers the uncertainty of resource estimates, intermittent and seasonal variability, extreme weather, and geographic limitations on the deployment of solar power plants. -------------------------------------------------------------------------------- ## Solargis Prospect features: Maps, reports & pre-feasibility Source: https://solargis.com/products/prospect/features # Solargis Prospect features and capabilities ## Automatically updated global solar database of long-term monthly values Use one reliable database for preliminary analysis of solar energy opportunities for sites all around the globe. ### Global, direct, and diffuse solar irradiation Beyond looking at annual data, understanding solar irradiation seasonality is important for choosing the right technology for your PV project. To see differences over the year, Prospect provides averaged irradiance values for each month. These values are derived from globally validated Solargis satellite models. ### 24x12 daily solar irradiation profiles With Prospect’s 24x12 daily profiles, you’ll see how solar irradiation averages change depending on the time of the day. ### Key meteo parameters Understand the expected average working conditions of the power plant to estimate related thermal, spectral, and snow losses, as well as other effects of the environment on the energy conversion. Besides solar irradiation parameters, Prospect gives you averaged hourly temperature and monthly values for wind, humidity, precipitation, snow, and others. ### Ground albedo To accurately calculate the reflected solar irradiance, you need site-specific averaged monthly albedo data. This is especially important for bifacial modules. ### Terrain data Surface slope, azimuth, and horizon data are available in Prospect as well. You want to know these values for improved incident irradiance and far shading loss calculations. We use digital surface models to help identify local features and their effect on solar energy at the pre-feasibility stage when detailed site information is not available. ### Standard CSV files for data imports Besides in-app data visualizations and summary reports, you can download solar irradiance and meteo data in CSV format. The format is especially useful if you want to import the data into other energy simulation tools. ### Automatic data updates Prospect updates and recalculates yield values automatically after each additional year of data is collected. To help you work with the most representative averages every time a new year is complete, we automatically add it to the app. You can also keep the calculations done using older data and recalculate using updated data any time you want. ## High-resolution solar resource maps Check how solar energy averages and key meteorological parameters change throughout the year in collecting locations and regions of interest. ### Solar energy geodata layers During the site prospection, you can visualize solar irradiation maps in high resolution: global irradiation, direct irradiation, diffuse to global fraction, irradiation seasonality and many more. Thanks to energy conversion algorithms, we have also created global tilted irritation map. ### Climate geodata layers As a Prospect user, you can explore the site’s climate conditions by switching between air temperature, precipitation (rainfall), snow days, wind speed, precipitable water, hail days, relative humidity data layers and many others. ### Ground albedo, terrain, and other geodata layers Access map visualizations of important local data: ground albedo, land cover, elevation and terrain slope and azimuth. Besides, other factors related to energy demand provide additional contextual information for your energy projects, e.g. population density, corrosion degradation rate, UVA, UVB, lowest and highest expected operating temperature and many more data layers. To help with local explorations, maps including topographic names and satellite views are also included. ### Distance and surface calculator The Prospect’s map interface allows for measuring the distance between sites and calculating areas by simply drawing them on the map without needing any additional GIS tool. ### Interactive markers By pinning your project on the Prospect’s map, you will have quick access to all project details and the option to share the location with other team members. ## User-friendly and reliable system configurator Run quick simulations for your chosen PV technology and let other project team members easily replicate and check expected energy yield values. ### Ground-mounted PV systems Run simulations for ground-mounted PV systems for any azimuth and tilt. — Prospect’s simulation engine can also simulate energy yield for the most common tracking systems. ### Roof-mounted PV systems Prospect can simulate PV systems taking into account specific characteristics of rooftop PV installed on flat or inclined roofs (or even vertically oriented systems). ### Floating PV systems Calculate the expected yield for floating PV systems thanks to our specific simulation model. ### Preset configurations Using the most common settings users can save time and generate results faster. Besides, this feature makes it simple to retrieve results even for those without an engineering background. ### Copy configuration from other projects Users can predefine their preferred PV system with some specific details so that they save time and foster the consistency of solar projects across their portfolio. ### Project settings export Prospect facilitates exporting any project configuration in CSV format. This is useful for using the same settings in a Solargis API call at a later stage. It is also a simple way to share your project configuration with other team members and stakeholders. ## Pre-feasibility reports in just a few clicks Run quick reports and comparisons of long-term expected values for your future PV project. Prospect allows you to keep track of all your solar energy opportunities and share them with the project stakeholders. ### System overview Prospect integrates all the insights to help you better analyze project opportunities. This includes expected energy yield during the PV plant’s lifetime together with an overview of solar irradiation and climatic conditions for the specific project location. ### Expected energy yield Prospect calculates the expected energy using the Solargis simulation engine. As a result, you will get tables and charts visualizing the expected energy generation and performance ratios for each year of the PV plant’s lifetime. ### 24x12 energy generation profiles With 24x12 daily profiles provided in Prospect, you will know how energy averages change depending on the time of the day. This is especially useful for projects where meeting certain demand requirements is important. ### Site’s environmental conditions Prospect provides an overview of site-specific environmental conditions in a comprehensive summary of long-term averages of irradiance and other key parameters. This allows for further calculation of related thermal losses (temperature, wind), spectral losses (humidity, precipitable water), soiling losses (precipitation rates), and snow losses (snow days). For the estimation of reflected irradiation from the ground surface, ground albedo values are also included. ### Far shading Terrain data and sun path diagrams will help you understand how the sun’s position and surrounding mountains impact the energy received by the system throughout the year. ### Energy losses Check energy losses across the whole energy conversion process. For the calculation, the software considers all steps on the energy conversion chain: far shading, soiling, snow, angular reflectivity, spectral loss, module’s PV conversion, inter-row shading, module’s power tolerance, inverter efficiency, and electrical losses related to mismatch, cabling, and other losses on the AC side of the system. ### Multi-language reports Generate, download, and share reports in PDF and XLSX formats. You can choose your preferred unit configuration and language (more than 15 are available) and keep track of all the generated reports for each of your solar energy projects. ### Change of units Solargis Prospect allows users to work on their preferred geographical and physical units e.g. solar radiation, temperature, latitude and longitude, and terrain. For numbers shown in the finance section, users can also choose from the most popular currency units. ## Pre-feasibility finance calculator Have a first approximation of your PV project’s return on investment. Besides expected energy values, Solargis Prospect has built-in features to calculate other financial indicators. ### Cash-flow analysis Prospect calculates the expected project revenue for the entire power plant’s lifetime. ### CAPEX and OPEX inputs Prospect’s finance calculator allows inserting main PV system costs for both installation and regular operations. ### Loan interest and taxes Besides your inputs on the price of electricity, Prospect takes into account also tax rates and debt ratio when calculating project finances. ## Project comparison and management Compare your projects and have them always organized. Solargis Prospect offers a useful set of features that will help you save time when prospecting for new opportunities. ### Comparison feature During the prospection phase, it’s natural that you want to compare several project options. Prospect’s comparison feature instantly shows yield and climate conditions for multiple projects at the same time, without the need to extract or import any data. ### Project organizer Prospect not only helps save time on data collection and energy calculation tasks. It also helps keep all user’s projects organized with additional features for naming, adding labels, and archiving projects. ### Project transfer To facilitate working across team members, users can change the ownership of the project to other users that are included in the same company subscription. -------------------------------------------------------------------------------- ## Solargis Prospect data specs: Solar, meteo & environmental Source: https://solargis.com/products/prospect/data-specs # Solargis Prospect - Parameters and data specification ## Map layers and data included #### Annual average, monthly averages, 12 x 24 profiles | GHI | Global horizontal irradiation | kWh/m2 | Average annual, monthly or daily sum of global horizontal irradiation. | | --- | --- | --- | --- | | DIF | Diffuse horizontal irradiation | kWh/m2 | Average yearly, monthly or daily sum of diffuse horizontal irradiation. | | DNI | Direct normal irradiation | kWh/m2 | Average yearly, monthly or daily sum of direct normal irradiation. | | GTI | Global tilted irradiation | kWh/m2 | Average annual, monthly or daily sum of global horizontal irradiation. | | TEMP | Air temperature at 2 meters | °C / °F | Average yearly, monthly and daily air temperature at 2 m above ground. | | PVOUT | PV cSi yield | kWh | Yearly and monthly average values of photovoltaic electricity (AC) delivered by the total installed capacity of a PV system. | #### Annual average, monthly averages | WS | Wind speed m/s at 10 m above ground | m/s | Average yearly, monthly and daily wind speed at 10 m above ground. | | --- | --- | --- | --- | | RH | Relative humidity at 2m above ground | % | Average yearly or monthly relative humidity at 2 m above ground. | | PWAT | Precipitable water | kg/m2 | Precipitable water is the depth of water vapour in a column of the atmosphere, if all the water in that column were precipitated as rain. It indicates the amount of moisture above ground. | | PREC | Precipitation (rainfall) | mm | Average yearly and monthly sums of precipitation. | | SNOWD | Snow days | days | Snow days are calculated as days with snow water depth equivalent to or higher than 5 mm. | | ALB | Surface albedo | | Fraction of solar irradiance reflected by surface. Ratio of upwelling to downwelling (GHI) radiative fluxes at the surface. | | CDD | Cooling degree days | degree days | Quantifies energy demand needed to cool a building. "Cooling degree days" are a measure of how much (in degrees), and for how long (in days), outside air temperature was higher than a specific base daily average temperature (18°C). Yearly and monthly values are aggregated from daily values. | | HDD | Heating degree days | degree days | Quantifies energy demand needed to heat a building. "Heating degree days" are a measure of how much (in degrees), and for how long (in days), outside air temperature was lower than a specific base daily average temperature (18°C). Yearly and monthly values are aggregated from daily values. | #### Additional map layers | GHI_VAR_LONG | Long-term variability of Global horizontal irradiation | % | The standard deviation of the yearly time series of GHI. | | --- | --- | --- | --- | | GHI_VAR_SHORT | Short-term variability of Global horizontal irradiation | | The yearly average count of GHI ramps exceeding the 400 W/m2 threshold, analyzed from Solargis GHI Time Series. | | CORR | Corrosion degradation rate | % | Degradation effect in PV modules, primarily driven by temperature and humidity. | | TMOD_AMP50 | Daily module temperature amplitude higher than 50 °C | days | The average number of days in a year with daily PV module temperature amplitude higher than 50 °C. | | WG p99 | Wind gust p99 | m/s | The 99th percentile of wind gusts at 10 m height above ground calculated from the ERA5 hourly dataset from 2001 to 2020, averaged to provide a yearly number. | | UVA | Ultraviolet A radiation | kWh/m2 | Average annual UVA radiation. | | UVB | Ultraviolet B radiation | kWh/m2 | Average annual UVB radiation. | | LOSS SOIL | Soiling losses for GTI Opta systems | % | Projected long-term GTI losses from soiling for optimally inclined PV modules. Natural cleaning from rain events is considered. | | HAILD | Hail days | days | Yearly average of number of days with a potential for severe hail event (diameter ~1.5 inch = ~3.8 cm) in the area of unit pixel size (~28x28 km), calculated by Solargis hail model | | ELE | Elevation | | | | SLO | Slope | | | | AZIM | Azimuth | | | | POPUL | Population density | | | | LANDC | Land cover | | | | D2G | Ratio of diffuse to global irradiation | | Ratio of diffuse horizontal irradiation and global horizontal irradiation (DIF/GHI). | | OPTA | Optimum tilt of PV modules | | | | TLEO | Lowest expected operating temperature | °C / °F | Mean of annual extreme low temperatures according to IEC 62738. | | THEO | Highest expected operating temperature | °C / °F | Average of the highest recorded air temperatures over 20 years at the site according to IEC 62738. | #### Other data Horizon, Sun path, Day length, Solar zenith angle ## Spatial resolution #### Map layers Spatial resolution Data parameters 3 arc-seconds, approx. 90m x 90m 9 arc-seconds, approx. 250m x 250m 10 arc-seconds, approx. 300m x 300m 30 arc-seconds, approx. 1 km x 1 km 2 arc-minutes, approx. 4 km x 4 km 0.1 degree, approx. 11 km x 11 km 0.25 degree, approx. 28 km x 28 km ELE, SLO, AZIM GHI, DIF, DNI, GTI, D2G, DNI_SEASON, GHI_SEASON LANDC PVOUT, TEMP, TLEO, THEO, ALBEDO, HDD, CDD, POPUL OPTA, GHI_VAR_LONG, GHI_VAR_SHORT, CORR, TMOD_AMP50 PREC, SNOWD WS, WG, RH, PWAT, UVA, UVB #### PV Calculation Spatial resolution Data parameters 3 arc-seconds, approx. 90m x 90m 30 arc-seconds, approx. 1 km x 1 km 2 arc-minutes, approx. 4 km x 4km 0.1 degree, approx. 11 km x 11 km 0.25 degree, approx. 28 km x 28 km PVOUT, Horizon TEMP GHI, DNI SNOWD PWAT ## Geographical and temporal coverage Time representation of various parameters depends on the input sources and models behind them. With few exceptions, it spans from the 1990s/2000s to the last year. Depending on the region, the long-term averages are calculated for a period from 1994/1999/2005/2007/2018 to 2025. Please, refer to the map above for the specific start year. The long-term averages of the other meteorological parameters are calculated for a period 1994-2025 globally. Ground albedo data represents the long-term average for a period 2006-2015. -------------------------------------------------------------------------------- ## Solargis Monitor: PV Performance Monitoring Software Source: https://solargis.com/products/monitor # Solargis Monitor: Satellite-based PV monitoring Independent validation of plant performance in near real-time. No sensors, no gaps, no errors. ## What is Solargis Monitor? Ground-based meteo stations are expensive, prone to errors, and often miss critical gaps in measurement. Solargis Monitor gives you reliable satellite-derived solar data for any PV site - in near real-time, gap-free, and [independently validated](https://solargis.com/technology/accuracy-and-validation). Calculate performance ratios with confidence, [benchmark real output against expected yield](https://solargis.com/solutions/real-power-plant-performance), and eliminate uncertainty from your reporting. - One solar data source for all sites - Independent validation of reported PV performance - Gap- and error-free solar radiation data - Minimize uncertainty in meteo station inputs - Benchmark planned performance against reality ## [Minimize errors from meteo station data](https://solargis.com/products/monitor/data-specs) Ground measurements tend to be costly and often come with gaps and errors, lacking some of the most necessary parameters that affect [plant performance](https://solargis.com/services/pv-performance-assessment). Solargis Monitor gives you reliable satellite-derived data that is easy to access, accurate, and continuous. Operate with 20+ solar, meteo, and environmental parameters in near real-time and for any site around the globe. ## [Benchmark expectations against reality](https://solargis.com/products/monitor/resources) Reliably calculate the site’s performance ratio using [site-specific solar radiation data](https://solargis.com/technology/expertise). Benchmark the real PV output against expected energy performance to assess the project’s actual operational efficiency. In doing so, you can be confident that your [performance reporting to key stakeholders](https://solargis.com/solutions/real-power-plant-performance) reflects the true environmental conditions and is both transparent and bulletproof. ## [Avoid the pain of comparing data from multiple sources](https://solargis.com/products/monitor/features) Solargis Monitor provides consistent and accurate performance data for any location and type of project. By using unified inputs across the [whole PV portfolio](https://solargis.com/solutions/real-power-plant-performance), you can compare projects efficiently and accurately, based on standardized metrics. Several [independent comparisons](https://solargis.com/technology/accuracy-and-validation) of solar radiation databases have named Solargis as the best-performing solution, making it the most suitable satellite-derived solar data source for PV performance monitoring. ## [Sample data](https://solargis.com/products/monitor/resources) Download sample data or a report from a specific site. You can also check technical documentation or learn about the technology behind this solution. ## Related products and services ### [Solargis Evaluate](https://solargis.com/products/evaluate) - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Unmatched level of detail and accuracy - PV simulation based on ray tracing and Perez all-weather sky model ### [Solargis Monitor API](https://solargis.com/products/integration/solargis-monitor-api) - XML format via REST API - Independent validation of reported PV performance - Gap- and error-free solar radiation data - Minimize uncertainty in meteo station inputs - Benchmark planned performance against reality ### [PV Performance Assessment](https://solargis.com/services/pv-performance-assessment) Data-driven insights from our PV Performance Assessment report conducted after months or years of the plant’s operation will help you optimize its performance. The report also provides a revised and more accurate long-term energy yield estimate for refinancing or new asset acquisition purposes. ### [PV Variability & Storage Optimization Study](https://solargis.com/services/pv-variability-and-grid-integration-study) The PV Variability & Storage Optimization Study delivers statistical data and insights to project developers needed for designing and managing PV-plus-storage systems. The study provides the most realistic data on PV power generation for grid integration analysis. -------------------------------------------------------------------------------- ## Solargis Monitor features: Satellite data, API & reporting Source: https://solargis.com/products/monitor/features # Solargis Monitor - PV monitoring features ## Always available solar irradiance reference Have a reliable reference for your performance calculations without installing another ground sensor. Solargis Monitor data service provides a satellite-based solution to cover the most recent periods in almost real-time. ### Works everywhere Solargis Monitor works for any site covered by Solargis irradiance model. This means that you can work with the same source of reference data, no matter where the solar power assets were installed or when. The current irradiance values are available right after a few minutes, i.e. the time the system takes to retrieve and process the satellite images. ### Fast set up The service is ready to go just after choosing the system’s most important parameters. Besides the power plant coordinates, other PV system settings can be provided for the calculation of expected energy yield. ### No gaps Since the service works with input data provided by geostationary satellites, the delivered outputs are given with no gaps. This is essential to assess the whole period when PV plants are producing energy. ### No maintenance needed Since Monitor relies on satellite remote sensing technology, for having the service up and running there is no need for cleaning, calibration, or maintenance of any ground-installed device on the power plant’s side. ### Higher accuracy with reanalyzed data At the end of the month we recalculate the operational/daily data to increase the accuracy of the values provided. ## Portfolio performance reporting Run a consistent reporting activity for your whole portfolio of PV assets. Thanks to the continuous stream of data provided for all your locations, Solargis Monitor makes the whole process more efficient. ### Independent assessment Since the satellite-based reference data used in Monitor is not directly measured by the PV asset managers, the service provides third-party validation of the reported performance values when presenting them to stakeholders. Similarly, PV production values will be independently validated to support the consistency of the statistics. ### Multi-site check No matter whether you have one or one thousand sites to be monitored, Solargis Monitor is ready to deliver all necessary data to meet your reporting requirements. ### Expected yield and other performance indicators The service not only provides irradiance and meteo parameters like temperature, wind, etc. but it also calculates the Global Tilted Irradiance (GTI, also known as plane-of-array), and the expected PV yield based on current site conditions. This allows the calculation of theoretical performance ratio (PR), and other indicators like the energy performance index. ### Comparison with past reference periods By combining the most recent information with other reference periods, Solargis Monitor allows studying the evolution of the site conditions along with the related performance results. ### Regional monthly reports To fully understand any regional weather patterns and have a better geographical context, data streams are delivered as an add-on with [Monthly reports](https://solargis.com/products/monitor/monthly-reports). The reports show solar radiation received during the latest period. Besides, the difference in the average values expected in the long term is also represented on the maps, which is useful for identifying the most recent climate anomalies related to solar irradiance and other meteo parameters like precipitation and temperature. ### Quality control of measurements add-on Monitor service is optionally offered together with regular quality assessments done by our team of experts. This is key to identifying non-valid measured values before calculating any further performance metrics. Alternatively, access to Analyst software is also possible, allowing your team to perform manual flagging and automatic checks on your measured data, among other features. ## API access for monitoring platforms Connect any monitoring platform you are already using with reference data streams provided by Solargis Monitor. Automating the ingestion of key data inputs is possible with the [Solargis Monitor API](https://solargis.com/products/integration/solargis-monitor-api) service. ### Ready-to-use integrations with the most popular platforms Our team has an open collaboration channel with the most popular software platforms available in the market so the integration of Solargis data is faster and easier. ### SFTP/FTP access Together with REST API, we also offer the possibility to receive data via SFTP/FTP for those users who work under this data transfer protocol. ### Advanced PV configuration variables A full set of PV system settings for calculating the expected PV yield is available within the API. This way more realistic results can be obtained when comparing expected vs real power production. -------------------------------------------------------------------------------- ## Solargis Monitor Data Specs: Solar & Meteo Parameters Source: https://solargis.com/products/monitor/data-specs # Solargis Monitor data specifications ## Data accessibility XML format via REST API: [Solargis Monitor API](https://solargis.com/products/integration/solargis-monitor-api) service CSV format via SFTP or email: [Solargis data via SFTP](https://solargis.com/products/integration/solargis-sftp-api) service ## Data parameters ### Solar parameters | GHI | Global horizontal irradiation | kWh/m2 | | | --- | --- | --- | --- | | DIF | Diffuse horizontal irradiation | kWh/m2 | | | DNI | Direct normal irradiation | kWh/m2 | | | GTI | Global tilted irradiation | kWh/m2 | | ### Meteorological parameters | TEMP | Air temperature at 2 meters | °C | | --- | --- | --- | | TD | Dew point temperature | °C | | WBT | Wet bulb temperature | °C | | TMOD | Module temperature | °C | | WS | Wind speed m/s at 10 m above ground | m/s | | WS100 | Wind speed at 100 m | m/s | | WD | Wind direction at 10 m above ground | ° | | WD100 | Wind direction at 100 m | ° | | WG | Wind gusts at 10 m above ground | m/s | | RH | Relative humidity at 2m above ground | % | | AP | Atmospheric pressure | hPa | | PWAT | Precipitable water | kg/m2 | | PREC | Precipitation (rainfall) | mm | | SDWE | Snow depth water equivalent | mm | | SFWE | Water equivalent of fresh snowfall rate | kg/m2/hour | ### PV parameters | PVOUT | Photovoltaic output | kWh | | --- | --- | --- | ### Additional parameters | ELE | Elevation | m | | --- | --- | --- | | SE | Solar elevation | ° | | SA | Solar azimuth | ° | | INC | Incidence angle of direct irradiance | ° | | TILT | Tilt of inclined surface | ° | | ASPECT | Aspect of inclined surface | ° | | KC | Clear-sky index | | | KT | Clearness index | | The full list of parameters can be found here. ### Temporal resolution | Primary data | 5/10/15-minute step (depending on satellite mission and region) | | --- | --- | | Aggregated data | hourly, daily, monthly, yearly | ### Spatial resolution 90 m x 90 m Solar radiation parameters 1 km x 1 km TEMP 11 x 11 km PREC, SDWE approx. 25 to 35 km Other meteorological data ## Geographical and temporal coverage -------------------------------------------------------------------------------- ## Solargis Forecast - Solar power forecasting for PV plants Source: https://solargis.com/products/forecast # Solargis Forecast: Solar power forecasting for PV plants Predict energy production up to 14 days ahead with 5–15 minute nowcasting updates ## What is Solargis Forecast? With Solargis Forecast you can get a [reliable prediction](https://solargis.com/solutions/power-output-forecast) of how much solar power your PV plant will generate in the coming minutes, hours, and days, for a period of up to two weeks. Every 5, 10, or 15 minutes, Solargis Forecast provides short-term forecast data – or [nowcasting](https://solargis.com/technology/expertise)– for up to 3 hours ahead. - Solar power output forecasts for up to 14 days - Nowcasting updates up to every 5 minutes - Minimize penalties from grid operators - Schedule maintenance based on the forecasts - Manage the variability of your PV plant portfolio ## [Minimize grid penalties with solar forecasting](https://solargis.com/products/forecast/data-specs) Due to the [high variability of renewable power output](https://solargis.com/solutions/real-power-plant-performance), accurate forecasting has become critical for maintaining [grid stability](https://solargis.com/solutions/power-output-forecast). With Solargis Forecast, you can report with much higher confidence how much power your solar power plant will provide to the grid. This helps you minimize potential under- and over-delivery penalties from grid operators. ## [Optimize energy trading bids](https://solargis.com/products/forecast/resources) Maximize earnings in markets with fluctuating energy prices. Accurate forecasts of solar energy production help you optimize the performance of hybrid PV-plus-battery systems, thanks to better management of [battery charging and energy dispatch](https://solargis.com/solutions/power-output-forecast). ## [Get accurate forecasts with multiple models](https://solargis.com/technology/accuracy-and-validation-forecast) Solargis Forecast combines multiple forecasting techniques to deliver [accurate predictions](https://solargis.com/solutions/power-output-forecast) across all time horizons. **Nowcasting (next 3 hours)** — Cloud Motion Vector models analyze real-time satellite imagery, updating every 5-15 minutes for immediate grid balancing and intra-day trading. **Day-ahead forecasts (up to 14 days)** — Multiple [Numerical Weather Prediction](https://solargis.com/technology/accuracy-and-validation) models are blended using a consensus approach that optimally weights each model. [Read the validation study](https://solargis.com/technology/accuracy-and-validation-forecast) ## [Schedule maintenance with forecast data](https://solargis.com/products/forecast/features) Solargis Forecast is based on global Numerical Weather Prediction (NWP) and satellite-to-irradiance models, making it one of the most precise forecast services available on the market. Precise forecasting allows you to plan site maintenance to minimize potential losses – for example during a period of cloudy weather when expected PV performance is reduced. ## [Hail forecasting for PV plants](https://solargis.com/resources/blog/solargis-news/hail-forecasting-for-the-us) Hail is one of the [most destructive weather threats](https://solargis.com/solutions/optimizing-power-plant-design) to solar power plants across the United States. Large hailstones can shatter PV modules, leading to costly repairs and downtime. Advanced hail forecasting with Solargis Forecast provides early warnings up to 48 hours in advance, enabling operators to take preventive actions and protect their investments. [Read more here](https://solargis.com/resources/blog/solargis-news/hail-forecasting-for-the-us) ## [Technical specifications](https://solargis.com/products/forecast/data-specs) **20+ forecast parameters:** GHI, DNI, DIF, GTI, PV power output, temperature, wind speed, humidity, hail risk **5/10/15-minute resolution:** Nowcasting updates every 5-15 minutes; day-ahead forecasts updated 2-4 times daily **Flexible delivery:** REST API, SFTP/FTP, or CSV export ## [Sample data](https://solargis.com/products/forecast/resources) Download sample data or a report from a specific site. You can also check technical documentation or learn about the technology behind this solution. ## Related products and services ### [Solargis Monitor](https://solargis.com/products/monitor) - One solar data source for all sites - Gap- and error-free solar radiation data - Minimize meteo station inputs uncertainty - Benchmark planned performance with reality - Near real-time PV output assessment ### [Solargis Forecast API](https://solargis.com/products/integration/solargis-forecast-api) - XML format via REST API - Solar power output forecasts for up to 14 days - Nowcasting updates up to every 5 minutes - Minimize penalties from grid operators - Schedule maintenance based on the forecasts -------------------------------------------------------------------------------- ## Solargis Forecast features — Nowcasting, NWP models & API Source: https://solargis.com/products/forecast/features # Solargis Forecast features ## Power output + solar irradiance + meteorological predictions Receive forecasts tailored to the specific needs of solar power plants. Solargis Forecast service includes [all relevant parameters](https://solargis.com/products/forecast/data-specs) to cover all the use cases related to solar (and solar + wind) power assets. ### From the next hour to day 14 To deal with both hours-ahead and days-ahead operations, the service offers time series datasets of expected energy, covering a period starting from the next hour up to 14 days ahead. ### Subhourly updates Since forecasts are continuously changing as new inputs are being received, the service is continuously generating updated data streams. Updates every 10/15 minutes are delivered for forecasts covering the next hours and 4 times a day for forecasts covering the next days. ### Works for fixed and tracking PV systems The use of advanced simulation models makes it possible to predict the energy power output expected for any type of PV configuration. This applies to systems with trackers, whose tracking algorithm is also modeled in the simulations. ### Solar + wind power forecasts For wind power assets, or projects combining both solar and wind generation capacity, Solargis Forecast service provides a complete solution including predictions of expected wind power output. ## Continuous data streams based on smart selection of models Get outputs from the best-performing forecasts for each time horizon. Solargis Forecast combines several models and forecasting techniques to deliver gap-less data streams with the lowest expected deviation. ### Cloud motion detection In addition to NWP models, we make use of real-time satellite data to calculate expected cloud cover in the next hours. The so-called Cloud Motion Vector (CMV) model is suitable up to 3-4 hours ahead and it is incorporated into the forecasted Time Series delivered. ### Numerical weather predictions blending Numerical weather prediction (NWP) models have different capabilities depending on the weather conditions. For this reason, Solargis Forecast service uses a consensus forecast that optimally integrates forecasts from various NWP models. ### Runs also without user data ingestion The service does not rely on the availability of real energy production data to run and calibrate the models. We can do this thanks to the implementation of advanced yield simulation models able to reproduce PV and wind plants’ behavior for particular irradiance and meteorological conditions forecast at each instant of time. ### Forecasts for PV aggregated capacity Solargis Forecast service can generate and aggregate forecasts for two or more, even thousands of energy generation assets. This is especially useful when it is required to predict the aggregated solar (or solar + wind) energy production for a certain country or region, no matter the size of the assets (utility-scale, commercial, rooftop, etc). ## Accuracy reporting Know the expected accuracy for particular locations in advance. This information is key to planning necessary actions on your asset operations, both on the business and technical side of things. ### Historical accuracy statistics We evaluate the performance of forecasts by comparing historical forecasts with solar radiation modeled based on satellite observations. This is key to understanding the expected range of deviations and in the case of hybrid systems, it helps determine the storage optimum capacity. ### Expert accuracy enhancement By knowing a long enough period of real production data, our team of experts can adjust the inputs influencing forecast accuracy to decrease the systematic deviations to the minimum possible. ## API service Connect any platform with data streams provided by [Solargis Forecast API](https://solargis.com/products/integration/solargis-forecast-api). This way you can incorporate forecasts into your daily O&M and trading activities more efficiently. ### REST API Request and exchange data in a standardized way using HTTP protocols. This can add a lot of flexibility to your forecast reporting needs. ### SFTP/FTP access Together with the REST API, we also offer the possibility to receive data via SFTP/FTP for those users who work under this data transfer protocol. ### Advanced PV configuration variables A full set of system settings for calculating the expected power output is available within the API. This can be very helpful for achieving more realistic power production values from the forecast service. -------------------------------------------------------------------------------- ## Solargis Forecast data specs: Parameters & API delivery Source: https://solargis.com/products/forecast/data-specs # Solargis Forecast parameters and data specification ## Data accessibility XML format via REST API: [Solargis Forecast API](https://solargis.com/products/integration/solargis-forecast-api) service CSV format via SFTP or email: [Solargis data via SFTP](https://solargis.com/products/integration/solargis-sftp-api) service ## Data parameters ### Solar parameters | GHI | Global horizontal irradiation | kWh/m2 | | | --- | --- | --- | --- | | DIF | Diffuse horizontal irradiation | kWh/m2 | | | DNI | Direct normal irradiation | kWh/m2 | | | GTI | Global tilted irradiation | kWh/m2 | | ### Meteorological parameters | TEMP | Air temperature at 2 meters | °C | | --- | --- | --- | | TD | Dew point temperature | °C | | WBT | Wet bulb temperature | °C | | TMOD | Module temperature | °C | | WS | Wind speed m/s at 10 m above ground | m/s | | WS100 | Wind speed at 100 m | m/s | | WD | Wind direction at 10 m above ground | ° | | WD100 | Wind direction at 100 m | ° | | WG | Wind gusts at 10 m above ground | m/s | | RH | Relative humidity at 2m above ground | % | | AP | Atmospheric pressure | hPa | | PWAT | Precipitable water | kg/m2 | | PREC | Precipitation (rainfall) | mm | | SDWE | Snow depth water equivalent | mm | | SFWE | Water equivalent of fresh snowfall rate | kg/m2/hour | | HAILRISK25MM | Potential hail risk with hail size ≥ 1 inch (~2.5 cm) within 50 km radius | % | | HAILRISK38MM | Potential hail risk with hail size ≥ 1.5 inch (~3.8 cm) within 50 km radius | % | | HAILRISK50MM | Potential hail risk with hail size ≥ 2 inch (~5 cm) within 50 km radius | % | ### PV parameters | PVOUT | Photovoltaic output | kWh | | --- | --- | --- | ### Additional parameters | ELE | Elevation | m | | --- | --- | --- | | SE | Solar elevation | ° | | SA | Solar azimuth | ° | | INC | Incidence angle of direct irradiance | ° | | TILT | Tilt of inclined surface | ° | | ASPECT | Aspect of inclined surface | ° | | KC | Clear-sky index | | | KT | Clearness index | | The full list of parameters can be found here. ### Temporal resolution | Primary data | 5/10/15-minute step (depending on satellite mission and region) | | --- | --- | | Aggregated data | hourly, daily, monthly, yearly | ### Frequency of updates Numerical Weather Prediction models 2 to 4 times per day Cloud Motion Vector model every 5, 10, or 15 minutes In other words, a revised forecast is available every 5/10/15 minutes for next hours, and a revised forecast for next day is typically available every 6-12 hours. ## Geographical and temporal coverage -------------------------------------------------------------------------------- ## Solar data analysis software - Overview | Solargis Source: https://solargis.com/products/analyst # Solargis Analyst Simplify & unify your solar data analysis ## What is Solargis Analyst? Solargis Analyst is a software for visualization, comparison, error detection, and analysis of solar and meteo data. We designed it to empower solar analysts to work with solar data more efficiently. - Visualize complex and large solar datasets - Compare measured data to model outputs - Identify and clean errors from measurements - Harmonize multi-source input streams - Streamline solar data management ## [Say goodbye to inconsistent solar measurements](https://solargis.com/products/analyst/features) Errors in solar measurements have a knock-on impact on the reliability of solar performance assessments. Solargis Analyst comes with automatic error detection tools and manual flagging options to highlight potential issues. It enables you to create customized calculations, aggregations, and comparisons, all from one user-friendly platform. ## [Visualize, compare, and analyze data faster](https://solargis.com/products/analyst/features) Solargis Analyst is designed to speed up your analysis processes. Load and compare various datasets to identify differences and investigate potential issues. Run efficient analysis of solar data without having to write a single line of code. Use pre-designed plots and visualizations to compare and zoom in or out on your various graphical data representations. ## [Improved transparency in regular financial reporting](https://solargis.com/products/analyst/resources) Use Solargis Analyst to create transparency with investors and lenders by showcasing consistent and reliable performance figures. Establish standardized and repeatable performance reporting to improve the project’s return on investment. Understand the health of your PV power plant portfolio throughout its full lifetime. [See Solargis Analyst quality report](https://solargis.com/products/analyst/resources) ## [Sample reports](https://solargis.com/products/analyst/resources) Download sample data or a report from a specific site. You can also check technical documentation or learn about the technology behind this solution. ## Related products and services ### [Quality Control of Solar & Meteo Measurements](https://solargis.com/services/quality-control-of-solar-radiation-meteo-measurements) One of the key challenges of measured solar irradiance data is the high occurrence of anomalous values. The Quality Control of Solar & Meteo Measurements service, based on our experience with measurements from hundreds of locations globally, helps you identify errors and prepare the datasets for the next steps of your project. ### [Solargis Evaluate](https://solargis.com/products/evaluate) - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Unmatched level of detail and accuracy - PV simulation based on ray tracing and Perez all-weather sky model -------------------------------------------------------------------------------- ## Effective solar resource analysis software | Solargis Source: https://solargis.com/products/analyst/features # Features ## Solar resource analysis Generate plots, calculate key indicators, and make comparisons on your datasets. Software modules inside Solargis Analyst are specifically designed to analyze solar resource parameters and datasets. ### Visualization of time series in multiple ways Solargis Analyst offers data visualizations for multiple datasets/columns with various display options. These visualizations include time series representations of multiple parameters showing timeline graphs, heatmaps, histograms, and cumulative distribution function plots. ### Solar resource analysis tools Analyst allows the analysis of specific aspects of solar resource data. Under this group of visualizations, users can generate Kt Graphs, yearly/monthly diurnal profiles, multiyear analysis plots, and trend graphs with linear, polynomial, or moving average trend lines. ### Data comparisons Tools for visualizing and statistics of two datasets over concurrent periods are also provided within Analyst. This allows the representation of the differences found across datasets with a set of graphs like scatterplots, histograms, and cumulative distribution plots. ### Statistics calculation In Analyst, visualizations are accompanied by key numbers characterizing the results. At the level of single datasets, users can easily obtain min, max, mean values, percentiles, etc. For comparisons between datasets, Analyst calculates bias, RMSD, MAD, and correlation coefficients, among other statistics. ## Ground measurements quality assessment Run automatic error detection and manual flagging options to highlight potential issues in solar irradiance and meteorological data. By following the same quality assessment procedures, results can be easily shared and replicated by other team members and project partners. ### Time reference check A time reference check tool is included in Analyst for the identification and correction of time shifts, time drifts, and other time-related issues based on testing diurnal symmetry, critical for all subsequent quality tests. The user can manually identify the shifts and also run the auto-detection mode. ### Automatic tests of irradiance data The module for irradiance quality check brings a set of tests commonly used by solar data experts. These include the detection of issues and flagging invalid values related to nighttime/daytime, artificial static values, breaking physical limits, and consistency of irradiance components. This module allows test groups of single and multi-component tests. ### Automatic tests for meteo data Automatic quality control of meteorological parameters allows the user to run the automatic tests applied to meteorological parameters as well. This includes the detection of invalid values, consecutive static values, and data below/above the physical minimum/maximum. ### Interactive flagging This tool enables to perform a manual quality assessment for the selected dataset by manually selecting data or by entering an expression for flags. Flagged data values can be explored in a plot, selected, and assigned to a new flag value. ### Shading detection In addition to quality control checks, Analyst provides a specific set of visualizations for the detection of other specific data problems, such as instrument shading. ### Tracker malfunction detection Solargis Analyst can identify situations when sun-tracking instruments are not working properly. After the detection, the user can visualize and highlight periods affected by this issue. ### Post-filtering After the data quality check, it is usual to have some valid flags left in the dataset. These individual flags often don’t provide much value and they can be easily removed with the automatic post-filtering feature included in Analyst. ### Quality assessment reports After a quality assessment is performed, the users of Solargis Analyst can generate a PDF report and XLSX tables with the results of the quality assessment. The report provides a summary of quality assessment results, and comparison statistics including the most representative graphs and tables, together with information about the measuring station, installed instruments, and measured parameters. ## Data handling tools Streamline data processing tasks using the data management toolbox. This set of features will help data teams ease time-consuming tasks and avoid common issues related to data import and export processes. ### Import templates In Analyst, you can import datasets from delimited text files (e.g. CSV). Import instructions can be saved as a template for later use on similar files. To enrich information about measured data, logs about the maintenance of sensors can be imported into Analyst too. ### Management of timestamps Analyst provides a specific feature to make sure that time stamps will be correctly imported. In addition, shifting data capabilities are available to have datasets under control when working with several time zones. ### Column calculator Solargis Analyst has specific features for data editing and calculation. The dataset calculator includes tools for column creation, column deletion, and application of common functions to the data. It also includes a unit conversion feature, which can convert between different data parameter units. ### Data aggregations Users of Analyst can aggregate multiple data records into one single record. Typical use cases of this feature can be the summarization of sub-hourly datasets into hourly, or the harmonization time steps across different datasets before comparing them. Customizable aggregation rules and functions can be applied to each data parameter. These rules include basic summarization functions and more specific methods like weighted mean or angular aggregation. ### Datasets joining and combining Joining is used to add columns of values from one dataset to another dataset. It does the job for all matching records based on timestamps. On the other hand, combining is used for adding new records to an existing dataset. This for instance useful when new sensor readings are available and need to be added to the dataset. Overlapping records can be managed from the Analyst user interface. ### Multiple data export options Analyst offers the possibility to export the full set of datasets under a working project or a selection of parameters. The user can choose between several exporting formats. Besides the original Analyst Exchange File format, which provides the fastest way to transfer a single dataset between different Analyst users, other commonly used formats like CSV or PVSYST compatible files are available among the exporting options. ### Data manager Under this group of features, users can find additional tools for linking, creating, deleting databases, physical removal of datasets from databases, cloning datasets, and adding/removing datasets to a project. -------------------------------------------------------------------------------- ## Solargis Solarmaps Source: https://solargis.com/products/solarmaps # Solargis Solarmaps Interactive maps for assessing weather impact on PV performance ## What are Solargis Solarmaps? Solargis Solarmaps offer PV managers interactive maps for assessing last month’s weather impact on PV performance, enhancing accountability, and visual reporting. Look into last month’s Global horizontal irradiation, air temperature, precipitation and wind speed, the key external factors affecting PV performance. - Monthly weather variation compared to LTAs - Harmonized last month’s summaries of key PV performance indicators - Weather variability in geographical context - Effective communication with stakeholders - Independent 3rd party assessment of PV performance ## Uncover the last month’s data for your selected sites Assess harmonized monthly summaries and compare actual values with your targets or on-site measurements. To unlock precise site-specific last month’s data, simply click on a map and locate your projects. Values for the previous month will enable you to attribute unexpected performance to environmental factors and support the assessment of financial impacts. *Example: Monthly average temperature (°C) for the selected sites in January 2025* ## Side-by-side analysis The split view feature allows you to combine multiple parameters and aggregations seamlessly, enabling a more in-depth analysis of the correlation between your selected variables. By viewing these insights side by side, you can assess their impact on monthly PV performance in one screen. This flexibility let’s you identify trends, draw meaningful conclusions, and make confident data-driven decisions. *Example: Monthly total of GHI (on the left) vs. monthly total of precipitation (on the right) in January 2025* ## Recognize cyclic trends and extreme weather through graphs The graphical representation makes it easy to analyze seasonal trends and the impact of weather fluctuations over time, helping you grasp the full picture of performance and environmental factors. Additionally, the graph visualizes full historical data for any site, including monthly and yearly time series, as well as variability (shown as “difference”). *Example: The graph illustrates Time Series of monthly averages of wind speed for the full history of selected sites* ## Independent 3rd party assessment of PV performance The world map offers a comprehensive geographical context of solar and meteorological events, allowing you to quickly identify if external environmental factors contributed to unexpected underproduction. This helps determine whether the PV performance aligns with weather conditions and provides a clear understanding of when further investigation may be necessary. *Example: GHI monthly difference in January 2025 against long-term monthly average (%)* ## Search locations within a custom value range When a map layer is selected, it is possible to define the range of monthly or annual values that you want to see on a map. The map will visually provide you with the areas that fall into your desired range and grey out the rest. *Example 1: Geographical areas where GHI totals in January 2025 were higher than 150 [kWh/m²]* *Example 2: Geographical areas where GHI totals in 2024 were higher than 2000 [kWh/m²]* ## [Want to receive these regularly into your inbox?](https://solargis.com/products/monitor/monthly-reports) With Solargis Solarmaps it is possible to select only up to 3 sites to receive the data. Managing multiple PV projects or large portfolios? — Solargis Monitor Monthly Reports provide automated reports tailored to your PV portfolio every month. [Solargis Monitor Monthly Reports](https://solargis.com/products/monitor/monthly-reports) ## Download 2024 annual map - GHI summary and difference map ### [Solargis Monitor](https://solargis.com/products/monitor) - One solar data source for all sites - Gap- and error-free solar radiation data - Minimize meteo station inputs uncertainty - Benchmark planned performance with reality - Near real-time PV output assessment ### [Solargis Monitor Monthly Reports](https://solargis.com/products/monitor/monthly-reports) Receive vital solar and meteorological events for PV performance impact in detailed monthly PDFs that contain data and maps. Increase accountability and transparency in asset management, reduce time spent in preparing reports and equip your team with reliable data for effective communication with stakeholders. ### [Customized GIS Data](https://solargis.com/services/customized-gis-data) Explore solar, meteorological, and PV potential GIS data in your own applications. You can use solar resource, PV, climate, and other geo data for analysis and visualization in all generally available GIS software (GeoTIFF, NetCDF, and others) with raster data processing capabilities or numerical implementation. -------------------------------------------------------------------------------- ## Solar Performance Maps | Solargis Resources Source: https://solargis.com/products/solarmaps/free-monthly-maps # Free monthly maps to your inbox ## Understand the impact of GHI variability on the PV performance Global Horizontal Irradiation (GHI) is the most important weather factor affecting the energy production of solar photovoltaic power plants. Therefore, having reliable information on recent values of GHI is critical for understanding whether your solar portfolio is performing optimally or not. Maps showing recent values can be used as a preliminary and approximate reference, and they can help identify the need for running a more detailed energy assessment in these cases: ### 1. You find out that map values are not falling within the range of your previously expected values. — Then you might need a new estimation of the expected solar resource in the long-term, including an updated monthly variability analysis. ### 2. You find out that map values are not aligned with your on-site observations and the actual energy generation. — Then you might need a review of your on-site sensors, together with a deeper look into possible sources of underperformance in the PV plant. ## 2025 GHI difference maps ## Download all maps - Solargis yearly GHI difference maps - Format: zipped PNG files - Size: 28MB ## See also ## [Solargis Monitor Monthly Reports](https://solargis.com/products/monitor/monthly-reports) Receive vital solar and meteorological events for PV performance impact in detailed monthly PDFs that contain data and maps. Increase accountability and transparency in asset management, reduce time spent in preparing reports and equip your team with reliable data for effective communication with stakeholders. ## [2025 solar resource overview in maps: A year of exceptional highs and lows](https://solargis.com/resources/blog/solargis-news/2025-solar-resource-overview-in-maps) As every year, we bring you a map summarizing last year’s global horizontal irradiation (GHI) anomalies, highlighting how much regional weather conditions diverged from long-term averages (LTA). These maps help project developers, asset managers, and investors understand how atmospheric variability may have influenced PV performance worldwide. -------------------------------------------------------------------------------- ## Integrations | Solargis Source: https://solargis.com/products/integration # Solargis Integrations Automate solar & weather data delivery into your applications ## What are Solargis Integrations? Solargis makes its data available to third-party applications via a wide range of API and SFTP integrations. Get automated and continuous access to solar, meteo, and environmental parameters for feasibility analysis, solar resource assessment, performance evaluation, ground measurement benchmarking, PV production forecasts, and more. - Long-term averages API - Typical Meteorological Year API - Historical Time Series API - Solargis Monitor API - Solargis Forecast API - Solargis data via SFTP ## API / SFTP Integrations ### [Solargis LTA API](https://solargis.com/products/integration/solargis-long-term-averages-api) - XML format via REST API - Automatically updated global solar database - Estimate PV sites' solar potential - Accurate and validated data compatible with later project stages - Key solar and meteorological parameters ### [Solargis TMY API](https://solargis.com/products/integration/solargis-api-typical-meteorological-year-tmy) - Asynchronous API - Computed on-demand - Bankable data validated against ground measurements - API key-based, userless tokens available - Easy integration with custom tools and PV software ### [Solargis Time Series API](https://solargis.com/products/integration/solargis-time-series-api) - Asynchronous API - Supports high-volume requests - Validates inputs before charging credits - API key-based, userless tokens available - Easy integration with custom tools and PV software ### [Solargis Monitor API](https://solargis.com/products/integration/solargis-monitor-api) - XML format via REST API - Independent validation of reported PV performance - Gap- and error-free solar radiation data - Minimize uncertainty in meteo station inputs - Benchmark planned performance against reality ### [Solargis Forecast API](https://solargis.com/products/integration/solargis-forecast-api) - XML format via REST API - Solar power output forecasts for up to 14 days - Nowcasting updates up to every 5 minutes - Minimize penalties from grid operators - Schedule maintenance based on the forecasts ### [Solargis data via SFTP](https://solargis.com/products/integration/solargis-sftp-api) - Large CSV data transfers - Automated SFTP machine-to-machine delivery - Asynchronous requests - Solar, meteorological and PVOUT data - Daily updates with monthly reanalysis # SECTION: Pricing -------------------------------------------------------------------------------- ## Solargis Evaluate pricing & tiers Source: https://solargis.com/pricing/evaluate # Solargis Evaluate pricing ## Unlimited possibilities for limited budgets A 12,000 EUR Solargis Evaluate subscription now includes: - 60 Early Stage projects​ - Unlimited number of 15-minute TMY P50 simulations for every project​ - Unlimited number of designs for every project​ - Unlimited number of collaborators for every project​ - Solar, meteo and environmental data for every project​ -------------------------------------------------------------------------------- ## Solargis Prospect pricing & tiers Source: https://solargis.com/pricing/prospect # Solargis Prospect pricing ### Basic For small businesses working primarily on C&I projects #### € 2,400 / year - Monthly profiles of solar radiation and meteo data - PV energy modelling for fixed tilt systems - Project comparison tool for easy site and technology selection - Economic calculator for pre-feasibility analysis - User interface and PDF reports in 15+ languages ### Professional For organisations working on large scale or large volume of projects #### € 4,800 / year **All features from Basic plan, and** - Albedo data for accurate estimation of bifacial gain - Precipitable water and humidity data for modelling spectral loss/gain - Rainfall and snow days data for estimation of soiling and snow losses - Support for 1-axis tracker and floating solar - Assited onboarding ### Enterprise For organisations looking for maximum flexibility #### Let’s talk **All features from Professional plan, and** - Unlimited users - Number of projects upon agreement - Dedicated account manager - Custom paperwork ## Plan details ### Users and projects | | Basic | Professional | Enterprise | |---|---|---|---| | Number of full-access users | Up to 5 | Up to 10 | Unlimited | | New projects / year | Up to 500 | Up to 1000 | Upon agreement | | User roles | Regular and admin users | Regular and admin users | Regular and admin users | ### Data specification | | Basic | Professional | Enterprise | |---|---|---|---| | Spatial resolution of solar resource and PV outputs | 250 m x 250 m | 250 m x 250 m | 250 m x 250 m | | Spatial resolution of meteorological data | Up to 1 km x 1 km | Up to 1 km x 1 km | Up to 1 km x 1 km | | Data type | Monthly averages (all parameters) and 24 x 12 profiles (select parameters) | Monthly averages (all parameters) and 24 x 12 profiles (select parameters) | Monthly averages (all parameters) and 24 x 12 profiles (select parameters) | | Data period | From 1994/1999/2007 to 2025 | From 1994/1999/2007 to 2025 | From 1994/1999/2007 to 2025 | ### Data parameters | | Basic | Professional | Enterprise | |---|---|---|---| | Solar resource parameters (GHI, DIF, DNI) | ✓ | ✓ | ✓ | | Basic meteorological parameters (TEMP, WS) | ✓ | ✓ | ✓ | | Meteo parameters for calculation PV performance estimation (RH, PREC, PWAT, SNOWD, TLEO) | ✗ | ✓ | ✓ | | Ground albedo for bifacial simulation | ✗ | ✓ | ✓ | ### Energy modelling | | Basic | Professional | Enterprise | |---|---|---|---| | Calculate theoretical and lifetime PV production | ✓ | ✓ | ✓ | | Energy conversion steps detailed in tables and loss diagram | ✓ | ✓ | ✓ | | Support for 1-axis trackers | ✗ | ✓ | ✓ | | Support for floating solar | ✗ | ✓ | ✓ | ### Data and report downloads | | Basic | Professional | Enterprise | |---|---|---|---| | PDF reports (15+ language options supported) | ✓ | ✓ | ✓ | | XLSX and CSV data downloads | ✓ | ✓ | ✓ | | Horizon (.HOR) file export | ✓ | ✓ | ✓ | ### Terms of use | | Basic | Professional | Enterprise | |---|---|---|---| | View, download, and store data and reports for internal use | ✓ | ✓ | ✓ | | Share PDF reports with 3rd parties | ✓ | ✓ | ✓ | | Share CSV files with 3rd parties | ✗ | ✗ | upon agreement | ### Support and onboarding services | | Basic | Professional | Enterprise | |---|---|---|---| | Knowledge base, tutorials, webinars | ✓ | ✓ | ✓ | | 14/5 Email support | ✓ | ✓ | ✓ | | Assisted onboarding | ✗ | ✓ | ✓ | | Dedicated account manager | ✗ | ✗ | ✓ | | Custom legal documents & contracts | ✗ | ✗ | ✓ | -------------------------------------------------------------------------------- ## Solargis Monitor pricing & tiers Source: https://solargis.com/pricing/monitor # Solargis Monitor pricing ### Basic Includes essential data for basic performance analysis #### Let’s talk - Data for multiple roof orientations per project - Hourly time step - 3 month’s history ### Professional For larger power plants, where even 1% performance improvement makes a big difference. All features available #### Let’s talk - 10/15 minute time step - Support for PV tracking systems - 12 months' history - Solargis Analyst software available as add-on ### Enterprise Customized offer for high-volume needs, advanced analytics or reporting, reseller partnerships, etc. #### Let’s talk - Dedicated account manager - Professional services - Monthly billing - Possibility to share data with 3rd parties ## Plan details ### Core features | | Basic | Professional | Enterprise | |---|---|---|---| | Global coverage | ✓ | ✓ | ✓ | | Solar resource parameters (GHI, DIF, DNI) at 90 m resolution | ✓ | ✓ | ✓ | | Elevation corrected air temperature (TEMP) at 1 km resolution | ✓ | ✓ | ✓ | | Detailed PV energy modelling | ✓ | ✓ | ✓ | ### Key specification | | Basic | Professional | Enterprise | |---|---|---|---| | Number of sites | Base plan includes access for up to 5 sites/year. Contact sales to get pricing for higher volume plans. | Base plan includes access for up to 5 sites/year. Contact sales to get pricing for higher volume plans. | Base plan includes access for up to 5 sites/year. Contact sales to get pricing for higher volume plans. | | Time step | Hourly | Up to 10/15 minutes | Up to 10/15 minutes | | Data available up to previous day | ✓ | ✓ | ✓ | | Real-time data updates | Available as add-on | Available as add-on | ✓ | | Access to data for recent history | Last 3 months | Last 12 months | Custom | ### Data parameters | | Basic | Professional | Enterprise | |---|---|---|---| | Solar resource parameters (GHI, DIF, DNI) | ✓ | ✓ | ✓ | | Basic meteorological parameters (TEMP, WS, WD) | ✓ | ✓ | ✓ | | Meteo parameters for calculation of spectral loss/gain (RH, PWAT, AP) | ✗ | ✓ | ✓ | | Meteo parameters to help estimate snow and soiling lossess (SDWE, PREC) | ✗ | ✓ | ✓ | | Wind gust (WG) | ✗ | ✓ | ✓ | ### PV modelling and KPIs | | Basic | Professional | Enterprise | |---|---|---|---| | Global tilted irradiation (GTI) | Fixed mount systems only | Fixed-mount + tracker systems | Fixed-mount + tracker systems | | PV simulation based on detailed configuration of power plant | Fixed mount systems only | Fixed-mount + tracker systems | Fixed-mount + tracker systems | | Automatic calculation of horizon shading | ✓ | ✓ | ✓ | | PV module temperature | ✗ | ✓ | ✓ | | Weather-corrected performance ratio | ✗ | ✗ | Available as add-on | | Weather correction factor | ✗ | ✗ | Available as add-on | ### Delivery method and support | | Basic | Professional | Enterprise | |---|---|---|---| | Data access | SFTP or API | SFTP or API | SFTP or API | | Onboarding and support for configuration of SFTP/API requests | ✓ | ✓ | ✓ | | Dedicated account manager | ✗ | ✗ | ✓ | | Reporting | Available as add-on | Available as add-on | Available as add-on | | Quality control and gap-filling of pyranometer measurements | ✗ | Available as add-on | Available as add-on | -------------------------------------------------------------------------------- ## Solargis Forecast pricing & tiers Source: https://solargis.com/pricing/forecast # Solargis Forecast pricing ### Basic Best value option for day-ahead forecasting requirements #### Let’s talk - Hourly time step - D0 + 7 days ahead - Forecast update frequency of 6 hours ### Professional For more accurate intra-day forecasts and advanced forecasting requirements #### Let’s talk - Sub-hourly time step - D0 + 14 days ahead - Forecast update frequency 10/15 minutes - Support for tracker systems ### Enterprise Customized forecasts for individual power plants or aggregated forecasts for thousands of power plants #### Let’s talk - Custom forecast formats - Aggregated forecasts - Customized solutions: sky-camera, advanced snow handling, etc. - Dedicated account manager ## Plan details ### Core features | | Basic | Professional | Enterprise | |---|---|---|---| | Solar resource parameters (GHI, DIF, DNI) at 90 m resolution | ✓ | ✓ | ✓ | | Elevation corrected air temperature (TEMP) at 1 km resolution | ✓ | ✓ | ✓ | | Satellite-based nowcasting | ✓ | ✓ | ✓ | | Numerical Weather Prediction models | Ensemble of IFS, GFS, ICON and HRRR models | Ensemble of IFS, GFS, ICON and HRRR models | Ensemble of IFS, GFS, ICON and HRRR models | | Detailed PV energy modelling | ✓ | ✓ | ✓ | ### Key specification | | Basic | Professional | Enterprise | |---|---|---|---| | Forecast range | D0+7 | D0+14 | D0+14 | | Frequency of updates | Every 6 hours | Up to every 5 minutes during daytime hours, 6 hours between sunset and sunrise | Up to every 5 during daytime hours, 6 hours between sunset and sunrise | | Time step | Hourly | 5/10/15 minutes | 5/10/15 minutes | | Historical forecasts for accuracy evaluation | Available as add-on | Available as add-on | Available as add-on | ### Solar resource and meteo data included | | Basic | Professional | Enterprise | |---|---|---|---| | Basic solar resource parameters (GHI, DIF, DNI) | ✓ | ✓ | ✓ | | Basic meteorological parameters (TEMP, WS, WD ) | ✓ | ✓ | ✓ | | Meteo parameters for calculation of spectral loss/gain (RH, PWAT, AP) | ✗ | ✓ | ✓ | | Meteo parameters to help estimate snow and soiling lossess (SDWE, PREC) | ✗ | ✓ | ✓ | | Wind gust (WG) | ✗ | ✓ | ✓ | | Hail risk: available only in the Continental U.S. | ✗ | ✓ | ✓ | ### PV modelling | | Basic | Professional | Enterprise | |---|---|---|---| | Global tilted irradiation (GTI) | Fixed mount systems only | Fixed-mount + tracker systems | Fixed-mount + tracker systems | | PV simulation based on detailed configuration of power plant | Fixed mount systems only | Fixed-mount + tracker systems | Fixed-mount + tracker systems | | Automatic calculation of horizon shading | ✓ | ✓ | ✓ | | Accuracy enhancement using actual PV production data | Available as add-on | ✓ | ✓ | | Wind power forecasts | Available as add-on | Available as add-on | ✓ | ### Delivery method and support | | Basic | Professional | Enterprise | |---|---|---|---| | Data access | SFTP or API | SFTP or API | SFTP, API, e-mail | | Onboarding and support for configuration of SFTP/API requests | ✓ | ✓ | ✓ | | Custom formats and delivery modes | ✗ | Available as add-on | ✓ | | Reporting | ✗ | Available as add-on | ✓ | | Dedicated account manager | ✗ | ✗ | ✓ | # SECTION: Solutions (use cases) -------------------------------------------------------------------------------- ## Use cases | Solargis Source: https://solargis.com/solutions # Use cases ### [Find the right solar project location](https://solargis.com/solutions/site-selection) Scan and compare tens or even hundreds of potential sites. Get an in-depth analysis of those with the highest solar potential. ### [Analyze potential gains](https://solargis.com/solutions/energy-yield-simulation) Simulate the yield potential of your next project with the most accurate solar and weather data on the market. ### [Find optimal power plant design](https://solargis.com/solutions/optimizing-power-plant-design) Get deep insights into potential power plant setups. Evaluate environmental data that will affect an asset’s efficiency and output. ### [Discover true output](https://solargis.com/solutions/real-power-plant-performance) Compare the actual performance of your power plant to the predictions. Identify the reasons for any lower production. ### [Predict your solar project energy output](https://solargis.com/solutions/power-output-forecast) Manage variability with 14-day power output forecasts. Get insights for trading, reduce grid penalties, and plan maintenance through accurate energy output predictions. ### [Improve data quality and reduce uncertainty](https://solargis.com/solutions/ground-data-verification) Cross-reference your ground measurements with satellite data. Uncover potential issues such as sensor soiling, shading, or equipment error. -------------------------------------------------------------------------------- ## Find the right location for your solar project | Solargis Source: https://solargis.com/solutions/site-selection # Find the right location for your next solar project Scan and compare hundreds of potential sites. Get an in-depth analysis of those with the best solar potential. Pick the most promising ones. ## [Make data-driven decisions in minutes](https://solargis.com/products/prospect) With so many opportunities for solar projects all over the globe, making the right choice about a site is getting harder. Having the right information about potential sites, in real-time, gives you the flexibility to react quickly to offers and requests. [Solargis Prospect](https://solargis.com/products/prospect) ## [Compare multiple opportunities](https://solargis.com/products/prospect/features) The location and conditions of a site directly influence the ROI of your solar project. Using our satellite technology and weather models, you can access in-depth data for any site, without the need for on-site measurements. This way, you can efficiently compare multiple sites and opportunities, as well as various technical configurations, and pick the ones that promise the highest returns. ## [Dive into the details](https://solargis.com/products/prospect/data-specs) Geographical and climate conditions are diverse and can differ even between sites that are close to each other. High spatial resolution and regularly updated maps are vital for characterizing site-specific conditions and choosing the best candidates for your solar project. With a complete set of parameters, you can create proposals that are technically sound, based on scientific evidence, not on “rule-of-thumb” assumptions. [Solargis Prospect data specs](https://solargis.com/products/prospect/data-specs) ## Related products and services ### [Solargis Prospect](https://solargis.com/products/prospect) - Reliable and accurate solar data - Fast sites comparison - Comprehensive environmental overview - Analytics and solar power calculator - Collaboration and multilingual support ### [Customized GIS Data](https://solargis.com/services/customized-gis-data) Explore solar, meteorological, and PV potential GIS data in your own applications. You can use solar resource, PV, climate, and other geo data for analysis and visualization in all generally available GIS software (GeoTIFF, NetCDF, and others) with raster data processing capabilities or numerical implementation. ### [Regional Solar Energy Potential Study](https://solargis.com/services/regional-solar-energy-potential-study) In the Regional Solar Energy Potential Study, we analyze not only solar resource information but also meteorological and geographic data. The analysis considers the uncertainty of resource estimates, intermittent and seasonal variability, extreme weather, and geographic limitations on the deployment of solar power plants. ## Useful resources ## [Google Earth maps for the whole world released by Solargis on Global Solar Atlas](https://solargis.com/resources/blog/solargis-news/google-earth-maps-on-global-solar-atlas) Solargis releases GHI, DNI and PVOUT maps for Google Earth in kmz format. ## [Supporting decade of growth in the solar sector](https://solargis.com/resources/blog/solargis-news/supporting-decade-of-growth-in-solar-sector) At Solargis, we have supported a decade of growth in the solar sector – and continue to support it through its transition into a new phase of post-subsidy development and operation. Reducing risk in this changing landscape requires a focus on efficiency. Solar asset owners are looking to get the most out of their projects, supported by the latest developments in solar and meteorological data. ## [Solargis resource data used to underpin rapid growth of SunSource Energy](https://solargis.com/resources/blog/solargis-news/solargis-resource-data-used-to-underpin-rapid-growth-of-sunsource-energy) High-resolution meteorological data crucial as Indian demand continues to increase for Commercial & Industrial solar projects. -------------------------------------------------------------------------------- ## Analyze the gains & risks of your solar project | Solargis Source: https://solargis.com/solutions/energy-yield-simulation # Analyze the potential gains & risks of your solar project Reduce uncertainty and minimize financial risk through best- and worst-case scenarios. Simulate the yield potential of your PV power plant to evaluate its financial feasibility. ## [Explore best- and worst-case scenarios](https://solargis.com/products/evaluate) Every energy yield calculation should include a detailed assessment of the range of weather variability at the project site. For that, you will need a full record of past weather conditions – considering weather trends and extremes. Then you’ll understand how the yield calculation models work in the simulation. This approach will enable you to accurately differentiate between your best- and worst-case scenarios, taking into account energy losses and related uncertainty. [Solargis Evaluate](https://solargis.com/products/evaluate) ## Estimate the project’s yield based on the most accurate data Use a comprehensive history of sub-hourly time series to capture detailed insights on expected production. Solargis energy yield simulations have been validated across hundreds of real-life projects and through independent academic studies. They are trusted by financial institutions and investors worldwide, who use Solargis data for tens of thousands of project evaluations each year. ## [Secure financing for your project](https://solargis.com/services/pv-energy-yield-assessment) Building a solar power plant requires a multi-million dollar upfront investment. Developers, investors, and financial institutions are all exposed to financial risk, both at the outset of the project and during its long-term operation. To manage this, they need to reliably evaluate the sustainability of energy generation and calculate the future earnings of the PV plant. Solargis yield simulation provides all the necessary data and solutions for these evaluations – improving confidence in vital investment decisions. ## Related products and services ### [Solargis Evaluate](https://solargis.com/products/evaluate) - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Unmatched level of detail and accuracy - PV simulation based on ray tracing and Perez all-weather sky model ### [Site Adaptation of Solargis Models](https://solargis.com/services/site-adaptation-of-solargis-models) Combine satellite data with on-site measurements to reduce the uncertainty of estimated energy output and achieve more accurate financial estimates. The Site Adaptation of Solargis Models service will give you locally enhanced solar and meteo parameters, enabling you to reduce uncertainty of power plant design and energy yield simulations. ### [PV Energy Yield Assessment](https://solargis.com/services/pv-energy-yield-assessment) For financial risk assessment, investors and developers require reports of expected energy production, including uncertainties, as well as related solar and meteorological data inputs. We offer an independent and impartial evaluation of PV yield assessment with our proprietary simulation tools and models. These are based on 15+ years of experience working on large and medium-scale PV power plants around the world. ### [PV Performance Assessment](https://solargis.com/services/pv-performance-assessment) Data-driven insights from our PV Performance Assessment report conducted after months or years of the plant’s operation will help you optimize its performance. The report also provides a revised and more accurate long-term energy yield estimate for refinancing or new asset acquisition purposes. ### [Solargis TMY API](https://solargis.com/products/integration/solargis-api-typical-meteorological-year-tmy) - Asynchronous API - Computed on-demand - Bankable data validated against ground measurements - API key-based, userless tokens available - Easy integration with custom tools and PV software ### [Solar Resource & Meteo Assessment](https://solargis.com/services/solar-resource-assessment) For larger and utility-scale solar projects, you need long-term solar and meteorological data to be regionally validated with the right uncertainty estimates. We can provide you with a detailed solar resource validation and assessment report as an add-on alongside standard data delivery. ## Useful resources ## [Why is 1-minute data essential for solar project financiers?](https://solargis.com/resources/blog/best-practices/why-is-1-minute-data-essential-for-solar-project-financiers) One of the key challenges for solar project financiers is to assess the expected energy production and revenue of a project over its lifetime, considering the uncertainties and fluctuations of the solar resource. ## [How to calculate P90 (or other Pxx) PV energy yield estimates](https://solargis.com/resources/blog/best-practices/how-to-calculate-p90-or-other-pxx-pv-energy-yield-estimates) One of the most critical outputs from PV simulations is the P50 annual energy yield estimate. Often referred to as the "best estimate," the P50 value represents the annual energy yield that has a 50% probability of being exceeded (with an equal 50% chance that the actual yield will fall below it). — However, relying solely on the P50 value may be too optimistic for project stakeholders. To address this, additional probability-based yield estimates are commonly used e.g. P90 value, which indicates the energy yield expected to be exceeded 90% of the time. ## [Managing Complexity: 5 benefits of high-frequency data for solar + storage projects](https://solargis.com/resources/blog/best-practices/managing-complexity-5-benefits-of-high-frequency-data-for-solar-storage-projects) Storing and dispatching power at the optimal time requires an extremely granular understanding of how much power a project is generating both now and in the immediate future. Developers, operators, and owners increasingly require high-frequency (1-minute) weather and solar irradiance data that enables them to make quick, accurate and financially effective decisions. -------------------------------------------------------------------------------- ## Discover your optimum power plant design | Solargis Source: https://solargis.com/solutions/optimizing-power-plant-design # Discover your optimum power plant design Deep dive, analyze, and aggregate the data for various power plant configurations within the given environmental conditions. ## [Evaluate various power plant configurations](https://solargis.com/products/evaluate) Solar power plants need to adapt to complex local environmental and terrain conditions. Site designs and structures must be configured not only to make the most of the solar resource, but also to account for surrounding objects and infrastructure – such as roads, hills, buildings and the electrical grid. Solargis data and software give you the necessary insights to evaluate the options and determine the best possible setup for your plant. [Solargis Evaluate](https://solargis.com/products/evaluate) ## Tackle the specific challenges of bifacial & floating designs Bifacial PV modules require new computational concepts based on location-specific high-resolution albedo data. Meanwhile, floating PV power plants come with new challenges such as wave impact and microclimate conditions above the water surface, including vapor, humidity, fog, temperature, and wind. You can address all these challenges with Solargis simulations. ## [Take local extremes into account](https://solargis.com/services/pv-variability-and-grid-integration-study) A solar power plant is a long-term project for 30+ years. To ensure its efficient operation over such a long period, you need to consider potential long-term risks, even if they occur infrequently. Solargis simulations can help you design a resilient project – by studying risks and local extreme weather conditions such as the lowest air temperatures, soiling, hail, snowfall, wind gusts, and others. [Get PV Variability & Storage Optimization Study](https://solargis.com/services/pv-variability-and-grid-integration-study) ## Related products and services ### [Solargis Evaluate](https://solargis.com/products/evaluate) - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Unmatched level of detail and accuracy - PV simulation based on ray tracing and Perez all-weather sky model ### [PV Variability & Storage Optimization Study](https://solargis.com/services/pv-variability-and-grid-integration-study) The PV Variability & Storage Optimization Study delivers statistical data and insights to project developers needed for designing and managing PV-plus-storage systems. The study provides the most realistic data on PV power generation for grid integration analysis. ### [Solargis TMY API](https://solargis.com/products/integration/solargis-api-typical-meteorological-year-tmy) - Asynchronous API - Computed on-demand - Bankable data validated against ground measurements - API key-based, userless tokens available - Easy integration with custom tools and PV software ## Useful resources ## [Impact of Extreme Weather Conditions on PV Projects](https://solargis.com/resources/webinars/impact-of-extreme-weather-conditions-on-pv-projects) Our expert team gives actionable tips on how to avoid the negative surprises of extreme weather and shares some of our customers' best practices. ## [4 reasons why PV project designers need 1-minute data](https://solargis.com/resources/blog/best-practices/4-reasons-why-pv-project-designers-need-1-minute-data) To design optimal PV projects, designers must consult 1-minute data which paint the most accurate picture of a plants’ PV power potential and output, while providing increased financial certainty to solar investors. ## [Growing Pain #1: Maximising efficiency and minimising risk in large-scale project design](https://solargis.com/resources/blog/best-practices/growing-pain-1-maximising-efficiency-and-minimising-risk-in-large-scale-project-design) Ten years ago, an average solar PV project was a relatively simple affair, comprising 10MW of fixed, monofacial modules. The market has moved on a long way since then. Now, typical solar farms approach 100MW in size and may use a range of technologies such as bifacial, intelligent tracking and floating modules, creating new possibilities for more efficient energy production. -------------------------------------------------------------------------------- ## Understand the output of your solar project | Solargis Source: https://solargis.com/solutions/real-power-plant-performance # Understand the actual output of your solar project Compare the true performance of your power plant to its expected output. Identify reasons for underperformance. Provide consistent reporting to external stakeholders. ## [Benchmark your planned performance with reality](https://solargis.com/products/monitor) Operational solar power plants require systematic performance monitoring to ensure they are delivering against expectations. With Solargis, you can compare your power plant’s actual performance to predictions made during the design phase. This will help you uncover potential long- or short-term issues if the real output does not match expectations. [Solargis Monitor](https://solargis.com/products/monitor) ## [Discover the root causes of underperformance](https://solargis.com/products/monitor) If a plant is underperforming, you need to find out why the actual output does not match the expectations. Solargis can help you determine if lower production is caused by weather or solar conditions or another issue such as equipment failure or technical problems. This will give you the certainty to go ahead and make adjustments. [Explore more](https://solargis.com/products/monitor) ## [Run continuous reports for multiple sites](https://solargis.com/products/monitor/resources) In the past, operators of large PV plant portfolios lacked unified reporting where they could compare “apples to apples” and present the results to the executive team in an easy-to-understand way. Solargis provides continuous performance reporting for your bank, investors, and other stakeholders so they can see how the project is progressing. The reports come in the same format and with the same parameters, making it easy to compare the performance of multiple sites in your portfolio. [See report samples](https://solargis.com/products/monitor/resources) ## Related products and services ### [Solargis Monitor](https://solargis.com/products/monitor) - One solar data source for all sites - Gap- and error-free solar radiation data - Minimize meteo station inputs uncertainty - Benchmark planned performance with reality - Near real-time PV output assessment ### [PV Performance Assessment](https://solargis.com/services/pv-performance-assessment) Data-driven insights from our PV Performance Assessment report conducted after months or years of the plant’s operation will help you optimize its performance. The report also provides a revised and more accurate long-term energy yield estimate for refinancing or new asset acquisition purposes. ### [PV Variability & Storage Optimization Study](https://solargis.com/services/pv-variability-and-grid-integration-study) The PV Variability & Storage Optimization Study delivers statistical data and insights to project developers needed for designing and managing PV-plus-storage systems. The study provides the most realistic data on PV power generation for grid integration analysis. ## Useful resources ## [Shift Energy Japan uses Solargis Monitor to track and assess its projects’ financial performance](https://solargis.com/resources/success-stories/shift-energy-japan-uses-solargis-monitor-to-track-and-assess-its-projects-financial-performance) Shift Energy Japan uses Solargis Monitor as a replacement for the scarce, publicly available data to track and assess its projects’ financial performance and deliver key insights into operations. ## [How satellite model data help with storage applications](https://solargis.com/resources/webinars/how-satellite-model-data-help-with-storage-applications) In this webinar, we will present how 1-minute granularity data can also help with optimal sizing of batteries and replacement strategy— even during the initial design phase. ## [Solar power performs amid hottest year on record in 2023](https://solargis.com/resources/blog/solargis-news/solar-power-performs-amid-hottest-year-on-record-in-2023) Solargis has analyzed the annual performance of solar irradiance for 2023, revealing the trends and variations of solar irradiance across the globe. -------------------------------------------------------------------------------- ## Predict the output of your solar project | Solargis Source: https://solargis.com/solutions/power-output-forecast # Predict the output of your solar power project Manage variability with accurate 14-day power output forecasts. Get insights for battery management and trading. Reduce grid penalties and plan power plant maintenance. ## [Manage batteries with short-term forecasting](https://solargis.com/products/forecast) Intraday forecasting is increasingly important for PV plant operators seeking to optimize battery management and energy trading. Solargis provides short-term forecasts to help you understand power output for the coming hours. These forecasts enable you to manage the charging and discharging of batteries to smooth variability, and shift noon power production to evening hours. [Solargis Forecast](https://solargis.com/products/forecast) ## [Prepare your next-day production plan](https://solargis.com/products/forecast#image-cards) Day-ahead forecasting for a single PV project or a PV project portfolio allows you to better prepare your power production plans, both for submission to the grid operator and for trading. Improved predictability of power plant operations and higher forecast accuracy reduce the risk of penalties from grid operators due to under- or over-delivery of electricity to the grid. Solargis data supports forecasting of power output up to 14 days in advance. ## [Plan your maintenance schedules](https://solargis.com/products/forecast) With a strong understanding of the future energy output of your power plant, you can schedule maintenance work for times that will have the smallest impact on revenue. For example, technical maintenance can be scheduled to take place during periods of cloudy weather with lower energy output. Or you can plan for drone mapping when you expect sunny weather and clear skies. [Find out more](https://solargis.com/products/forecast) ## Support grid balancing Power forecasting data is essential for supplying reliable services to the local energy grid. Solargis forecasting can help you drive better organization among energy system actors and plan around your production for the coming days. It enables you to better understand your ability to supply ancillary grid balancing services and enhance your relationship with the grid operator. ## Related products and services ### [Solargis Forecast](https://solargis.com/products/forecast) - Solar power output forecast for up to 14 days - Nowcasting every 15 minutes up to 3 hours - Minimize penalties from grid operators - Schedule maintenance based on the forecast - Manage the variability of your PV plant portfolio ## Useful resources ## [How Solargis is improving accuracy of solar power forecasts](https://solargis.com/resources/blog/best-practices/improving-accuracy-of-solar-power-forecasts) Just as there are horses for courses, different forecasting techniques are more suitable depending on the intended forecast lead time. ## [Growing Pain #4: Effective integration – Managing grid and storage requirements](https://solargis.com/resources/blog/best-practices/growing-pain-4-effective-integration-managing-grid-and-storage-requirements) As solar projects grow in size and number, the challenges of integrating them successfully into national grids increase as well. The intermittency of solar energy requires careful management, and solar developers that under- or over-produce face curtailment and penalties from grid operators. ## [How satellite model data help with storage applications](https://solargis.com/resources/webinars/how-satellite-model-data-help-with-storage-applications) In this webinar, we will present how 1-minute granularity data can also help with optimal sizing of batteries and replacement strategy— even during the initial design phase. -------------------------------------------------------------------------------- ## Turn on-site measurements into bankable reports | Solargis Source: https://solargis.com/solutions/ground-data-verification # Increase quality of measurements for regular performance reporting Identify sensor, soiling, shading, and equipment issues by improving data quality. Harmonize inputs from multiple sources and fill in the gaps. ## [Learn how (in)correct pyranometer measurements are](https://solargis.com/products/analyst) The accuracy of on-site pyranometer readings can be affected by a number of different factors. Therefore, it is vital to have reliable reference data to understand how correct your measurements are. Thanks to data analysis supported by our satellite-based Time Series, Solargis is able to identify and flag errors in measured data and recommend improvements to reduce the occurrence of these issues. [Solargis Analyst](https://solargis.com/products/analyst) ## [Use ground measurements for site adaptation](https://solargis.com/services/site-adaptation-of-solargis-models) The output of models may not always perfectly reflect local conditions. Measured data combined with time series help improve our models and provide you with more precise inputs. By complementing satellite irradiation data with ground measurements, you can reduce the uncertainty of energy output estimates. [Site Adaptation of Solargis Models](https://solargis.com/services/site-adaptation-of-solargis-models) ## [Regular, bankable reporting for financial stakeholders](https://solargis.com/services/quality-control-of-solar-radiation-meteo-measurements) Measurements from operational power plants often come from multiple sensors. We take these measurements, run quality controls, harmonize, and gap-fill data to assemble a complete time series that is ready for regular performance evaluation. We help you convert your on-site measurements into bankable reports trusted by financial stakeholders all around the world. [Quality Control of Solar & Meteo Measurements](https://solargis.com/services/quality-control-of-solar-radiation-meteo-measurements) ## Related products and services ### [Solargis Analyst](https://solargis.com/products/analyst) - Visualize complex and big solar datasets - Compare measured data to model outputs - Identify and clean errors from measurements - Harmonize multisource input streams - Streamline solar data management ### [Quality Control of Solar & Meteo Measurements](https://solargis.com/services/quality-control-of-solar-radiation-meteo-measurements) One of the key challenges of measured solar irradiance data is the high occurrence of anomalous values. The Quality Control of Solar & Meteo Measurements service, based on our experience with measurements from hundreds of locations globally, helps you identify errors and prepare the datasets for the next steps of your project. ### [Site Adaptation of Solargis Models](https://solargis.com/services/site-adaptation-of-solargis-models) Combine satellite data with on-site measurements to reduce the uncertainty of estimated energy output and achieve more accurate financial estimates. The Site Adaptation of Solargis Models service will give you locally enhanced solar and meteo parameters, enabling you to reduce uncertainty of power plant design and energy yield simulations. ## Useful resources ## [How to validate solar models using ground measurements](https://solargis.com/resources/ebooks/how-to-validate-solar-models-using-ground-measurements) Since there is effectively no updated independent study that compares all databases globally, validation statistics are one of the most important tools when comparing models from different providers. ## [How meteorological factors can affect the accuracy of your ground measured data](https://solargis.com/resources/webinars/how-meteorological-factors-can-affect-the-accuracy-of-your-ground-measured-data) In this webinar we cover how to evaluate the impact of the different factors that can affect the accuracy of your ground-measured data. ## [Growing Pain #3: On-site measurements in large-scale solar](https://solargis.com/resources/blog/best-practices/growing-pain-3-on-site-measurements-in-large-scale-solar) Designing and operating a large-scale solar project without fully understanding its potential output inevitably increases risks throughout its lifecycle. One part of the solution for developers is validated solar resource data calculated through satellite-based models, helping produce accurate energy yield calculations. # SECTION: Services -------------------------------------------------------------------------------- ## Services | Solargis Source: https://solargis.com/services # Services ### [Solar Resource & Meteo Assessment](https://solargis.com/services/solar-resource-assessment) For larger and utility-scale solar projects, you need long-term solar and meteorological data to be regionally validated with the right uncertainty estimates. We can provide you with a detailed solar resource validation and assessment report as an add-on alongside standard data delivery. ### [Site Adaptation of Solargis Models](https://solargis.com/services/site-adaptation-of-solargis-models) Combine satellite data with on-site measurements to reduce the uncertainty of estimated energy output and achieve more accurate financial estimates. The Site Adaptation of Solargis Models service will give you locally enhanced solar and meteo parameters, enabling you to reduce uncertainty of power plant design and energy yield simulations. ### [Quality Control of Solar & Meteo Measurements](https://solargis.com/services/quality-control-of-solar-radiation-meteo-measurements) One of the key challenges of measured solar irradiance data is the high occurrence of anomalous values. The Quality Control of Solar & Meteo Measurements service, based on our experience with measurements from hundreds of locations globally, helps you identify errors and prepare the datasets for the next steps of your project. ### [Customized GIS Data](https://solargis.com/services/customized-gis-data) Explore solar, meteorological, and PV potential GIS data in your own applications. You can use solar resource, PV, climate, and other geo data for analysis and visualization in all generally available GIS software (GeoTIFF, NetCDF, and others) with raster data processing capabilities or numerical implementation. ### [PV Energy Yield Assessment](https://solargis.com/services/pv-energy-yield-assessment) For financial risk assessment, investors and developers require reports of expected energy production, including uncertainties, as well as related solar and meteorological data inputs. We offer an independent and impartial evaluation of PV yield assessment with our proprietary simulation tools and models. These are based on 15+ years of experience working on large and medium-scale PV power plants around the world. ### [PV Performance Assessment](https://solargis.com/services/pv-performance-assessment) Data-driven insights from our PV Performance Assessment report conducted after months or years of the plant’s operation will help you optimize its performance. The report also provides a revised and more accurate long-term energy yield estimate for refinancing or new asset acquisition purposes. ### [PV Variability & Storage Optimization Study](https://solargis.com/services/pv-variability-and-grid-integration-study) The PV Variability & Storage Optimization Study delivers statistical data and insights to project developers needed for designing and managing PV-plus-storage systems. The study provides the most realistic data on PV power generation for grid integration analysis. ### [Regional Solar Energy Potential Study](https://solargis.com/services/regional-solar-energy-potential-study) In the Regional Solar Energy Potential Study, we analyze not only solar resource information but also meteorological and geographic data. The analysis considers the uncertainty of resource estimates, intermittent and seasonal variability, extreme weather, and geographic limitations on the deployment of solar power plants. -------------------------------------------------------------------------------- ## Solar Resource & Meteo Assessment - Services | Solargis Source: https://solargis.com/services/solar-resource-assessment # Solar Resource & Meteo Assessment As solar projects grow in scale and move into emerging markets worldwide, it is increasingly important for long-term solar and meteorological data to be validated on a regional basis, with the right uncertainty estimates. We can provide you with a detailed solar resource assessment and validation report as an add-on to our standard data delivery. ### [Modeled solar resource and meteo data validation](https://solargis.com/technology/expertise) To support the bankability of the assessment, we include solar and meteorological data validation from nearby sites. Regional validation statistics provide a better indication of the expected quality of GHI and DNI model estimates. [More about our technology](https://solargis.com/technology/expertise) ### Specific uncertainty of solar and meteo estimates Our reports include uncertainty and probability of exceedance values for multiple scenarios. We derive this key information from an analysis of the representativeness and accuracy of regional measurements, model validation, and interannual variability. ### [Integration of on-site measurements to reduce uncertainty](https://solargis.com/services/site-adaptation-of-solargis-models) If available, we integrate on-site measurements of solar and meteorological data to perform site adaptation of the Solargis model. This enables the delivery of long-term time series with reduced uncertainty, giving project stakeholders more confidence. [Site Adaptation of Solargis Models](https://solargis.com/services/site-adaptation-of-solargis-models) > "We had been in touch with several meteo providers and we found that Solargis provides the most representative estimates for each site." > > — Fadi Ferzli, Senior Manager, Technical Sales and Engineering ## Discover more ### [Solargis Evaluate](https://solargis.com/products/evaluate) - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Unmatched level of detail and accuracy - PV simulation based on ray tracing and Perez all-weather sky model ### [Solargis Monitor](https://solargis.com/products/monitor) - One solar data source for all sites - Gap- and error-free solar radiation data - Minimize meteo station inputs uncertainty - Benchmark planned performance with reality - Near real-time PV output assessment ### [Site Adaptation of Solargis Models](https://solargis.com/services/site-adaptation-of-solargis-models) Combine satellite data with on-site measurements to reduce the uncertainty of estimated energy output and achieve more accurate financial estimates. The Site Adaptation of Solargis Models service will give you locally enhanced solar and meteo parameters, enabling you to reduce uncertainty of power plant design and energy yield simulations. ### [PV Energy Yield Assessment](https://solargis.com/services/pv-energy-yield-assessment) For financial risk assessment, investors and developers require reports of expected energy production, including uncertainties, as well as related solar and meteorological data inputs. We offer an independent and impartial evaluation of PV yield assessment with our proprietary simulation tools and models. These are based on 15+ years of experience working on large and medium-scale PV power plants around the world. ### [PV Performance Assessment](https://solargis.com/services/pv-performance-assessment) Data-driven insights from our PV Performance Assessment report conducted after months or years of the plant’s operation will help you optimize its performance. The report also provides a revised and more accurate long-term energy yield estimate for refinancing or new asset acquisition purposes. ## Useful for ### [Find the right solar project location](https://solargis.com/solutions/site-selection) Scan and compare tens or even hundreds of potential sites. Get an in-depth analysis of those with the highest solar potential. ### [Find optimal power plant design](https://solargis.com/solutions/optimizing-power-plant-design) Get deep insights into potential power plant setups. Evaluate environmental data that will affect an asset’s efficiency and output. ### [Improve data quality and reduce uncertainty](https://solargis.com/solutions/ground-data-verification) Cross-reference your ground measurements with satellite data. Uncover potential issues such as sensor soiling, shading, or equipment error. -------------------------------------------------------------------------------- ## Site Adaptation of Solargis Models | Solargis Source: https://solargis.com/services/site-adaptation-of-solargis-models # Site Adaptation of Solargis Models Medium and large-scale ground-based projects are sensitive to every percentage point of uncertainty. Higher uncertainty reflects in higher risk and less favorable financing terms. We combine Solargis satellite-based data with your on-site measurements to reduce the uncertainty of the estimated energy output and achieve more accurate financial estimates. If you have high-quality on-site measurements of irradiation for a period of 9-12 months, we can adapt our satellite data to improve the overall bias and fit of statistical characteristics. Our Site Adaptation service will give you locally-enhanced solar and meteo parameters. This way, you can reduce uncertainty of power plant design and energy yield simulations to a minimum. ### Quality assessment of ground measurements Sensors on the ground are prone to errors – making the process of data evaluation challenging. We take the ground-based measurements and run a suite of procedures to identify errors, validate the measurements, and check for missing data. Only inputs that pass our quality assurance tests are used in the Site Adaptation service to ensure the process is not impacted by poor-quality data. ### Re-computation of solar parameters In the Site Adaptation process we use model inputs that better represent the local conditions and recalculate the data. This gives us results that have lower uncertainty, both for the full 30-year history of the site and for the future. As a result, we deliver higher-accuracy data with features adapted to local conditions, reducing both systematic and random errors. The delivered data is also internally better harmonized (with better quality and structure). With 20+ years of experience, 2000+ projects, and full control of our proprietary models, with the Site Adaptation service we are able to deliver data of incomparable quality. ### Estimating local solar and meteo conditions correctly The site adaptation process helps fix issues in satellite-derived data, such as under/over-estimation of local aerosol loads. This is especially true when the magnitude of this deviation does not vary over time or has a seasonal periodicity. We can adapt satellite-derived DNI and GHI, air temperature, and other meteorological parameters to the local climate conditions, which cannot be captured in the original satellite and atmospheric inputs. ### Enhancing meteorological parameters Based on the availability of ground measurements and using our post-processing algorithms, we validate and improve the accuracy of parameters such as air temperature, wind, relative humidity, precipitation, albedo, and others. This gives us results that have lower uncertainty, both for the full history of the past 30 years of time series data and for the future. > "Through Solargis and GroundWork’s robust statistical processes and quality control measures, we are able to focus efforts on projects slated for success and financial viability." > > — Jolyon Dent, VP of Analytics ## Related products ### [Solargis Evaluate](https://solargis.com/products/evaluate) - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Unmatched level of detail and accuracy - PV simulation based on ray tracing and Perez all-weather sky model -------------------------------------------------------------------------------- ## Quality Control of Solar & Meteo Measurements | Solargis Source: https://solargis.com/services/quality-control-of-solar-radiation-meteo-measurements # Quality Control of Solar & Meteo Measurements Ground-based solar and meteo measurements play a vital role in the solar industry. They are used for adapting models and evaluating the performance of solar power plants. However, one of the key challenges of measured solar irradiance data is a high occurrence of anomalous values. Before ground-based measurements are used for performance assessment or for adaptation of modeled time series, all suspicious values in the measured time series must be identified and flagged through quality control procedures. Our Quality Control of Solar & Meteo Measurements service is based on Solargis experience handling measurements from thousands of locations globally. It helps you identify errors and prepare your datasets for the next steps of your project. ## 1. Identifying and flagging errors ### Equipment-related errors The magnitude of errors stemming from the cosine effect, temperature response, spectral sensitivity, stability, non-linearity, etc. depends on the quality of sensors and local conditions. So first, we review the technical specifications of sensors and their calibration certificates. ### Installation-related errors As a second step, we identify errors such as misalignment of sensors, shading by surrounding objects, etc., and flag the affected measurements. ### Operation-related errors In most cases, soiling of sensors (dust, snow, water droplets, frost, bird droppings, etc.) is difficult to prevent. However, we can identify these irregularities through thorough data analysis in [Solargis Analyst](https://solargis.com/products/analyst). ## 2. Gap filling and data harmonization ### Performance evaluation of solar power plants In addition to flagging and removing errors, we substitute missing and erroneous data with inputs from our models. With that, we can create a harmonized, complete, gap-free time series dataset ready to use in performance evaluation. ### Harmonizing the data In most cases, on-site data comes from several pyranometers. By harmonizing it, we replace erroneous values with modeled data and merge multiple datasets into one. Unlike simple averaging, which can degrade the original quality of data, our statistical multicriteria approach preserves the natural features of measurements. ## 3. Results ### Adjusting our models to the resolution of your measurements With our statistical approaches, we can adapt our model values to the same granularity as your measurements – for example disaggregating 15-minute measured data to 1-minute time series. ### Providing a statement of uncertainty We issue a statement of uncertainty for the datasets that have been quality controlled. This enables you to use the data for bankable performance assessment and site adaptation of satellite-modeled historical time series. > "Utilising data sets from Solargis has helped us improve our generation forecasting and project monitoring. Solargis has a genuine interest in maintaining the most accurate solar resource data sets." > > — Adarsh Das, Co-Founder & CEO ## Related products ### [Solargis Analyst](https://solargis.com/products/analyst) - Visualize complex and big solar datasets - Compare measured data to model outputs - Identify and clean errors from measurements - Harmonize multisource input streams - Streamline solar data management ### [Solargis Evaluate](https://solargis.com/products/evaluate) - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Unmatched level of detail and accuracy - PV simulation based on ray tracing and Perez all-weather sky model -------------------------------------------------------------------------------- ## Customized GIS Data | Solargis Source: https://solargis.com/services/customized-gis-data # Customized GIS Data Explore solar, meteorological, and PV potential GIS data in your own applications. You can use our solar resource, PV, climate, and other geo data (provided in GeoTIFF, NetCDF, and other formats) for analysis and visualization in all generally available GIS software with raster data processing capabilities. ### Market analysis in your own environment Integrate Solargis data with your software solutions to create proprietary maps and cartographic outputs. Run complex multi-parameter regional analysis to better understand the market conditions and select the most suitable site for the project. ### For geo data professionals Take Solargis GIS and array-oriented data and perform your own spatial analysis, develop your own algorithms, or simply add the data to your spatio-temporal data pool. You can use Solargis solar resource, PV, or climate raster data in GeoTIFF (for aggregated data) and NetCDF (for time series data) formats. ### Raster to vector Solargis data comes primarily as raster data. However, for certain applications, you might need the vector format. In such cases, we employ raster-to-vector transformation techniques and provide the data in standard GIS vector formats such as geopackage, shapefile, geojson, etc. ### [Free to download](https://solargis.com/resources/free-maps-and-gis-data) You can download the basic set of GIS data for free. If you need more detailed temporal or spatial data, advanced parameters, or further post-processing, we’ll find a customized solution. [Free maps](https://solargis.com/resources/free-maps-and-gis-data) ## Related products ### [Solargis Prospect](https://solargis.com/products/prospect) - Reliable and accurate solar data - Fast sites comparison - Comprehensive environmental overview - Analytics and solar power calculator - Collaboration and multilingual support -------------------------------------------------------------------------------- ## PV Energy Yield Assessment | Solargis Source: https://solargis.com/services/pv-energy-yield-assessment # PV Energy Yield Assessment For financial risk assessment, investors and developers require reports of expected energy production, including uncertainties, as well as related solar and meteorological data inputs. We offer an independent and impartial evaluation of PV energy yield with our proprietary simulation tools and models. These are based on 15+ years of experience working on large-scale PV power plants around the world. ### Low uncertainty of input data Solar resource and air temperature are the most significant contributors to the uncertainty of PV energy simulations. Solargis PV energy estimates are based on our own solar and meteorological Time Series, known for their rigorous validation and low uncertainty. ### Simulation in 1- to 15-minute time steps In contrast to the common practice of using hourly TMY data in PV yield simulation, we use sub-hourly time series of solar radiation and air temperature. Weather inputs with higher temporal resolution and a full history (more than 30 years) deliver higher accuracy and help you correctly evaluate atypical weather conditions. ### [Proprietary models](https://solargis.com/technology/expertise) For energy PV yield assessment, we use our proprietary simulation engine and algorithms. This enables us to provide you with detailed insights into energy losses, uncertainties, and PV plant design options. [Our technology](https://solargis.com/technology/expertise) ### [Integration of on-site measurements to reduce uncertainty](https://solargis.com/services/site-adaptation-of-solargis-models) If available, we integrate on-site measurements of solar and meteorological data for site adaptation of the Solargis model, delivering long-term time series with reduced uncertainty and, in turn, giving project stakeholders higher confidence. [Site Adaptation of Solargis Model](https://solargis.com/services/site-adaptation-of-solargis-models) > "At all 10 projects, Solargis irradiation data closely matched on-site measurements, giving First Solar and other project stakeholders full confidence in the accuracy of Solargis estimates." > > — Fadi Ferzli, Senior Manager, Technical Sales and Engineering ## Related products ### [Solargis Evaluate](https://solargis.com/products/evaluate) - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Unmatched level of detail and accuracy - PV simulation based on ray tracing and Perez all-weather sky model -------------------------------------------------------------------------------- ## PV Performance Assessment | Solargis Source: https://solargis.com/services/pv-performance-assessment # PV Performance Assessment Understanding how your PV power plant is performing against expectations, months or years after it starts operating, is an important part of optimizing your asset. Our PV Performance Assessment report performs this analysis and can also provide a revised and more accurate long-term PV energy yield estimate for the purposes of refinancing or new asset acquisition. ### Independently verified performance We compare the actual PV power production and performance ratio for a given period with simulated values for P50 (best estimate) and P90 (conservative estimate) scenarios. These simulated values are based on our satellite-derived irradiance and meteorological inputs for the comparison period and [our proprietary simulation tools and models](https://solargis.com/services/pv-energy-yield-assessment). ### Recommended steps for performance improvement We undertake a detailed inspection of the actual production data to identify reasons for underperformance. Where possible, we provide recommendations on what you can do to improve performance. ### Revised and more accurate long-term energy estimates Energy yield estimates in the pre-construction stage have higher uncertainties, both for weather inputs and estimation of losses. We analyze solar irradiance, meteorological, and production data from the power plant to improve the accuracy of solar and weather inputs and PV energy yield simulation settings. These adjustments help you reduce the uncertainty and inaccuracy of long-term energy statistics. ### Continued fine-tuning of energy losses We regularly analyze actual vs simulated PV production for large- and utility-scale PV power plants across the globe. This helps us fine-tune our models and more precisely estimate energy losses. ## Related products ### [Solargis Evaluate](https://solargis.com/products/evaluate) - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Unmatched level of detail and accuracy - PV simulation based on ray tracing and Perez all-weather sky model ### [Solargis Monitor](https://solargis.com/products/monitor) - One solar data source for all sites - Gap- and error-free solar radiation data - Minimize meteo station inputs uncertainty - Benchmark planned performance with reality - Near real-time PV output assessment -------------------------------------------------------------------------------- ## PV Variability & Storage Optimization Study | Solargis Source: https://solargis.com/services/pv-variability-and-grid-integration-study # PV Variability & Storage Optimization Study Due to high variability in cloud cover, PV power production fluctuates. This creates a challenge for grid operators who need to balance the electricity network. The PV Variability & Storage Optimization Study delivers statistical data and insights to project developers, which are needed to design and manage PV-plus-storage systems. The study provides the most realistic data on PV power generation for grid integration analysis. ### [Optimum sizing of PV and BESS systems](https://solargis.com/technology/expertise) To effectively design and operate a hybrid PV and battery energy storage system (BESS) plant, you need the right inputs integrated from multiple sources. High-resolution solar irradiance data, together with energy simulation and storage dispatch modeling solutions – all combined with forecasted energy outputs – will lead you to the optimum design of your hybrid system. [Simulation details](https://solargis.com/technology/expertise) ### Lower risk in power purchase agreements To meet the restrictions of off-takers and grid operators, PV plant owners need to understand PV energy output variability. The study will describe the pattern of variability and power fluctuations, as well as the smoothing capabilities of the storage system, allowing you to be more confident that your PV plant will perform according to your expectations. ### More profitable energy trading Smarter ways of managing energy trading on the spot markets have become necessary to make hybrid systems more competitive. The PV Variability & Storage Optimization Study provides you with insights to make business decisions that will match your energy production profile with energy demand. ## Related products ### [Solargis Evaluate](https://solargis.com/products/evaluate) - 15-minute Time Series and TMY data - More than 30 years of data history - 3D energy system designer - Unmatched level of detail and accuracy - PV simulation based on ray tracing and Perez all-weather sky model ### [Solargis Monitor](https://solargis.com/products/monitor) - One solar data source for all sites - Gap- and error-free solar radiation data - Minimize meteo station inputs uncertainty - Benchmark planned performance with reality - Near real-time PV output assessment -------------------------------------------------------------------------------- ## Regional Solar Energy Potential Study | Solargis Source: https://solargis.com/services/regional-solar-energy-potential-study # Regional Solar Energy Potential Study Before choosing the location for a solar power plant, you should understand the climate and solar resource of the region to ensure optimal placement. However a generic solar resource map may not show the complete picture. In the Regional Solar Energy Potential Study, we analyze not only solar resource information but also meteorological and geographical data. The analysis considers the uncertainty of resource estimates, intermittency and seasonal variability, extreme weather, and geographical limitations on the deployment of solar power plants. ### Regional variability management To identify regions with the highest solar potential, we use Solargis modeled solar radiation data as well as locally available ground measurements. We analyze both the short-term and seasonal variability of solar power production to help you understand how it matches demand. For example, the study identifies sites and regions where power generation variability is lower, resulting in more stable grids. Uncertainty of our solar resource estimates is quantified, enabling technically sound analysis. ### Geographical constraints and opportunities The geographical characteristics of a region or particular location create technical and environmental constraints or prerequisites for the development of solar power plants. We analyze parameters such as terrain, population, site accessibility, energy demand centers (industry, agriculture, services), electricity grid infrastructure, and road networks. ### [Modeled data combined with ground measurements](https://solargis.com/services/site-adaptation-of-solargis-models) Weather conditions define the operating environment for solar power plants. To identify the characteristics of the regional climate and improve accuracy, we use data both from meteorological models and local meteorological stations. [More about Site Adaptation service](https://solargis.com/services/site-adaptation-of-solargis-models) ### PV electricity potential Different solar technologies might work better in different places. Using in-house simulation algorithms, we calculate PV power generation potential for the specific region. PV electricity generation maps help you choose the most suitable technology. ### Deliverables - Digital maps and GIS data layers - Technical report - Workshop > "We had been in touch with several meteo providers and we found that Solargis provides the most representative estimates for each site." > > — Matteo Riccieri, Chief Operating Officer # SECTION: Resources & data -------------------------------------------------------------------------------- ## Free Solar Maps & GIS Data | High-Res Download | Solargis Source: https://solargis.com/resources/free-maps-and-gis-data # Solar resource maps & GIS data Access high-resolution static maps to visualize global solar potential. For site-specific prospecting and bankable project analysis, use our professional solutions. ## Solar resource maps of World The map and data products on this page are licensed under the Creative Commons Attribution license (CC BY-SA 4.0). You are free to download, share, adapt, use the maps but you must credit the source on the appropriate place as follows: ***Solar resource map **©** 2021 Solargis***. We also request you to provide a backlink to *https://solargis.com* website when appropriate. ### Direct Normal Irradiation ### Medium Size English PNG, 1.5 MB Español PNG, 1.5 MB Français PNG, 1.5 MB ### Poster Map English TIF, 122.3 MB Español TIF, 122.5 MB Français TIF, 122.6 MB ### Global Horizontal Irradiation ### Medium Size English PNG, 1.5 MB Español PNG, 1.5 MB Français PNG, 1.5 MB ### Poster Map English TIF, 123.2 MB Español TIF, 123.3 MB Français TIF, 123.4 MB ### Photovoltaic Electricity Potential ### Medium Size English PNG, 1.5 MB Español PNG, 1.5 MB Français PNG, 1.5 MB ### Poster Map English TIF, 127.4 MB Español TIF, 127.5 MB Français TIF, 127.6 MB ## GIS Data - DIF LTAy Avg Daily Totals GEOTIFF - ZIP, 2 GB - DNI LTAy Avg Daily Totals GEOTIFF - ZIP, 3.5 GB - ELE GEOTIFF - ZIP, 1 GB - GHI LTAy Avg Daily Totals GEOTIFF - ZIP, 2.5 GB - GTI LTAy Avg Daily Totals GEOTIFF - ZIP, 3.1 GB - OPTA LTAy GEOTIFF - ZIP, 5.9 MB - PVOUT LTAm Avg Daily Totals GEOTIFF - ZIP, 4.3 GB - PVOUT LTAy Avg Daily Totals GEOTIFF - ZIP, 345 MB - TEMP LTAy GEOTIFF - ZIP, 215.2 MB ## Related products and services ### [Solargis Prospect](https://solargis.com/products/prospect) - Reliable and accurate solar data - Fast sites comparison - Comprehensive environmental overview - Analytics and solar power calculator - Collaboration and multilingual support ### [Solargis Solarmaps](https://solargis.com/products/solarmaps) - Monthly weather variation compared to LTAs - Harmonized last month’s summaries of key PV performance indicators - Weather variability in geographical context - Effective communication with stakeholders - Independent 3rd party assessment of PV performance -------------------------------------------------------------------------------- ## Solargis blog | Leading insights on solar energy Source: https://solargis.com/resources/blog # Blog ## [Site adaptation: A way to improve confidence in solar yield estimates](https://solargis.com/resources/blog/best-practices/site-adaptation-improves-confidence-in-solar-yield-estimates) Depending on location and conditions, satellite-based solar resource data typically have uncertainty in the range of about 4–8%. With high-quality ground measurements and proper site adaptation, that uncertainty can often be reduced to around 3–5%. This reduction may look small but it is commercially meaningful for many utility-scale PV projects. ## [Product news: Soiling losses calculation now available in Solargis Evaluate](https://solargis.com/resources/blog/solargis-news/soiling-losses-calculation-in-solargis-evaluate) We’ve just added another layer of precision to Solargis Evaluate. Following the recent launch of snow loss calculations, soiling losses are now also part of our energy yield simulation toolkit – helping developers, investors, and technical advisors model real-world PV performance more accurately. ## [How PV yield uncertainty impacts project engineers, investors and banks](https://solargis.com/resources/blog/best-practices/how-pv-yield-uncertainty-impacts-project-engineers-investors-and-banks) Every solar PV project usually starts with one simple number: expected annual PV yield. At first glance, this number looks clear and precise. In reality, however, it always comes with uncertainty. How a project team deals with this uncertainty matters a lot. It influences engineering design, investor expectations, and bank financing, often in ways that only become visible late in the project lifecycle. ## [Product news: Solargis Evaluate 2.7 brings smart collision detection, cable design, and more](https://solargis.com/resources/blog/product-updates/solargis-evaluate-2-7-new-pv-design-and-analytics-features) Approximately one year ago, we launched Solargis Evaluate packed with features for complex site assessment, PV system design, energy yield simulation, analysis, and reporting, all under one roof. Our latest Evaluate 2.7 release is introducing major improvements in the PV system designer, data analysis, and bankable reports. Get to know the latest feature updates. ## [Prospect or Evaluate? Choose the right Solargis solution for your project stage](https://solargis.com/resources/blog/best-practices/prospect-or-evaluate-choose-the-right-solargis-solution-for-your-project-stage) Solargis Prospect and Solargis Evaluate are essential in the pre-feasibility and feasibility phases, but they serve different purposes. Many of our customers ask how the two products differ, how they fit into the project development process, and whether – or when – to transition from Prospect to Evaluate. In the article, we’ll walk you through the role of each solution and help you make the right product decision. ## [Solargis data helps unveil new insights into Europe’s solar radiation trends](https://solargis.com/resources/blog/solargis-news/solargis-data-unveil-new-insights-into-europe-solar-radiation-trends) A new peer-reviewed study, conducted in collaboration with Solargis experts and the researchers from the University of Murcia and the University of Málaga, powered by Solargis data, evaluates the impact of clouds and aerosols on surface solar radiation (SSR), identified as the two main drivers of long-term solar trends in Europe. ## [Product news: Hail forecasting now available for the US region](https://solargis.com/resources/blog/solargis-news/hail-forecasting-for-the-us) Great news for our North American clients. From now on, the Solargis Forecast platform provides early warnings for one of the most damaging weather hazards for solar power plants: Hail forecasting - crucial especially in regions with frequent severe storms. As the continental United States is one of the areas most affected by hail, we’ve launched this update for this region first, with plans to expand coverage in the near future. ## [One year of the new Solargis Evaluate: More features, more data, fewer limitations](https://solargis.com/resources/blog/solargis-news/one-year-of-the-new-solargis-evaluate) Twelve months have passed since we launched the new Solargis Evaluate platform. We are proud to say that since then, we have made numerous great product updates and Evaluate has grown to be an even more powerful solution for complete PV project design and evaluation. Learn about the most important updates and new features. ## [2025 solar resource overview in maps: A year of exceptional highs and lows](https://solargis.com/resources/blog/solargis-news/2025-solar-resource-overview-in-maps) As every year, we bring you a map summarizing last year’s global horizontal irradiation (GHI) anomalies, highlighting how much regional weather conditions diverged from long-term averages (LTA). These maps help project developers, asset managers, and investors understand how atmospheric variability may have influenced PV performance worldwide. ## [Unlimited 15-minute TMY P50 simulations now in every active Solargis Evaluate project](https://solargis.com/resources/blog/solargis-news/unlimited-15-minute-tmy-p50-simulations-in-solargis-evaluate) It’s been a year since we launched Solargis Evaluate 2.0, the platform that brings high-quality solar and meteorological data, system design, and advanced PV simulation together into one solution. Today, we’d like to share some good news. From now on, every active project in Solargis Evaluate includes unlimited TMY P50 PV simulations in 15-minute temporal resolution. ## [Solargis wins Best Poster Award at PVPMC 2025 in Cyprus](https://solargis.com/resources/blog/solargis-news/solargis-wins-best-poster-award-at-pvpmc-2025) The Solargis team recently joined the Photovoltaic Performance Modeling Collaborative (PVPMC) 2025 Workshop, held on 29–30 October in Ayia Napa, Cyprus. This year’s event brought together leading experts in PV performance modeling, system analytics, and solar energy integration, with a special focus on the challenges and solutions of island grids. ## [Look beyond GHI: Four solar resource maps from Solargis Prospect worth watching](https://solargis.com/resources/blog/best-practices/look-beyond-ghi-solar-resource-maps-from-solargis-prospect) When prospecting and evaluating solar projects, most attention goes to Global Horizontal Irradiation (GHI). It is the most widely used metric for solar resource assessment and the basis of nearly every PV feasibility study. But GHI is only one of the many parts of the picture. At Solargis, we go deeper. ## [How to calculate P90 (or other Pxx) PV energy yield estimates](https://solargis.com/resources/blog/best-practices/how-to-calculate-p90-or-other-pxx-pv-energy-yield-estimates) One of the most critical outputs from PV simulations is the P50 annual energy yield estimate. Often referred to as the "best estimate," the P50 value represents the annual energy yield that has a 50% probability of being exceeded (with an equal 50% chance that the actual yield will fall below it). — However, relying solely on the P50 value may be too optimistic for project stakeholders. To address this, additional probability-based yield estimates are commonly used e.g. P90 value, which indicates the energy yield expected to be exceeded 90% of the time. -------------------------------------------------------------------------------- ## Explore key resources | Solargis Source: https://solargis.com/resources # Resources ### [Solargis vs. alternatives](https://solargis.com/resources/solargis-vs-alternatives) See what sets Solargis Evaluate apart from alternatives on the market across solar resource data, PV simulation, and bankable yield assessment. [See how it compares](https://solargis.com/resources/solargis-vs-alternatives) ### [Blog](https://solargis.com/resources/blog) Industry best practices, Solargis news, and product updates – all in one place. ### [Webinars](https://solargis.com/resources/webinars) Learn how to use Solargis solutions in your everyday job or explore current solar industry topics and insights. ### [Ebooks & Whitepapers](https://solargis.com/resources/ebooks) Free to download, our ebooks and whitepapers dive deeper into trending or demanded themes of the solar industry. ### [Solargis in media](https://solargis.com/resources/solargis-in-media) A selection of articles and interviews featuring Solargis insights across global energy and solar industry media. [Read articles](https://solargis.com/resources/solargis-in-media) ### [Publications](https://solargis.com/resources/publications) Want to go more technical and scientific? Here you’ll find Solargis' scientific papers, research contributions, and presentations. [Browse Solargis' scientific outputs](https://solargis.com/resources/publications) ### [Free Maps & GIS Data](https://solargis.com/resources/free-maps-and-gis-data) The largest collection of free solar resource maps to help the solar industry with the development of their PV projects. [Download for free](https://solargis.com/resources/free-maps-and-gis-data) ### [Collaterals & Success stories](https://solargis.com/resources/collaterals) Explore and download a complete list of our collateral materials dedicated to various topics - products, technology, success stories and other. [Explore & download](https://solargis.com/resources/collaterals) -------------------------------------------------------------------------------- ## Ebooks | Solargis Resources Source: https://solargis.com/resources/ebooks # Ebooks & Whitepapers ## [Best practices for performance evaluation of PV power plants](https://solargis.com/resources/ebooks/best-practices-performance-evaluation-pv-plants) Accurate performance evaluation verifies design expectations, maximizes energy yield, and supports informed decision-making — yet its quality depends entirely on how well measurement campaigns are designed and maintained. ## [How to validate solar models using ground measurements](https://solargis.com/resources/ebooks/how-to-validate-solar-models-using-ground-measurements) Since there is effectively no updated independent study that compares all databases globally, validation statistics are one of the most important tools when comparing models from different providers. ## [How to use albedo for more accurate bifacial PV estimates](https://solargis.com/resources/ebooks/albedo-for-bifacial-pv-projects-whitepaper) The increased adoption of bifacial PV technology has put the spotlight on the importance of obtaining reliable and accurate values for this spatially and temporally variable parameter. ## [How to choose the right dataset](https://solargis.com/resources/ebooks/ebook-how-to-choose-solar-resource-data) Research shows that a sub-optimal design of a large-scale power plant leading to a 1% lower yield would result in lost profitability in the range of millions of dollars over the asset’s lifetime. -------------------------------------------------------------------------------- ## Webinars | Solargis Resources Source: https://solargis.com/resources/webinars # Webinars ## [Can you trust your Pxx? Why PV yield uncertainty matters for your project](https://solargis.com/resources/webinars/can-you-trust-your-pxx-why-pv-yield-uncertainty-matters-for-your-project) Most PV projects rely on a single P50 value to represent expected annual energy yield. But one number can’t tell the whole story and it certainly can’t capture the range of outcomes your project might face. Watch this webinar to see how Solargis calculates PV yield uncertainty backed by industry-leading solar and meteorological data, proven modeling approaches, and transparent methodology. ## [How site adaptation reduces your energy yield uncertainty](https://solargis.com/resources/webinars/how-site-adaptation-reduces-your-energy-yield-uncertainty) More accurate energy yield assessments start with closing the gap between satellite model data and ground measurements. Watch this webinar to learn how the site adaptation process works and what best practices for ground measurements can do for the accuracy of your energy yield assessments. ## [Discover unlimited possibilities with Solargis Evaluate](https://solargis.com/resources/webinars/discover-unlimited-possibilities-with-solargis-evaluate) Find out how to simplify solar project feasibility with Solargis Evaluate. Our team explains how Solargis TMY data supports reliable early-stage decisions, and highlights the key benefits of the Evaluate subscription. Watch this webinar to learn how to create unlimited PV designs and run unlimited number of energy yield simulations. ## [Meet Solargis Evaluate: from data to bankable reports](https://solargis.com/resources/webinars/meet-solargis-evaluate-from-data-to-bankable-reports-all-in-one-place) Are you still juggling multiple applications for PV evaluation? You don’t have to. During this webinar you will get a chance to see why Evaluate represents a new way of working; no more siloed tools, no more wasted effort. Just a single, trusted platform for confident solar project evaluation. ## [Impact of Extreme Weather Conditions on PV Projects](https://solargis.com/resources/webinars/impact-of-extreme-weather-conditions-on-pv-projects) Our expert team gives actionable tips on how to avoid the negative surprises of extreme weather and shares some of our customers' best practices. ## [Solargis Analyst New Features and Improvements (version 1.4.2)](https://solargis.com/resources/webinars/solargis-analyst-new-features-and-improvements-version-1-4-2) The new version of Solargis Analyst (version 1.4.2) includes an updated database structure that is crucial for all upcoming new features and parameters. ## [How satellite model data help with storage applications](https://solargis.com/resources/webinars/how-satellite-model-data-help-with-storage-applications) In this webinar, we will present how 1-minute granularity data can also help with optimal sizing of batteries and replacement strategy— even during the initial design phase. ## [Working with Solargis Analyst as a team](https://solargis.com/resources/webinars/working-with-solargis-analyst-as-a-team) In this webinar, we will show how to share datasets between the company with saved flags and export the data to make your workflow more efficient and productive. ## [Check consistency between solar radiation components](https://solargis.com/resources/webinars/check-consistency-between-solar-radiation-components) During this webinar, we will show how to run this type of analysis to help gain confidence in the performance values of solar power plants. ## [Calculation of hourly profiles, extreme values and probabilistic scenarios](https://solargis.com/resources/webinars/calculation-of-hourly-profiles-extreme-values-and-probabilistic-scenarios) Calculate different statistic values or probabilistic scenarios (P50, P75, P90…), explore typical daily profiles of solar resource data and see common weather patterns with the help of Solargis Analyst. ## [Harmonization of different data formats](https://solargis.com/resources/webinars/harmonization-of-different-data-formats) See how Solargis Analyst can help with data management in a very simple way, gathering the data in one place with a harmonized format. ## [Visualization and analysis of generation from different power plants](https://solargis.com/resources/webinars/visualization-and-analysis-of-generation-from-different-power-plants) During this webinar, we introduce another benefit of using Solargis Analyst: data comparisons between different renewable energy sources, e.g. generated by wind and solar power plants. ## [Easy visualization and comparison of large data streams](https://solargis.com/resources/webinars/easy-visualization-and-comparison-of-large-data-streams) How do 100.000 lines of GHI in the dataset look like? In this webinar you can learn how you can visualize multiyear datasets with various type of graphs with Solargis Analyst. -------------------------------------------------------------------------------- ## Success Stories | Solargis Resources Source: https://solargis.com/resources/success-stories # Success stories ## [candi solar optimizes portfolio performance with Solargis Monitor](https://solargis.com/resources/success-stories/candi-solar-solargis-monitor) "Solargis’ bankable and credible data underpins our decision-making throughout the project lifecycle." ## [candi solar turned to Solargis Evaluate to provide robust and accurate irradiation data](https://solargis.com/resources/success-stories/candi-solar) “We had a suspicion of overestimation in the solar resource and when we downloaded our first Solargis file, we saw it was 3-10% less than expected.” ## [Site adaptation of high resolution satellite data using ground-based measurements](https://solargis.com/resources/success-stories/site-adaptation-of-high-resolution-satellite-data-using-ground-based-measurements) Convergent turned to Solargis and GroundWork for highly accurate and site-specific resource assessments for its solar-plus-storage systems to better understand expected performance and maximize confidence for project stakeholders. ## [Shift Energy Japan uses Solargis Monitor to track and assess its projects’ financial performance](https://solargis.com/resources/success-stories/shift-energy-japan-uses-solargis-monitor-to-track-and-assess-its-projects-financial-performance) Shift Energy Japan uses Solargis Monitor as a replacement for the scarce, publicly available data to track and assess its projects’ financial performance and deliver key insights into operations. ## [Iberdrola rolls out Solargis Analyst software to support global solar operations](https://solargis.com/resources/success-stories/iberdrola-rolls-out-solargis-analyst-software-to-support-global-solar-operations) Solargis Analyst is helping Iberdrola make more informed investment and financial decisions, which are bolstered by secure and granular data. ## [Solargis provides data for Apex Clean Energy’s 10GW U.S. solar portfolio](https://solargis.com/resources/success-stories/apex-clean-energy) Apex Clean Energy has bolstered its bifacial projects with Solargis’ albedo data, and to optimize its storage assets, it has invested in 1- and 5- minute data provided by Solargis. ## [SunSource is able to confidently verify its own GHI and meteorological data](https://solargis.com/resources/success-stories/sunsource-energy) With greater insight into irradiance levels, SunSource Energy is able to confidently bid for contracts, while ensuring that its bifacial and storage assets are designed and operated to the highest standard. ## [Solargis assessment supports optimized asset value for First Solar](https://solargis.com/resources/success-stories/first-solar) At all 10 projects, Solargis irradiation data closely matched on-site measurements, giving First Solar and other project stakeholders full confidence in the accuracy of Solargis estimates. ## [Solargis validates ground-based measurements for Cleantech Solar](https://solargis.com/resources/success-stories/cleantech-solar) Solargis supported Cleantech Solar with API-based daily irradiation estimates, facilitating strategic decision making by the management to increase financial performance. -------------------------------------------------------------------------------- ## Publications | Solargis Resources Source: https://solargis.com/resources/publications # Publications Categories Scientific paper( 138 ) Presentation / Poster( 31 ) Pop-science( 6 ) Book( 1 ) Publication date 1 - 4 years( 27 ) 5 - 9 years( 16 ) 10+ years( 133 ) Topics Modelling( 111 ) Uncertainty( 16 ) Solar resource assessment( 96 ) Regional studies( 21 ) Solar atlases( 3 ) Intermitency analysis( 9 ) Photovoltaic (PV) electricity( 50 ) Bankability( 8 ) Quantifying the Impact of Quality Control on PV Applications Hulik Jansova M., Blstak Catlosova K., Camara A., Cebecauer T., Jakubik M., Osvald O. , 2025 , We quantify how retaining flawed measurements propagates errors into downstream applications and show the resulting inaccuracies across common data-quality issues. Download poster Operational scheme for detailed PV simulation results Michal Jun, Marek Minda, Branislav Schnierer, Jozef Rusnak , 2025 , This poster describes a detailed PV energy simulation methodology that uses sub-hourly time-series data and a digital twin concept. Download poster Impact of PV modules degradation on inverter clipping losses Jozef Rusnak, Branislav Schnierer , 2025 , This study analyzes the impact of PV module performance degradation on variability and amplitude of inverter clipping losses (CLs) occurring during the expected lifetime of a PV power plant. Download poster Past, current and future solar radiation trends in Europe: Multi-source assessment of the role of clouds and aerosols Leandro C. Segado-Moreno, José A. Ruiz-Arias, Juan Pedro Montávez, Juraj Betak , 2025 , Remote Sensing of Environment, Volume 333, Nov. 2025, doi: 10.1016/j.rse.2025.115122 Download article GISPLIT: High-performance global solar irradiance component-separation model dynamically constrained by 1-minute sky conditions José A. Ruiz-Arias, Christian A. Gueymard , 2024 , Solar Energy, vol. 269, p. 112363, Feb. 2024, doi: 10.1016/j.solener.2024.112363. Download article SPARTA: Solar parameterization for the radiative transfer of the cloudless atmosphere José A. Ruiz-Arias , 2023 , Renewable and Sustainable Energy Reviews, vol. 188, p. 113833, Dec. 2023, doi: 10.1016/j.rser.2023.113833. Download article CAELUS: Classification of sky conditions from 1-minute time series of global solar irradiance using variability indices and dynamic thresholds José A. Ruiz-Arias, Christian A. Gueymard , 2023 , Solar Energy, vol. 263, p. 111895, Oct. 2023, doi: 10.1016/j.solener.2023.111895. Download article Verification of Photovoltaic System Configuration O. Osvald, M. Garaj, A. Skoczek, and T. Cebecauer , 2023 , 40th EU PVSEC 2023, Lisbon, Portugal, Sep. 20, 2023. Assessing PV Energy Loss due to Snow with Meteorological Models Artur Skoczek, Oliver Osvald, Branislav Schnierer, Lubos Helienek, Tatiana Harcinikova , 2023 , 40th EU PVSEC 2023, Lisbon, Portugal, Sep. 18, 2023. [Online]. Download article Impact of Time Resolution of Solar and Meteorological Data on Clipping Losses and Energy Yield Simulation Jozef Rusnak, Branislav Schnierer, Marcel Suri, Martin Opatovsky, Giridaran Srinivasan , 2023 , Proceedings of the 40th European Photovoltaic Solar Energy Conference and Exhibition in Lisbon, Portugal, Lisbon, Portugal: WIP-Munich, Sep. 2023, pp. 020493-001-020493–010. doi: 10.4229/EUPVSEC2023/5CO.6.2. Download article Global High-Resolution Map of the Lowest Expected Operating Temperature for Optimum Sizing of PV Arrays Artur Skoczek, Juraj Betak, Jose Antonio Ruiz Arias, Jan Veres, Tomas Cebecauer , 2023 , 40th EU PVSEC 2023, Lisbon, Portugal, Sep. 18, 2023. [Online] Download article Multi-model approach for improved solar nowcasting Vos, J., Muskova K., Montserrat, J., Rosina, K., Ruiz-Arias, J. A., and Cebecauer, T. , 2023 , EUMETSAT Meteorological Satellite Conference 2023, Malmo, Sweden, Sep. 11, 2023. Download article Uncertainties in PV Power Simulation Chain Lubos Helienek; Jozef Rusnak; Branislav Schnierer; Martin Opatovsky; Lukas Dvonc; Vicente Lara Fanego; Artur Skoczek; Tomas Cebecauer , 2023 , 2023 IEEE 50th Photovoltaic Specialists Conference (PVSC), IEEE, Jun. 2023, pp. 1–6. doi: 10.1109/PVSC48320.2023.10359583. Download article Soiling Model for PV Applications: Improved Parameterizations Vicente Lara-Fanego; Christian A. Gueymard; Leonardo Micheli , 2023 , 2023 IEEE 50th Photovoltaic Specialists Conference (PVSC), IEEE, Jun. 2023, pp. 1–3. doi: 10.1109/PVSC48320.2023.10359694. Download article Solargis PV Components Catalogue M. Opatovsky, J. Rusnak, B. Schnierer, T. Sasko, M. Šúri , 2023 , 2023 16th PV Performance Modeling Workshop, Salt Lake City, May 10, 2023. [Online]. Download article Quality assessment of global tilted irradiance by automatic and manual procedures M. H. Jansova, M. Dzamova, M. Jakubik, and J. Dudzak , 2023 , 2023 16th PV Performance Modeling Workshop, Salt Lake City, May 10, 2023. [Online]. Download article Surface solar radiation trends over Europe assessed from ground-based measurements and satellite imagery and their comparison with climate models Leandro Cristian Segado-Moreno, José Antonio Ruiz-Arias, Juan Pedro Montávez , 2023 , EGU General Assembly 2023, Vienna, Austria: Copernicus Meetings, Apr. 2023. doi: 10.5194/egusphere-egu23-15554. Download article Mean-preserving interpolation with splines for solar radiation modeling José A. Ruiz-Arias , 2022 , Solar Energy, vol. 248, pp. 121–127, Dec. 2022, doi: 10.1016/j.solener.2022.10.038. Download article State-of-Play and Emerging Challenges in Photovoltaic Energy Yield Simulations: A Multi-Case Multi-Model Benchmarking Study Ioannis (John) A. Tsanakas, Ismaël Lokhat, Arnaud Jay, Clément Gregoire, Branislav Schnierer, Jozef Rusnak, Lukas Dvonc, Joséphine Berthelot, Chloé Monet, Kévin Garcia, Iván Lombardero, Jonathan Leloux, Babacar Sarr, Alfons Armbruster, Jefferson Bor , 2022 , Solar RRL, vol. 7, no. 8, p. 2200582, 2023, doi: 10.1002/solr.202200582. Download article Automated data integration of residential and commercial PV systems into DSO SCADA utilising IEC 61850 compliant comprehensive data model S. Chen; H. Lorenz; C. Kondzialka; B. Idlbi; K. Belkilani; G. Heilscher; J. Beták; J. Rusnák; B. Schnierer; M. Resch; G. Supper , 2022 , presented at the 21st Wind & Solar Integration Workshop, IET Digital Library, Oct. 2022, pp. 517–525. doi: 10.1049/icp.2022.2820. Download article -------------------------------------------------------------------------------- ## Solargis Evaluate vs. alternatives | Competitive comparison Source: https://solargis.com/resources/solargis-vs-alternatives # How Solargis Evaluate compares to alternatives [Solargis Evaluate](https://solargis.com/products/evaluate) is the only cloud-based platform that brings together solar and meteorological data, PV design, PV simulation, data analytics, a PV component catalog, and bankable reporting in one place. Other tools cover one or two of these areas; Solargis Evaluate connects all of them, so your data, models, and outputs stay consistent from first screening to financial close. Here’s how Solargis Evaluate compares across areas that matter most to solar developers, EPCs, and lenders: ## Solar, meteorological and environmental data [Solargis Evaluate](https://solargis.com/products/evaluate) combines the most extensively validated commercial irradiance dataset — [independently ranked #1 by the IEA PVPS](https://solargis.com/resources/blog/solargis-news/iea-worldwide-benchmark-highest-accuracy-solargis-model) — with the design and simulation tools that turn it into a bankable yield assessment. There’s no gap between the data you sourced and the report you submit. | | What dedicated data providers offer | What Solargis Evaluate delivers | | --- | --- | --- | | Data methodology | Some providers rely on empirical models built on statistical correlations — these can perform well in familiar regions but degrade in complex terrain or high-aerosol environments. Some mix datasets from multiple sources and convert monthly averages into synthetic hourly values, requiring manual tweaking to produce acceptable results. | Physics-based solar model — simulates how sunlight travels through the atmosphere accounting for clouds, aerosols, water vapor, and terrain. Consistent accuracy across all climates and geographies; results are fully traceable and reproducible. | | Historical record | Some providers cover 15–20 years of data. They explicitly exclude older satellite generations, citing lower resolution and geolocation quality, limiting their ability to capture long-term climate variability and interannual patterns. | 30+ years of validated solar and meteorological data: the longest commercially available record, built into the same platform as your simulation. | | Time resolution | Sub-hourly satellite data is available from some providers, but often from a shorter historical record. | 15-minute time series data as standard: 30+ years of history. 1-minute data available as an add-on. Data is directly fed into simulation without format conversion. | | Validation transparency | Providers typically publish headline accuracy statistics. Site locations, raw ground measurements, and quality control methodology are rarely disclosed for independent verification. | Full transparency: validation site coordinates, raw ground-measured data, quality control reports, and statistics are all publicly available. Every claim is independently verifiable. | | Validation breadth | Validation studies focus on irradiance accuracy and are usually limited to GHI across a defined set of stations. | Validated at 320 GHI sites and 235 DNI sites globally. In addition to that, separate validations of soiling, snow, and meteorological parameters across diverse climates are publicly available. | | Independent benchmark | Accuracy comparisons are typically self-published and based on the provider’s own selection of stations and methodology. | Ranked #1 in the IEA PVPS 2023 Worldwide Benchmark of Modeled Solar Irradiance Data for lowest average deviation metrics (IEA PVPS, 2023). The most comprehensive independent comparison across 10 global models, conducted by a neutral third party. | | Ground albedo | Most providers offer monthly albedo at ~1 km resolution, if at all. Some apply a single fixed reflectance value regardless of location or season. | Monthly ground albedo from MODIS satellite data at 1 km resolution — varies by location, season, and surface cover. Available daily time series at 0.5 km resolution for due diligence analysis. | | Uncertainty calculation | P90 scenarios often rely on assumed uncertainty values rather than systematic propagation. Where uncertainty is quantified, it’s typically a geographic or regional average. | Uncertainty (P90 and other Pxx) is calculated by combining three components: solar irradiance model, PV simulation, and interannual variability - using the root-sum-square method. Each is quantified separately from at least 10 years of historical data and derived site-specifically, not as a regional average. | | Integration with simulation | Data is delivered as a file for import into a separate simulation tool. Any mismatch between the data version used for simulation must be managed manually. | Data flows directly into simulation within the same platform. No additional imports and exports, no versioning risk. The dataset that powers your layout is the dataset that powers your energy yield report. | ## PV simulation The most widely used simulation tools were designed when projects were simpler and computing power was scarce. Hourly datasets, view factor shading, string-level mismatch, and expert-guessed loss inputs were reasonable approximations then. For modern utility-scale PV — bifacial modules, single-axis trackers, complex terrain and 25-year forecasts, they introduce systematic error that compounds over the project lifetime. [Solargis Evaluate](https://solargis.com/products/evaluate)'s simulation engine handles today’s utility-scale PV: bifacial modules, single-axis trackers, complex terrain, and validated loss models. **What dedicated PV simulation tools offer** **What Solargis Evaluate delivers** **Input data resolution** Hourly TMY data is the standard input: 8,760 data points representing a synthetic 'typical' year. It misses actual year-to-year variability, systematically underestimates risk exposure, and introduces error margins easily reaching 10%. [15-minute time series spanning 30+ years as default:](https://solargis.com/resources/blog/best-practices/why-tmy-data-is-not-enough-for-pv-simulations) more than 1,000,000 data points per parameter, 120x more than hourly TMY. Captures real historical variability including extreme weather events and short-term irradiance fluctuations. **Simulation speed** Detailed 3D simulation of large, terrain-complex projects on a desktop CPU takes hours. Running multiple design variants in a single session isn’t practical. Cloud GPU processing delivers a step change in simulation speed — large, terrain-complex projects that take hours on a desktop CPU are processed in cloud in a matter of minutes. **Shading model** View factor models are computationally fast but use simplified geometric approximations. Accuracy degrades for complex terrain, irregular layouts, and bifacial rear-side modeling. 3D Monte Carlo backward ray tracing: accurate near-shading simulation across any terrain complexity. Shading objects and nearby structures can be modeled directly in the Energy System Designer — obstacles that only physically rigorous ray tracing can handle correctly. **Bifacial rear irradiance** View factor models average rear irradiance uniformly using a fixed albedo — ignoring mounting hardware, terrain, and row geometry. Rear-side shading from frames and adjacent rows is simplified or ignored. Soiling and spectral correction are not applied to the rear side. [3D ray tracing calculates rear irradiance at cell level](https://solargis.com/resources/blog/best-practices/tackling-inaccuracies-pv-performance-predictions-bifacial-solar-systems) on both front and rear sides — accounting for mounting hardware, row structure, terrain geometry, and inter-row shadows. Every obstruction that reduces rear-side gain is modeled explicitly. **Ground albedo for bifacial** A single fixed albedo value (typically 0.20) is applied uniformly across the site throughout the year. Bifacial gain is highly sensitive to albedo; a static estimate introduces systematic error at sites with seasonal snow cover or varying vegetation. Monthly ground albedo from satellite captures snow cover, vegetation cycles, and surface type. Fed directly into Monte Carlo rear-side ray tracing for bifacial systems, ensuring consistency between what you designed and what gets simulated. **Tracker–bifacial interaction** Backtracking algorithms minimize front-side inter-row shading. The influence of tilt angle on rear-side shadow patterns and bifacial gain is not factored into tracker optimization. Simulation links tracker tilt angle, rear-side shading geometry, and ground albedo — providing an accurate bifacial energy model for single-axis tracker configurations across the full annual cycle. **Electrical mismatch** String-level mismatch models are the norm. Where cell-level simulation is available, it is typically restricted to projects under 5 MWp and degrades in performance at larger scales — making it impractical for the utility-scale projects. Cell-level IV curve aggregation using the De Soto single diode model — detailed mismatch simulation for any project size. **Soiling losses** Most tools rely on a user-entered percentage. Industry surveys show 62% of practitioners use an expert guess for soiling and snow losses (SERENDI-PV, 2023). Physics-based soiling model driven by satellite-derived atmospheric inputs (PM2.5, PM10, dust, precipitation, and local climate conditions) validated at 51 sites globally. Mean bias +0.1%, standard deviation 0.9%. **Snow losses** Rarely modeled explicitly. When included, it’s usually a fixed percentage based on the user’s judgment. Snow loss model validated at 27 sites. Included as standard in the simulation chain. ## Bankability A bankable yield assessment isn’t just a number — it’s a methodology lenders can follow from first principles to final output. One desktop tool dominates the market, accepted as the de facto standard through industry habit, while specialist data providers underpin the resource side of the study. Both leave gaps. [Solargis Evaluate](https://solargis.com/products/evaluate) closes both gaps in one platform. **What standalone tools offer** **What Solargis Evaluate delivers** **Basis for acceptance** Desktop simulation tools are accepted by lenders and technical advisors largely because of their ubiquity. That acceptance is built on familiarity, not on independent validation of the underlying methodology or its accuracy. Solargis provides bankable data accepted by banks, investors, and other stakeholders. Our algorithms are based on real-world physics, grounded in [peer-reviewed scientific literature](https://solargis.com/resources/publications) and built on transparent, traceable, and validated models. Our scientific, rigorously documented approach leaves no room for doubts in any simulation step. **Audit trail** When data, simulation, and reporting live in separate tools, the audit trail must be reconstructed manually from exported files and saved inputs. Lenders and technical advisors increasingly flag these gaps under scrutiny. From raw satellite data to bankable reports, everything runs in one integrated system. The audit trail is unbroken: no file handoffs, no version mismatches and no gaps to explain. **Software version traceability** Desktop simulation tools are version-dependent. Studies run on different software versions produce different results, with no central record of which version generated which output. Reproducing a result months later requires discipline the workflow doesn’t enforce. Cloud platform with a single maintained version. Every simulation is logged with the software state at the time it ran — results are reproducible and auditable across projects, teams, and time. **Data origin** Resource data is sourced separately: imported from a TMY file or third-party dataset and then used as simulation input. The connection between the source data and the final yield figure depends on the analyst maintaining the link manually. Your simulation runs on data produced by the same organization that built it. Solargis owns the full chain: from satellite data to simulation output. **Independent data recognition** Data providers typically back their accuracy claims with self-published comparisons or third-party validation studies of their own selection. Desktop tools rely on the user to source and validate their own input data. [Ranked #1 in the IEA PVPS 2023 benchmark](https://solargis.com/resources/blog/solargis-news/iea-worldwide-benchmark-highest-accuracy-solargis-model) for lowest average deviation metrics (IEA PVPS, 2023). [320 GHI validation sites](https://solargis.com/technology/accuracy-and-validation), and publicly available QC reports give reviewers a methodology they can verify. **Validated loss models** Soiling, snow, and degradation inputs typically rely on the user’s judgment or flat-percentage assumptions — regardless of whether the simulation engine is a desktop tool or a data provider’s add-on. These are exactly the assumptions technical advisors challenge most frequently. Data-driven, validated models for key losses: soiling loss model validated at 51 sites globally and snow loss model validated at 27 sites across the US and Europe. **Track record** Desktop simulation tools have broad market acceptance but limited transparency about how their models have evolved. Data-focused providers have credibility in resource assessment but limited history as end-to-end yield modeling platforms. [On the market since 2010](https://solargis.com/about/about-us); 16 years of continuous R&D. 9,000+ utility-scale projects supported annually across 100+ countries. Accepted and trusted by banks, investors, and other market stakeholders since day one as a basis for bankable yield assessments. **Transparency** Simulation methodology details are often limited or require specialist knowledge to interpret. Validation statistics may be published, but underlying data (site coordinates, raw measurements, QC results) is rarely disclosed for independent verification. Full transparency: every model is documented in the public knowledge base, and every validation claim is backed by publicly available site coordinates, raw ground-measured data, and QC reports. Any lender or technical advisor can verify independently. *This comparison is based on publicly available information as of April 2026.* # SECTION: Best practices & technical articles -------------------------------------------------------------------------------- ## How to calculate P90 and other Pxx energy yield estimates Source: https://solargis.com/resources/blog/best-practices/how-to-calculate-p90-or-other-pxx-pv-energy-yield-estimates # How to calculate P90 (and other Pxx) PV energy yield estimates Sep 24, 2025 ## What is P90 and why does it matter? One of the most critical outputs from [PV simulations](https://solargis.com/products/evaluate) is the P50 annual energy yield estimate. Often referred to as the "best estimate," the P50 value represents the annual energy yield that has a 50% probability of being exceeded (with an equal 50% chance that the actual yield will fall below it). However, relying solely on the P50 value may be too optimistic for project stakeholders. To address this, additional probability-based yield estimates are commonly used e.g. P90 value, which indicates the energy yield expected to be exceeded 90% of the time. This helps provide a broader picture of [expected project returns](https://solargis.com/solutions/energy-yield-simulation). In this article, we will explain how to calculate these probabilistic energy yield scenarios and how different ways of calculation impact the final result. ## Normal probability distribution Since representative datasets spanning very long time periods are often unavailable, it is common practice to assume that annual PV yield follows a normal (or Gaussian) distribution for simplification. In a normal distribution, the center of the curve is represented by the mean, while the width of the curve is defined by the standard deviation (σ or *stdev*), which quantifies the uncertainty or variability in energy yield estimates. Let’s now situate the most common probability scenarios within the same normal curve: - The **P50 value** represents the mean of the distribution. It is the most probable single value and divides the probability distribution equally. There is a 50% chance the actual yield will exceed this value, and a 50% chance that the actual yield will fall below it. - The **P75 value**, which lies between P50 and P90, represents the yield expected to be exceeded in 75% of cases. - The **P90 value** lies to the left of the mean and represents a more conservative estimate. It corresponds to the yield that is expected to be exceeded in 90% of the cases, with a 10% chance of falling below this value. P90 is perhaps the most commonly used scenario in PV yield assessments. - The **P99 value** lies further to the left, representing the yield that is exceeded in 99% of cases. These values can be generalized as Pxx estimates, where “xx” indicates the probability of exceedance as a percentage. By positioning these estimates within a normal distribution curve, one can visualize the range of likely outcomes and better understand the risk associated with each. — Fig. 1: P50, P75, P90, and P99 values represented in a normal distribution. Although they may appear related, Pxx scenarios in PV yield estimates should not be confused with statistical percentiles. In statistics, the p90 or 90th percentile is the value below which 90% of a dataset falls. In contrast, a P90 estimate in PV yield assessments represents a yield level that is expected to be exceeded with 90% probability. ## Confidence intervals One of the key advantages of assuming a normal distribution is the ability to easily derive one Pxx scenario from another using straightforward mathematical relationships. In all normal distributions, one standard deviation from the mean captures approximately 68.3% of the distribution. This statistical property allows us to calculate exceedance probabilities, such as P90, using simple multipliers. For example, the P90 yield can be estimated by subtracting 1.282 times the standard deviation from the P50 value. This multiplier corresponds to a 90% probability of exceedance. — Fig. 2: Uncertainty intervals, expressed at standard deviation and 80% confidence levels (P90 exceedance) Tab. 1: Calculation of different Pxx from a normal distribution of probability. **Probability of occurrence** **Formula** One standard deviation 68.3% ± STDEV Two standard deviations 95.5% ± 2*STDEV Three standard deviations 99.7% ± 3*STDEV P75 uncertainty 50% ± 0.675*STDEV P90 uncertainty 80% ± 1.282*STDEV P95 uncertainty 90% ± 1.645*STDEV P97.5 uncertainty 95% ± 1.960*STDEV P99 uncertainty 98% ± 2.326*STDEV In general, any Pxx value—such as P75 or P99—can be calculated from the P50 estimate using the appropriate multiplier from the Gaussian (normal) distribution. Tab. 2: Calculation of different Pxx exceedance values for a normal distribution of probability. **Probability of exceedance** **Probability of non-excedance** **Formula** P50 value 50% 50% Mean P75 value 75% 25% Mean - 0.675*STDEV P90 value 90% 10% Mean - 1.282*STDEV P95 value 95% 5% Mean - 1.645*STDEV P97.5 value 97.5% 2.5% Mean - 1.960*STDEV P99 value 99% 1% Mean - 2.326*STDEV ## Aggregation of uncertainty and variability factors The [total uncertainty](https://solargis.com/resources/blog/best-practices/being-certain-about-solar-radiation-uncertainty) associated with a yield estimate should account for all relevant contributing factors, each expressed at the same probability of exceedance. This requires aggregating various sources of uncertainty into a single combined value and understanding how these sources interact and influence one another. In addition to assuming that PV yield follows a normal (Gaussian) distribution, it is typically assumed that the individual sources of uncertainty are independent. This enables the use of the root-sum-square (RSS) method to calculate the total uncertainty, as follows: $$U_{\text{total}}= \sqrt{U_1^2 + U_2^2 + \ldots + U_n^2}$$ In the case of PV yield assessments, there are three main factors or components we take into account when calculating the total uncertainty of the annual PV yield: - **Uncertainty of solar irradiance models.** Solar irradiance model uncertainty typically refers to annual GHI (Global Horizontal Irradiance) estimates. Model uncertainty typically includes limitations in satellite-based data (this applies to satellite and validation discrepancies with ground measurements. - **Uncertainty of PV simulation.** Uncertainty also arises from the PV simulation models themselves, which estimate energy yield. Common contributing factors include modeling assumptions and limitations of user-inputs of different nature. - **Interannual variability.** Year-to-year variability of weather conditions (related with solar radiation but also with temperature and other meteorological parameters) introduces natural fluctuations in energy production. This interannual variability is quantified as the standard deviation of annual values in a multi-year time series. To characterize this properly at least 10 years of high-quality historical data is recommended. All these contributing factors are combined in a total uncertainty Utotal in a quadratic sum following the previously mentioned root-sum-square (RSS) method: $$U_{\text{total}} = \sqrt{U_{\text{model}}^2 + U_{\text{simulation}}^2 + U_{\text{interannual}}^2}$$ ## Type of input datasets In solar energy simulations, it is common to use [different types of datasets](https://solargis.com/resources/blog/best-practices/from-time-series-to-tmy-when-to-use-each) to estimate expected yield. The choice can be limited to the software that is being used for the output simulation. Below is an overview of the most commonly used dataset types, along with descriptions and sample data formats. ### Time Series The [historical time series dataset](https://solargis.com/products/evaluate/data-specs) includes all available data from the earliest year (1994, 1999, or 2007, depending on the location) up to the present. Advantages: - Most comprehensive representation of weather patterns Limitations: - Larger file sizes and longer computation times - Limited support in old PV simulation tools Best Use: Ideal for high-fidelity energy yield simulations and long-term variability assessments. Provides the most realistic input for Pxx scenario modeling. Download Sample 15-minute Time Series Data (ZIP, 27.3 MB) ### TMY P50 The TMY P50 dataset represents the typical climate conditions for each month, selected from the historical dataset. These "typical" months are joined together to form a synthetic year. The dataset is designed to represent a “standard” or most probable year. Advantages: - Small file size and faster simulation speed - Common format supported by most PV simulation software, including old ones Limitations: - Compresses the full time series into a simplified file - Excludes extreme or atypical weather events Best Use: Widely used for baseline (P50) simulations in early-stage project development. Download Sample 60-minute TMY P50 Data (ZIP, 0.3 MB) ### TMY P90 The TMY P90 dataset represents a conservative year with below-average solar resource conditions. It is built by recombination of monthly data from different years to ensure that the annual GHI is close to the P90 value. This dataset is suitable for simulating conservative yield scenarios. Advantages: - Small file size and faster simulation speed - Common format supported by most PV simulation software, including old ones - Practical for conservative energy yield estimates Limitations: - Include atypical weather patterns that may not represent long-term conditions Best Use: Simulations in early-stage project development that require conservative projections. ***Note:** In the sample calculation included in this article, we use hourly Typical Meteorological Year (TMY) datasets derived from original time series data. This involves generating a single year of hourly data (8,760 values) from more than 1 million data points.* *This should not be confused with simplified methods that also generate a year of hourly data, but using synthetic generators based on monthly long-term averages (LTA), represented by only 12 values.* *Synthetic TMY approaches are less accurate, as they introduce artificial variability that does not reflect real conditions, potentially leading to errors of up to 10%. Additionally, they often lack internal consistency between key variables such as GHI and temperature, which can result in unrealistic scenarios (e.g., low GHI paired with high temperatures), ultimately causing underestimation of PV output.* *You can learn more about this in this [other article](https://solargis.com/resources/blog/best-practices/why-tmy-data-is-not-enough-for-pv-simulations).* ## Calculation of interannual variability The variability of energy yield (the third component in the overall uncertainty calculation) is computed using the following formula: $$\text{var}_n = \frac{\text{stdev}}{\sqrt{n}}​$$ Where: - *stdev* is the standard deviation of annual energy yield time series - *n* is the number of years over which the variability is assessed Since PV yield estimates are typically expressed as annual values, in this article we are always using n = 1, which reflects the variability expected in any single year. This is the standard assumption for P90 energy yield calculations. — For longer-term projections, such as over 10, 20, or 25 years, the value of n increases accordingly (e.g., n = 10, 20, 25). In these cases, the overall uncertainty decreases, because the effects of interannual variability become less significant when averaged over longer periods. The interannual variability value used for the P90 estimate is typically derived from the annual energy yield (PVOUT) based on historical time series data. However, when TMY datasets are used in simulations, interannual variability is often simplified and instead based on historical annual global horizontal irradiance (GHI). While PVOUT and GHI generally follow similar trends, related interannual variability calculations do not produce identical results. This is because GHI does not capture various dynamic factors involved in converting irradiance into electricity, such as system performance, temperature effects, and other losses. — Fig. 3. Representation of annual time series sums for GHI and PVOUT. Source: Evaluate 2.0 ## Sample Calculation ### Project Details - Installed capacity: 1 kWp - Technology: Crystalline silicon (c-Si) - Inverter efficiency: 97.5% - DC losses: 2.5% - AC losses: 1.5% - Relative row spacing: 2.5 - Degradation: Not considered (first-year production only) - Location: Plataforma Solar de Almería (37.094416,-2.35985) ### Input Datasets - Time Series Dataset: subhourly satellite-based solar irradiance data spanning 1994–2024 with additional meteorological parameters required for the simulation. - TMY P50 Dataset: Created by selecting and concatenating typical months from the time series. - TMY P90 Dataset: Created by recombining representative monthly data from different years to represent a P90 scenario. ### Uncertainty Parameters - Irradiance model uncertainty (GHI): ±3.5% (P90 confidence level) - PV simulation uncertainty: ±5% (P90 confidence level) - Interannual variability: Calculated from PVOUT annual values (when using time series dataset) o GHI annual values (when using TMY datasets). ### Calculations **1. **PVOUT P90 from Full Historical Time Series - Simulate PV output using the full historical time series. - Derive the P50 value as the average of annual PVOUT values. - Compute total uncertainty: - Uncertainty in annual GHI - PV simulation uncertainty - Interannual variability derived from PVOUT time series - Derive the P90 value by applying total uncertainty to the P50 value. **2. **PVOUT P90 from TMY P50 Dataset - Simulate PV output using the TMY P50 dataset (single year). - Calculate P50 value as the annual total of PVOUT. - Compute total uncertainty: - Uncertainty in annual GHI - PV simulation uncertainty - Interannual variability based on the GHI time series used to create the TMY - Derive the P90 value by applying total uncertainty to the P50 result. **3. **PVOUT P90 from TMY P90 Dataset - Simulate PV output using the TMY P90 dataset (single year). - The resulting annual PVOUT is already representative of P90 conditions. - Apply only PV simulation uncertainty to obtain the final P90 value. Fig. 4. Diagram showing calculation process of PVOUT P90 from Time Series, TMY P50 and TMY P90 datasets ## Results The findings are summarized in the table below: - Compared to the time series simulation (the most complete and accurate method), using TMY P50 led to a 1% overestimation of the P90 energy yield. - The TMY P90 approach resulted in a 4% underestimation of the P90 energy yield. ** **Tab. 3. Summary of results from a sample calculation for a project located in Almería, Spain. Please note that these outcomes are specific to the site’s characteristics and system layout, and trends may vary for projects in different locations or with different configurations. All uncertainties in the table are expressed at P90 confidence level. **Simulation with TS** **Simulation with TMY P50** **Simulation with TMY P90** P50 annual value 1705 kWh/kWp 1716 kWh/ kWp 1606 kWh/ kWp Uncertainty factors Solar radiation model ±3.5% ±3.5% included in TMY P90 Energy simulation model ±5% ±5% ±5% Interannual variability ±3.2% ±2.6% included in TMY P90 Total uncertainty ±6.9% ±6.6% ±5% P90 anual value 1588 kWh/kWp 1602 kWh/ kWp 1526 kWh/ kWp ## Conclusions Accurately estimating expected irradiance under conservative scenarios (e.g., P75, P90, P99, etc) is essential for the successful development and financing of photovoltaic (PV) projects. [Reliable Pxx energy yield assessments](https://solargis.com/services/pv-energy-yield-assessment) must account for several key sources of uncertainty, including the quality of the solar irradiance data, the[accuracy of PV simulation](https://solargis.com/technology/accuracy-and-validation) models, and the interannual climate variability specific to the project site. Although it can be expressed using different confidence intervals, uncertainty is a fixed value specific to a given PV project determined by considering various contributing factors. Any change in the level of uncertainty reflects a change in one or more of these underlying factors. The choice of dataset also has a critical impact on results. Even when based on the same underlying time series, using typical meteorological year (TMY) datasets can introduce notable deviations in P90 yield estimates. These differences arise from the information loss inherent in generating TMY datasets and also from the way uncertainty is treated in the calculation. ## Need a bankable Pxx estimate? Solargis Evaluate gives you the data, simulation tools, and uncertainty quantification to produce audit-ready yield assessments. [Explore Evaluate ->](https://solargis.com/products/evaluate) If you prefer expert-led assessment, our team delivers independent PV energy yield assessments with full uncertainty quantification. [Learn about the service ->](https://solargis.com/services/pv-energy-yield-assessment) ### Further reading - https://kb.solargis.com/docs/methodology - https://kb.solargis.com/docs/accuracy-validation - Suri M., Cebecauer T., 2014. Satellite-based solar resource data: Model validation statistics versus user’s uncertainty. ASES SOLAR 2014 Conference, San Francisco. Available at https://solargis2-web-assets.s3.eu-west-1.amazonaws.com/public/publication/2014/1f0b376723/Suri-Cebecauer-ASES-Solar2014-Satellite-Based-Solar-Resource-Data-Model-Validation-Statistics-Versus-User-Uncertainty.pdf - Cebecauer T., Suri M., 2015. Typical Meteorological Year Data: Solargis Approach. Energy Procedia 69, 1958-1969. Available at https://doi.org/10.1016/j.egypro.2015.03.195 Pablo Caballero — Technical Writer Pablo is an industrial engineer with extensive experience in the renewable energy and software development sectors. He specializes in technical writing and content marketing and is driven by a passion for bridging gaps between audiences, technology, and business. With a diverse background, he is dedicated to promoting sustainability ideas and education in STEM fields. ## Keep reading ## [Being certain about solar radiation uncertainty](https://solargis.com/resources/blog/best-practices/being-certain-about-solar-radiation-uncertainty) In the context of PV yield simulation, uncertainty helps users understand the potential deviations in the results produced by the software they are using. Understanding these deviations plays a key role in selecting the optimal design of a power plant and in evaluating financial risks and return on investment. ## [The pros and cons of 1-minute, 15-minute, and 60-minute solar data](https://solargis.com/resources/blog/best-practices/the-pros-and-cons-of-1-minute-15-minute-and-60-minute-solar-data) Depending on the source and desired application, solar data can have distinctive temporal resolutions, such as sub-hourly (1-, 2-, 5-, 10-, 15-, 30-minute) or hourly intervals. But how are you supposed to know the difference, and why should you care? ## [How to choose the right dataset](https://solargis.com/resources/ebooks/ebook-how-to-choose-solar-resource-data) Research shows that a sub-optimal design of a large-scale power plant leading to a 1% lower yield would result in lost profitability in the range of millions of dollars over the asset’s lifetime. -------------------------------------------------------------------------------- ## The Pros and Cons of 1-Minute, 15- and 60-Minute Solar Data Source: https://solargis.com/resources/blog/best-practices/the-pros-and-cons-of-1-minute-15-minute-and-60-minute-solar-data # The pros and cons of 1-minute, 15-minute, and 60-minute solar data Sep 21, 2023 Solar data can be gathered in several ways, including satellites, ground-based sensors, weather models, or historical databases. Depending on the source and desired application, this data can have distinctive temporal resolutions, such as sub-hourly (1-, 2-, 5-, 10-, 15-, 30-minute) or hourly intervals. But how are you supposed to know the difference, and why should you care? ## **Why does temporal resolution matter?** The temporal resolution of solar data can affect simulation results relating to how accurately it reflects the variability of solar irradiance, the complexity of the simulated scene and how useful it is for specific purposes. When deciding on the most appropriate data, it’s useful to consider what you’re wanting to achieve. For example, if you want to [design a solar installation or estimate its energy output](https://solargis.com/products/evaluate), you’ll want to know how much sunlight it is likely to receive throughout the year. For this, you can use: - Historical monthly long-term averages, if early site selection is being performed, or if only a rough pre-feasibility study is prepared, - Typical meteorological year (TMY) data, which represents the typical (average) weather patterns at a site, which can then be integrated into a pre-feasibility study, - Time-series data with sub-hourly temporal resolution (typically 10- or 15-minute data), covering a long period of time (for bankable studies at least 10 years of data is expected) to accurately capture occurred weather patterns. Furthermore,** if you want to operate or [monitor a solar system in real time](https://solargis.com/products/monitor) or [forecast its output for the near future](https://solargis.com/solutions/power-output-forecast), you need to know how much sunlight it will receive in the coming minutes or hours. ** For this, **you need nowcast or forecast data that has a high temporal resolution (1-, 2- or 5-minute intervals)** but covers a short period of time (such as 24 hours). This way you can capture the rapid and unpredictable changes of solar irradiance due to clouds or other factors. ## **What are the advantages and disadvantages of each temporal resolution?** Each temporal resolution has its own pros and cons depending on the source and application. ***Figure 1:** Differences in details covered by 60- 15- and 1-minute data* ### 60-minute solar data Hourly data intervals are still widely used, persisting from the early stages of the data industry. Originally, data collection, processing systems, and PV simulation tools were designed to gather, export and model datasets in hourly intervals, typically across a period of one year. For this purpose, the concept of TMY was introduced to represent the typical weather conditions at a given site. Now in an age of significant digital advancements we can move beyond hourly (and TMY) approaches. Instead, modern technologies offer better and faster simulations with high quality data at shorter (sub-hourly) time intervals. Nonetheless, if they are used, a coarse representation of the average solar irradiance and other weather variables over a longer time scale should be expected. This may be useful for applications that only require **lower accuracy and precision such as**: - Assessing the feasibility and potential of solar projects - Evaluating the **long-term** performance and profitability of solar investments - Studying the trends and patterns of solar resources ### 15-minute solar data This captures the variability of solar irradiance and other weather conditions over a shorter time scale than above. 15-minute solar data is useful for applications that require **high accuracy and precision**, such as: - Detailed assessment of the potential of solar projects - [Detailed evaluation of the performance of solar assets](https://solargis.com/products/monitor) - Planning and scheduling solar power generation - Optimizing the operation and maintenance of solar systems - [Forecasting solar power output and demand](https://solargis.com/solutions/power-output-forecast) - Integrating solar energy into the grid and markets ### 1-minute solar data This is the highest resolution available for solar data analysis, as covered by our first blog, “What is 1-minute data?”. It provides detailed information on the fluctuations of solar irradiance and other weather variables across a very short time scale. This can help with applications that require an **analysis of short-term effects, **such as: - Monitoring and controlling solar power plants - Evaluating the behavior of PV components and systems during high cloud variability (clipping losses in inverters, variability smoothing) - Studying the impact of clouds, aerosols, and shading on solar output - Integration of energy storage systems (batteries) - Developing and testing new technologies and algorithms for solar energy ## **Advantages of 1-minute compared to 15-minute and 60-minute temporal resolutions** Since it is the highest resolution, **1-minute solar data has several advantages compared to 15-minute and 60-minute data.** These include: - Providing more accurate and precise information on the variability and quality of solar irradiance and other weather variables - Enabling more reliable and efficient calculation of solar power output and performance metrics - An accurate reflection of current weather conditions and events which may affect solar systems - Providing increased information for optimal control and management of solar systems and grids **The quality of this high resolution information affects the feasibility, performance, and profitability of your project.** Having access to high quality solar data is essential in reducing risks and maximizing returns of solar energy investments. Solargis’ solar data is the highest quality, most accurate and reliable in the market, based on independent comparisons and multiple independent studies. [To find out more about how we can support you with your solar data needs, please get in touch here.](https://solargis.com/contact-us) To read other editions of our 1-minute data blog series, please click on the links below. - [Solargis’ approach to 1-minute data](https://solargis.com/resources/blog/best-practices/solargis-approach-to-1-minute-data) - [4 reasons why PV project designers need 1-minute data](https://solargis.com/resources/blog/best-practices/4-reasons-why-pv-project-designers-need-1-minute-data) - [Why is 1-minute data essential for solar project financiers?](https://solargis.com/resources/blog/best-practices/why-is-1-minute-data-essential-for-solar-project-financiers) Branislav Schnierer — Head of Consultancy Branislav received MSc in electronics at the Faculty of electrical engineering at the Slovak University of Technologies, in Bratislava, Slovakia. Since 2010 he has been working as a support for energy auditors and bank supervision for photovoltaic power plants, focusing on energy audits, on-site photovoltaic power plant inspection during construction and performance guarantee measurements. At Solargis since 2012, he is leading the team providing photovoltaic power plants consultancy and customer support and helping with development of a new photovoltaic power plant simulation software. Branislav also developed and is currently managing two solar and meteorological measurement stations. ## Keep reading ## [How to calculate P90 (or other Pxx) PV energy yield estimates](https://solargis.com/resources/blog/best-practices/how-to-calculate-p90-or-other-pxx-pv-energy-yield-estimates) One of the most critical outputs from PV simulations is the P50 annual energy yield estimate. Often referred to as the "best estimate," the P50 value represents the annual energy yield that has a 50% probability of being exceeded (with an equal 50% chance that the actual yield will fall below it). — However, relying solely on the P50 value may be too optimistic for project stakeholders. To address this, additional probability-based yield estimates are commonly used e.g. P90 value, which indicates the energy yield expected to be exceeded 90% of the time. ## [From Time Series to TMY: When to use each?](https://solargis.com/resources/blog/best-practices/from-time-series-to-tmy-when-to-use-each) All Solar industry players need to simulate their power plant designs and financial plans at some point. To do so against summarized conditions given by data products like Typical Meteorological Years has been common until recently. However, running energy simulations using more realistic conditions described by Multi-Year Time Series of data is recommended to reduce project risk and evaluate all scenarios. ## [Solar data should be based on physics, not assumptions](https://solargis.com/resources/blog/best-practices/solar-data-should-be-based-on-physics-not-assumptions) In the solar industry today, we face a fundamental challenge: distinguishing between data that are based on physics and validated scientific methodology, and those that are a product of subjective manipulation and legacy approaches. This distinction has a profound impact on the quality of decision-making and the long-term success of projects. -------------------------------------------------------------------------------- ## Being certain about solar radiation uncertainty Source: https://solargis.com/resources/blog/best-practices/being-certain-about-solar-radiation-uncertainty # Being certain about solar radiation uncertainty Jul 17, 2025 One of the few things we can be sure of is that nothing is absolutely certain. This paradox is especially relevant in science, where uncertainty is part of every model. In the context of PV yield simulation, uncertainty helps users understand the potential deviations in the results produced by the software they are using. Understanding these deviations plays a key role in [selecting the optimal design of a power plant](https://solargis.com/products/evaluate) and in evaluating financial risks and return on investment. Uncertainty aims to reflect the limitations and assumptions built into simulation models and their input data. One of the most important inputs when calculating PV yield uncertainty is the solar irradiance dataset used in the simulation. In this article, I highlight key points to explain what solar irradiance uncertainty is, how it is calculated, and why understanding it is essential for reliable project assessment. ## Solar radiation uncertainty is more than one number Uncertainty quantifies the range around an estimate within which the true value of a particular magnitude is expected to fall, typically expressed as a margin of expected deviation and a level of probability. This means that when dealing with uncertainty, there are several key data points we must consider to avoid confusion and enable meaningful comparison with other values: - What exactly you are looking at: Solar radiation consists of different components (e.g., direct, diffuse) and can be measured on different planes (horizontal to the ground, normal to the sun, tilted, etc.). It is therefore essential to clearly specify the solar parameter to which the estimate refers. GHI, DNI, DIF and GTI are the standard parameters used when discussing solar radiation. - How the value is aggregated: In addition to specifying the physical unit (commonly kWh/m² for solar radiation), it is important to indicate the aggregation level used (e.g., daily, monthly, annual). Using appropriate terminology is also important: irradiance typically refers to instantaneous power values, while irradiation denotes the accumulated energy over time. - How the uncertainty is expressed: Uncertainty is usually given as a percentage of the estimated value. However, to avoid ambiguity, it should also include the confidence level used (e.g., P90, P99, standard deviation). Summing up, a complete uncertainty statement should include: *Estimated value + solar parameter + physical unit + aggregation level + uncertainty margin + confidence level* Let’s illustrate this with an example: - Incomplete uncertainty statement: *“For this project, we expect a solar radiation value of 1234 kWh/m² with an uncertainty of ±4%.”* - Complete uncertainty statement:* “For this project, we expect a solar radiation (*GHI*) of 1234 kWh/m² (**annual sum for a single year**) with an uncertainty of ±4% (**expressed at P90 level**).”* ## How uncertainty is calculated Solar irradiance estimates based on semi-empirical satellite models have become a standard, thanks to the availability of consistent, high-resolution, and global satellite data. However, the process of evaluating the accuracy of these modeled solar radiation datasets can sometimes be unclear. Let’s summarize the procedure in five key steps: ### Step #1: Model-measurement comparisons The first step of the process consists of the systematic comparison of top-class, high-quality instruments (for solar radiation, secondary standard pyranometers and first-class pyrheliometers) with model estimates at the same locations and during the same periods. An assessment of ground measuring device quality and maintenance practices is also required. This means ensuring that pyranometers, pyrheliometers, and other sensors meet international calibration standards and are regularly recalibrated. Routine maintenance, including regular cleaning of sensor surfaces, should be also documented and strictly followed. For each site, different indicators are calculated. They are most often known as validation statistics, since they are also used for the purpose of validating the performance of a model. This comparison should be repeated for as many locations and over as long a period as possible. To properly evaluate a solar irradiance model, it should be carried out at meteorological stations representing all geographical regions and follow a [standardized process](https://solargis.com/technology/accuracy-and-validation) that enables comparison across different sites and measuring stations. It is important to highlight that validation and uncertainty are conceptually different: while validation stops at collecting a series of statistics for those sites where ground references are available, uncertainty goes one step further and aims to estimate the expected performance at sites outside the reference network. ### Step #2: Bias characterization Once a sufficient number of validation sites have been analyzed, an initial estimate of the model’s performance can be obtained. This step involves examining the frequency distribution and magnitude of deviations between model estimates and measured values. Analyzing the bias distribution across climate zones and geographical regions helps identify the main conditions or areas where larger or smaller discrepancies are likely between modeled and ground-measured irradiance values. These findings are often published in publicly available reports, offering preliminary guidance for solar model users on expected accuracy levels for annual values of solar radiation. However, for a more accurate uncertainty estimate at specific sites, a deeper view of all validation statistics and a complete analysis of contributing factors is required (as outlined in the next steps). ### Step #3: Non-systematic deviations While bias quantification is essential for identifying systematic deviations between model estimates and measurements, assessing non-systematic errors is equally important. This is typically achieved by calculating the Root Mean Square Error (RMSE), which captures non-systematic deviations and offers a more comprehensive assessment of overall model performance. Incorporating additional indicators of consistency, such as the Kolmogorov-Smirnov Index (KSI), helps evaluate a model’s ability to represent various solar irradiance conditions and offers deeper insights into model behavior before proceeding to uncertainty estimation. Focusing exclusively on long-term (annual) validation statistics can also hide seasonal inconsistencies. Therefore, for a robust uncertainty assessment, monthly statistics should be analyzed as well. ### Step #4: Factors affecting solar model uncertainty The characteristics of the different empirical error distributions identified previously are now analyzed and compared with the characteristics of each validation site and validation period. The aim is to associate site- and period-specific properties with corresponding features of the error distribution. Key factors influencing model performance may include: - Cloud persistence - Cloud variability - Aerosol optical depth - Total water vapor - Snow coverage - Terrain variability - Distance to water surfaces - Anthropogenic pollution - Satellite pixel distortion - High-albedo surfaces As a result, we can already identify situations where the performance of the solar model is expected to be lower, including high mountain areas, snow conditions, reflective deserts, proximity to coastlines, or urbanized environments. ### Step #5: Site-specific uncertainty estimate Bringing together the findings from the previous step, the goal now is to estimate the level of uncertainty for the requested site. This means evaluating, for that specific site, each of the factors identified as influencing the model’s performance. This is not an easy task and requires deep, expert knowledge of the model, its internal algorithms, and its input data. Due to the complexity of certain interactions and the limited validation experience in some regions, fully automating the uncertainty estimation process remains challenging. The limited availability of public reference stations in certain regions also necessitates the use of more conservative uncertainty estimates. However, as new solar projects are developed, more weather stations are installed, and scientific understanding progresses, our confidence in estimating solar radiation uncertainty continues to grow. Fig.1: Steps to estimate solar irradiance model uncertainty ## Comparing solar irradiance models Comparison between models can be tricky and requires a careful review of the underlying validation processes. To help with this, here’s a checklist of key questions that can help when comparing different solar irradiance models: - **Did the models undergo the same uncertainty evaluation process?**This is crucial, as comparing uncertainty values across models is only meaningful if both have been assessed using the same methodology covering identical statistical metrics (such as bias, RMSE), aggregation levels (monthly, annual), and confidence intervals (e.g., P90). Without a consistent evaluation framework, uncertainty figures from different models cannot be directly compared. - **Do any of the models employ site-specific tuning?**If yes, although such models may report excellent validation results at selected locations, their performance can deteriorate significantly when applied outside those areas. The validation statistics provided by such models often reflect "apparent accuracy" rather than genuine predictive performance. - **Are comparison studies conducted directly by data providers (or affiliated consultants), or are they performed by independent organizations?**Besides the accuracy statistics provided by data providers, independent studies allow models to be evaluated on equal terms using standardized methods. Collaborative benchmark exercises typically involve public research institutions and private stakeholders. Beyond fostering transparency, these exercises promote industry-wide knowledge exchange and continuous improvement of solar modeling practices. - **Do the statistics refer to the same reference locations, validation periods, and quality control protocols?**If this is not the case, relying on internal validations for direct model comparisons can be misleading, as variations in reference sites, data periods, or data filtering procedures may skew results in favor of one model over another. ## Uncertainty can be low but never zero As mentioned at the beginning of this article, uncertainty can never be entirely eliminated. However, this does not mean it cannot be minimized. In the context of solar irradiance inputs, there are clear steps that can be taken to reduce uncertainty — for example, carefully reviewing input datasets and selecting the highest-quality satellite-based data provider. Irradiance models rely on publicly available reference networks for validation, which also form the basis of uncertainty modeling (see the previous chapter). Uncertainty can be further reduced by incorporating local measurements to correct for biases introduced by the original resolution of satellite data and adjust the internal structure of the data, a process known as [site-adaptation](https://solargis.com/services/site-adaptation-of-solargis-models). For this, local solar irradiance measurement campaigns — lasting at least one year — are required. That said, ground measurements are not perfect. Even the highest-quality, well-maintained GHI (Global Horizontal Irradiance) sensors carry an inherent uncertainty in the range of ±2% to ±3%. This must always be taken into account when estimating data model uncertainty. Before any comparison or site-adaptation, measured data must undergo a rigorous quality assessment to eliminate values affected by measurement errors. These issues are typically only visible in high-resolution data — ideally sub-hourly or hourly. Aggregated data at daily or monthly resolution is unsuitable for this purpose, as proper quality screening is impossible and measurement errors cannot be reliably identified or quantified. ## Uncertainty, P50 and P90: Sample calculation A useful way to show how uncertainty affects expected values for the same project is to look at the different probability distribution charts and P50 and P90 values of solar irradiance. The following example illustrates why choosing assessments based on accuracy rather than just optimistic estimates is essential for sound project development and bankability. Let’s consider a sample case involving two models: - Model A: It gives a higher P50 GHI (1250 kWh/m2 ), which will likely provide higher P50 yield when running the simulation. - It comes with higher associated uncertainty (±10.4% for P90 confidence interval). - Model B: It gives a lower P50 GHI, which will likely provide lower P50 yield when running the simulation. - It also comes with lower associated uncertainty (±6.6% for P90 confidence interval). **Model A** ** [kWh/m2]** **Model B** **[kWh/m2]** **Most expected value (P50)** 1250 1230 **Value exceeded with 90% probability (P90)** 1120 1149 **Uncertainty (P90 confidence interval)** ±10.4% ±6.6% Fig.2: Uncertainty in global horizontal irradiance (GHI) estimates from two models at a sample site in Slovakia. Although Model A may initially appear more attractive due to its higher central estimate, its broader distribution reflects increased variability and risk. In contrast, Model B’s tighter distribution results in a higher P90 irradiance, which can lead to better financial terms for project financing, especially from conservative lenders. ## Adding other sources of uncertainty The solar irradiance model is not the only source of uncertainty affecting the PV yield estimate provided by the software. Throughout the PV simulation process, there are several other points where deviations may occur between the assumed values and what will happen in reality. This means accounting for additional sources of uncertainty that must be combined to [calculate annual P90, P99, or other Pxx values](https://solargis.com/resources/blog/best-practices/how-to-calculate-p90-or-other-pxx-pv-energy-yield-estimates). **Energy simulation:** While it is challenging to develop detailed uncertainty models for every step of the simulation, ongoing research continues to make progress in this area. The process involves evaluating various factors that introduce different types of uncertainty, including: - The quality of additional meteorological parameters included in the input datasets, and how these are utilized in conversion models. - The granularity of input data and how it is processed during the simulation (some software performs preliminary aggregation, meaning actual calculations may be done at a different time resolution than that of the input data). - The energy computation methods used within the software, such as ray tracing, simplified view factor models, etc. - User-adjustable input parameters and the default reference values provided by the software. - Technical specifications of PV components, including the verification of characteristics for PV modules and inverters. **Year-to-year variability:** In addition to uncertainties arising from solar irradiance and the simulation model, calculating annual P90, P99, or other Pxx values also requires accounting for interannual variability. This factor reflects natural weather fluctuations and can only be reliably estimated using a sufficiently long historical dataset. To properly account for interannual variability, it is important to examine the reference period of the input data and how it is handled within the simulation (some software can only operate with single-year periods). Fig.3: Screenshot of combined GHI uncertainty results in [Solargis Evaluate 2.0](https://solargis.com/products/evaluate). ## Knowing uncertainty unlocks finance Understanding PV yield uncertainty is essential for estimating financial risks. Reliable estimates of the expected solar resource are often a *conditio sine qua non* for securing project financing. On the solar irradiance side, it is important for data providers not only to improve weather models and measurement techniques (i.e., to deliver more accurate data), but also to deepen their understanding of model performance, specifically by providing robust estimates of data uncertainty. From a technical design perspective, optimizing a power plant’s components without knowledge of data uncertainty is nearly impossible. Engineers must ensure that selected equipment operates within the manufacturer’s recommended conditions, and doing so requires a solid knowledge of the input data’s reliability. High-quality solar irradiance inputs must be complemented by accurate and well-established computation models to minimize uncertainty in final energy yield estimates. Although uncertainty may seem to fall somewhere between probability and expectation, a thorough and careful evaluation of it is always necessary for PV projects. Assessing uncertainty is one of the first steps toward unlocking financing and estimating realistic project returns. Pablo Caballero — Technical Writer Pablo is an industrial engineer with extensive experience in the renewable energy and software development sectors. He specializes in technical writing and content marketing and is driven by a passion for bridging gaps between audiences, technology, and business. With a diverse background, he is dedicated to promoting sustainability ideas and education in STEM fields. ## Keep reading ## [Why to use satellite-based solar resource data in PV performance assessment](https://solargis.com/resources/blog/best-practices/why-to-use-satellite-based-solar-resource-data-in-pv-performance-assessment) It is widely accepted that high-standard pyranometers operated under rigorously controlled conditions are to be used for bankable performance assessment of photovoltaic (PV) power systems. ## [How Solargis is improving accuracy of solar power forecasts](https://solargis.com/resources/blog/best-practices/improving-accuracy-of-solar-power-forecasts) Just as there are horses for courses, different forecasting techniques are more suitable depending on the intended forecast lead time. ## [WEBINAR: Solar resources data applications for utility planning and operations](https://solargis.com/resources/blog/best-practices/webinar-solar-resources-data-applications-for-utility-planning-and-operations) On Monday 23 Feb 2015 at 16:00 UTC, Marcel Suri (Solargis) and Tom Hoff (Clean Power Research) present the use of weather satellite data -------------------------------------------------------------------------------- ## Solar Resource Data: Time Series Data vs Monthly Averages Source: https://solargis.com/resources/blog/best-practices/solar-resource-data-time-series-data-vs-monthly-averages # Solar resource data – time series data vs monthly averages Jan 10, 2019 Bad data in equals bad data out. This well-known phrase is very relevant in a context of technical design and energy simulation of photovoltaic (PV) power plants. Most solar companies understand this and are carefully evaluating uncertainty of solar resource data used for feasibility purposes. Together with quality and accuracy, it is also important to look at the **original time granularity **of the input solar and weather data. In these regards, we have seen two main approaches when running PV simulations: - The use of **artificial hourly data **profiles, generated from monthly averages by **synthetic generation**. - The use of **real hourly data time series**, as an **output **of the weather data models. Even though use of synthetically-generated, artificial hourly values has been a common practice, this approach is not recommended. In this article, we discuss the benefits of using [real hourly or sub-hourly time series](https://solargis.com/products/evaluate) instead of hourly data generated synthetically from monthly averages. For this purpose, we have run a PV simulation using real data obtained from Solargis model in hourly resolution. We then compared the results with the same simulation using synthetically generated hourly data (derived from monthly averages of original hourly time series). — ### Minimum uncertainty, maximum P90 The main solar and weather parameters needed for PV energy output simulation are the incident solar radiation and the PV cell temperature (calculated from air temperature). For a reliable energy simulation, it is important to have the **correct match between solar radiation and temperature **for each time step. Otherwise, power plant output and relevant energy losses can be over- or under-estimated. This effect should be accounted as an **additional factor to the uncertainty **analysis when using synthetic data. In a sample simulation in the software PVsyst for a default 15 kWp system located in Southeast Spain (*Plataforma Solar de Almería*, sample files available [here](https://solargis.com/products/time-series-and-tmy-data/useful-resources#sample-data)), we see a difference of 1.2% in annual energy output when using real hourly data compared to the use of synthetically generated hourly data. A similar exercise for other locations showed higher differences but mostly within ±2%, which can be considered as a reasonable estimate of additional uncertainty when using synthetically generated data. For sites with more complex patterns of solar radiation and temperature the additional uncertainty is expected to be even higher. Once all uncertainties are accounted (more information about how to calculate P90 in [a previous article](https://solargis.com/resources/blog/best-practices/how-to-calculate-p90-or-other-pxx-pv-energy-yield-estimates)), we can compare the results of PV energy simulation for our sample site in Table 1, below. Table 1: Energy output obtained from the simulation of a simple fixed-mounted system * (1) Use of pre-calculated long-term averages from pvPlanner could have a slightly higher uncertainty of approx. 1%* *(2) Inter-annual variability taken from PVsyst* *(3) Uncertainty factor from using synthetically generated profiles is included* In above example, even though the **P50 value **is higher (and looking more “appealing” at first sight) when using synthetic hourly data, the fact of having a higher uncertainty results in a **lower P90 value **(and less “appealing” in the end). This situation is illustrated in the probability chart below. In any case, for sake of project success, it is recommended to always use approach with lowest uncertainty. Fig. 1. P50 and P90 energy values from two different simulations with different uncertainties ### Accurate estimate of interannual variability For estimation of total uncertainty of the annual energy estimates we need to know the annual variability of weather conditions and expected energy output. This can be estimated accurately if we know the annual sums for at least a 10-year period, from recent history. In absence of multiple year time series, we can only guess the expected annual variability of annual energy production, which is very difficult, as year-by-year variability has very complex geographical patterns. In absence of time series data, a substitute approach is to make use of GHI annual variability values from sites with similar irradiation levels and climate conditions. For example, this approach is used as default when calculating P90 in PVsyst software (see http://files.pvsyst.com/help/meteo_notes_annual_variability.htm). There are two issues with this approach. First, the annual variability can vary significantly at different locations that have similar climate conditions and solar radiation. Second, as explained in a previous [blog article](https://solargis.com/resources/blog/best-practices/how-to-calculate-p90-or-other-pxx-pv-energy-yield-estimates), annual variability of GHI does not equal annual variability of PV output. Table 2: Interannual variability (standard deviation) for a sample location in Almería, Spain ### Understand range and extremes in weather data for optimum PV design Even though monthly sums of both real and synthetic data are the same, the hour-by-hour analysis will always show critical differences. This means that in a synthetically-generated hourly time series the **typical and extreme values are not fully captured **and the synthetic data typically show systematic deviations. Selection of optimum design and components without making higher risk assumptions on expected weather conditions becomes an impossible task when using synthetic data. For the sample site used in this article, such differences can be directly observed when comparing both datasets, real hourly TMY and synthetically generated year. There are differences between the maximum values registered in the data, but also in the statistical occurrence of hourly values above or below a certain threshold. This can be easily observed for all parameters used as inputs in the simulations; in our case Global Horizontal Irradiation (GHI), Diffuse Horizontal Irradiation (DIF), Air temperature (TEMP) and Wind Speed (WS). Related statistics for the sample site are shown in the Table 3. Table 3: Average values of main weather parameters for PV simulation for the sample location. Table 4: Weather statistics showing extreme values, calculated for real and synthetic data for the sample site in Almería, Spain. For even more complete results, it is advised to use a complete set of historical time series (provided with TMY datasets in Solargis). ### Accurate revenue planning and self-consumption analysis The hourly distribution is most typically not well represented in the synthetic data and this can have a significant impact in the analysis of expected revenues. Not only because of the difference in pricing for the generated energy, which may be according to **hourly prices **set either by the spot markets or by an eventual power purchase agreement (PPA), but also for situations when the PV system is designed for **self-consumption**. In such cases, having an accurate long-term prediction of generation profiles is also needed in order to design a plan that maximizes the energy that is produced the PV system. Figure 2 and Tables 5 and 6 below represent the differences per hour of the day when comparing simulation based on time series with real and synthetic hourly data. At the annual level, the highest overestimation, seen in the synthetic data, occurs in the afternoon. From the table of monthly deviations, we can also see that noticeable differences during the first and last hours of the day are significant, especially during certain months. Figure 2: Aggregated annual value of the difference between synthetic and real hourly data, for each hour Table 5: Aggregated monthly values of the difference between synthetic and real hourly data, by hour of the day. Table 6: Aggregated monthly values of the difference between synthetic and real hourly data, by hour of the day, expressed in percentage ### Validate (and accuracy-enhance) the model data using high quality ground measurements The performance of satellite-based solar resource models for a given site can be characterized by a set of indicators. **Bias or Mean Bias Deviation (MBD) **characterizes systematic model deviation at a given site, i.e. systematic over- or underestimation; **Root Mean Square Deviation (RMSD) **and Mean Absolute Deviation (MAD) are used for indicating the spread of error for instantaneous values. Having **good quality ground measurements **from the project site provides a unique opportunity for calculating such statistics and validating the weather data model (Table 7 and Figure 3). However, only if we have a **coincident period in hourly granularity of real values **we can run a proper comparison and identify possible deviations of the model. Synthetically generated data or monthly averages are not suitable for validation of solar resource model, as they do not represent real measurements. Table 7: Validation statistics for the sample site in Almeria, Spain. Figure 3: Dispersion representing satellite-based modelled data versus ground-measured data for hourly, daily and monthly values for the sample site. Validating the quality of weather data used in the simulation will provide confidence to the results. But if a sufficient period is recorded (at least 12 months), it can be also used for **model site-adaptation and data correlation**. The site-adapted models are then used for recalculating the dataset at higher accuracy and lower uncertainty. ### Calculate consistent metrics along the entire project lifetime Weather data models are able to provide **real hourly data for historical time**, but the models also offer the possibility of real time data calculation across the whole PV plant life-time (see Figure 5). This means that the same source of data used for site prospection, planning and design of the power plant, can be − at a later stage − used also during the power plant operation. This allows for a consistent comparison between the [actual ](https://solargis.com/products/monitor)**[performance assessment](https://solargis.com/products/monitor) **and the one initially calculated during the project development. It becomes helpful when the plant is a subject of **re-evaluation**, allowing more transparent transactions and solid evaluation of investment related to the solar PV asset. Figure 5: Real model data can be used consistently across all evaluation activities during the PV plant lifetime ### Summary Use of synthetically generated data from monthly averages is an outdated approach, as it allows for a very limited range of analysis and the results obtained from the simulation are of higher uncertainty. As a result, use of synthetically generated data can lead to incorrect decisions. Using real hourly data from a reliable source is preferred in all cases: to achieve the most accurate results and to run a complete analysis and optimization of the PV power plant. The use of monthly averages is useful for site prospection and very basic studies at preliminary stages of the project. Table 8: Summary of the capabilities of real hourly data versus synthetic hourly data. ### Further reading - A description of the methodologies taken in synthetic generation is described here: http://files.pvsyst.com/help/meteo_synthetique.htm - Solargis methodology for TMY generation:[https://solargis.com/products/evaluate](https://solargis.com/products/evaluate) - How to calculate P90 (or other Pxx) PV energy yield estimates: [https://solargis.com/resources/blog/best-practices/how-to-calculate-p90-or-other-pxx-pv-energy-yield-estimates](https://solargis.com/resources/blog/best-practices/how-to-calculate-p90-or-other-pxx-pv-energy-yield-estimates) Pablo Caballero — Technical Writer Pablo is an industrial engineer with extensive experience in the renewable energy and software development sectors. He specializes in technical writing and content marketing and is driven by a passion for bridging gaps between audiences, technology, and business. With a diverse background, he is dedicated to promoting sustainability ideas and education in STEM fields. ## Keep reading ## [Why to use satellite-based solar resource data in PV performance assessment](https://solargis.com/resources/blog/best-practices/why-to-use-satellite-based-solar-resource-data-in-pv-performance-assessment) It is widely accepted that high-standard pyranometers operated under rigorously controlled conditions are to be used for bankable performance assessment of photovoltaic (PV) power systems. ## [How Solargis is improving accuracy of solar power forecasts](https://solargis.com/resources/blog/best-practices/improving-accuracy-of-solar-power-forecasts) Just as there are horses for courses, different forecasting techniques are more suitable depending on the intended forecast lead time. ## [WEBINAR: Solar resources data applications for utility planning and operations](https://solargis.com/resources/blog/best-practices/webinar-solar-resources-data-applications-for-utility-planning-and-operations) On Monday 23 Feb 2015 at 16:00 UTC, Marcel Suri (Solargis) and Tom Hoff (Clean Power Research) present the use of weather satellite data -------------------------------------------------------------------------------- ## From Time Series to TMY: When to use each? Source: https://solargis.com/resources/blog/best-practices/from-time-series-to-tmy-when-to-use-each # From Time Series to TMY: When to use each? May 29, 2024 A PV yield simulation serves multiple purposes. It is essential for finding the most appropriate design and components for a project while providing confidence to those investing in building the project. In the last decades of the 20th century, with the development of solar energy applications, energy simulations have traditionally run their calculations using summarized conditions described by the so-called Typical Meteorological Year datasets (TMY). Currently, thanks to the relatively recent development of satellite-based modeling and easier access to computing capabilities and software tools, solar industry players can now [test their power plant designs](https://solargis.com/products/evaluate) and financial plans against more realistic conditions described by multi-year Time Series (TS). To what extent is it possible to characterize solar irradiance and meteorological conditions with the 8760 values stored in a TMY? Does it mean that popular TMY datasets are no longer useful? — In this article, we provide an overview of these two main datasets commonly used for yield assessments and give recommendations on when to use each of them. ### A short description of how TMY is made from TS The TMY P50 is constructed by selecting the most representative months from the available time series (i.e. the most typical January, February, March, etc.), which are then concatenated into one artificial and representative single year. In Solargis TMY, the selection of representative months is done through an iterative process based on two main criteria: firstly, achieving minimal differences between the statistical characteristics of the Typical Meteorological Year (TMY) and the actual time series; and secondly, ensuring maximum similarity between the monthly Cumulative Distribution Functions (CDF) of the TMY and the time series, to accurately represent typical values for each selected month. The difference in relevance of each parameter when selecting the typical months is addressed by assigning weights to the parameters included in the dataset. Besides solar irradiance and temperature, other meteorological parameters are also included in the dataset, but typically these are secondary parameters with less relevance in the analysis and thus do not influence the choice of the representative month. It is also important to know that different weighting can be used depending on the type of solar energy applications under consideration. For example, a TMY made for building performance simulation could not be the same as one made for thermosolar applications, which in turn could differ from TMYs made for PV simulation software. Therefore, when comparing TMY datasets we always recommend to note how TMY is constructed and what it is made for. ***Figure 1. **Representation of monthly temperature data included respectively in TMY (chart on the left, reference year set to 1900 by convention) and Time Series (chart on the right, data period covering complete years since 1994).* ### Data loss Essentially, TMY is an attempt to summarize variable conditions into a single year with hourly granularity. This results in lighter files that are easy to handle, but as a consequence, a lot of valuable information is lost. — During the conversion, this data loss occurs in two ways. First, long periods are discarded in the process. Second, the data aggregation of sub-hourly values into hourly hides aspects of resource variability that can be useful to look at. Besides, since each site may result in a different set of selected months, the TMY algorithm makes it difficult to compare sites. This lack of spatial continuity also makes TMY unsuitable for regional analysis or for adjusting data using nearby ground measurements (site adaptation). ***Figure 2.** Graphical representation of the conversion process of TMY from TS* ### When TMY is still useful Regardless of the data discarded during its construction process, Typical Meteorological Year (TMY) datasets can still be useful for making quick comparisons at the early stages of a solar energy project when [site prospection or pre-feasibility analysis](https://solargis.com/products/prospect) is usually required. Handling light datasets has an effect on data services, making API calls faster. That can be particularly useful for applications where multiple sites need to be checked in a short period of time. ***Figure 3.** Typical Meteorological Year monthly values are the monthly averages* ### When to definitely use Time Series Since it represents typical conditions rather than extreme ones, TMY is less suitable after the initial preliminary stages of project development. That is exactly when Time Series can be useful, for instance, when designing systems to withstand the most adverse conditions that might occur at a specific location. When conducting financial studies, Time Series are recommended to anticipate years with lower (or higher) returns. In general, when a more detailed site characterization is required, Time Series should be used. This includes the case of variability analysis at different levels: interannual, monthly, or sub-hourly. In practice, when the simulator’s data handling capacity is limited, we can consider Time Series as the “raw material” to create secondary data products if needed. That means that if someone still wants to use “chunks” of a one-year period for energy simulations, they can easily be extracted from the original Time Series and identify those that were particularly extreme in terms of highest or lowest solar irradiance (or any other meteorological parameter with influence in PV system design and performance). ***Figure 4.** Time series monthly values provide minimum and maximum monthly values besides monthly averages* ***Figure 5.** Time series allows the calculation of other statistics e.g. P90, P99, etc.* ### Conclusions While TMY datasets could still be useful when fast comparisons are required (usually at the first stages of the project), Time Series datasets are required for doing a technical and financial analysis of a PV power plant. — The most relevant differences between both datasets are summarized in the tables below. ***Figure 6.** Comparison of Time Series and TMY data features* ***Figure 7.** Comparison of Time Series and TMY data applications * Download sample Time Series here (CSV) and sample TMY file here (CSV). Pablo Caballero — Technical Writer Pablo is an industrial engineer with extensive experience in the renewable energy and software development sectors. He specializes in technical writing and content marketing and is driven by a passion for bridging gaps between audiences, technology, and business. With a diverse background, he is dedicated to promoting sustainability ideas and education in STEM fields. ## Keep reading ## [Why to use satellite-based solar resource data in PV performance assessment](https://solargis.com/resources/blog/best-practices/why-to-use-satellite-based-solar-resource-data-in-pv-performance-assessment) It is widely accepted that high-standard pyranometers operated under rigorously controlled conditions are to be used for bankable performance assessment of photovoltaic (PV) power systems. ## [How Solargis is improving accuracy of solar power forecasts](https://solargis.com/resources/blog/best-practices/improving-accuracy-of-solar-power-forecasts) Just as there are horses for courses, different forecasting techniques are more suitable depending on the intended forecast lead time. ## [WEBINAR: Solar resources data applications for utility planning and operations](https://solargis.com/resources/blog/best-practices/webinar-solar-resources-data-applications-for-utility-planning-and-operations) On Monday 23 Feb 2015 at 16:00 UTC, Marcel Suri (Solargis) and Tom Hoff (Clean Power Research) present the use of weather satellite data -------------------------------------------------------------------------------- ## Solargis’ Approach to One-Minute Data Source: https://solargis.com/resources/blog/best-practices/solargis-approach-to-1-minute-data # Solargis’ approach to 1-minute data Jul 3, 2023 An increasing number of solar PV plant developers, operators and owners require high frequency data (1-minute) to make qualitative improvements throughout the entire lifecycle of a solar project. To fulfil this demand, Solargis has collaborated with the University of Malaga to create a new approach to producing 1-minute data, which uses statistic methods (stochastic generator) to insert high-frequency weather and solar irradiance events into satellite data.** — ***Illustration of the daily profile of the monthly average GHI value for January 2021 calculated from 1-minute and 1hr aggregation of the same dataset.*** — Filling gaps in understanding — **Today, the solar industry mostly relies on low frequency weather and solar irradiance data collected at 60-minute intervals. However, low granularity data can lead to gaps in effectively understanding and optimising the financial and technical performance of solar PV plants, and their designs. Instead, the industry needs to move towards collecting weather and solar irradiance data at shorter intervals. By accurately capturing the solar irradiance variability, weather and PV power potential of a chosen solar plant site, 1-minute data reduces broad and hindered decision making around project design, development, and operations. In turn, this helps project designers, developers and operators maximise a plant’s energy yield, optimise its relationship with the grid, and, ultimately, strengthen its financial and technical performance. — * Illustration of problematic measurement error detection in in the data (specifically the occurrence of dew on the sensor in this case). In 1-minute data the dew occurrence is visible in the morning hours 7:00-8:00, while in the 1h smoothed dataset the dew on the sensor cannot be identified.* **How does Solargis’ 1-minute data approach work — **1-minute data produces an extremely granular or high frequency dataset, by combining real satellite-based values with a stochastic data generator model, to produce a secondary stream of data that can better describe the variability of a particular site. The stochastic generator is designed to evaluate solar irradiance over prolonged periods of time, at least one year. To do this, specific transition probability matrices (TPM), alongside four different sky conditions ranging from stable to highly variable GHI, are collected using Solargis’ extensive, multiyear worldwide database. Only the 1-minute GHI measurements from locations with comparable solar climatic features are compared to any specific desired location of a solar PV plant. Next, 1-minute GHI is generated through combining 10-15-30-min GHI values and the TPMS as inputs using only 1-minute GHI values whose mean is close to the 10/15/30-min GHI input within a tolerance limit (±1 W/m2). Through Solargis’ global coverage, data from a vast range of sites can be accessed, enabling a deep analysis to be performed in the planning phase of the project. — **What is the potential of Solargis’ approach to 1-minute data? — **Solargis’ approach to 1-minute data has the potential to make qualitative improvements in the simulation of PV systems, in particular addressing transient effects. For example, high frequency data can offer the opportunity to study the temporal and spatial effects of clouds over large area power plants. Solargis’ 1-minute data can be used in combination with ground measurements (although not a necessity) as well as a company’s historical forecasting data, to unlock significant financial and technical benefits that are facilitated by more accurate decision making. — **Solargis’ 1-minute data blog series — **Our following blog series will discuss: - [4 reasons why PV project designers need 1-minute data](https://solargis.com/resources/blog/best-practices/4-reasons-why-pv-project-designers-need-1-minute-data) - [Why 1-minute data is essential for solar project financiers ](https://solargis.com/resources/blog/best-practices/why-is-1-minute-data-essential-for-solar-project-financiers) - The pros and cons of 1-minute, 15-minute, and 60-minute solar data Find out more about [**Solargis’ 1-minute data methodology**](https://solargis.com/technology/methodology/methodology/1-minute-solar-data)[**.**](https://solargis.com/technology/methodology/methodology/1-minute-solar-data) Pablo Caballero — Technical Writer Pablo is an industrial engineer with extensive experience in the renewable energy and software development sectors. He specializes in technical writing and content marketing and is driven by a passion for bridging gaps between audiences, technology, and business. With a diverse background, he is dedicated to promoting sustainability ideas and education in STEM fields. ## Keep reading ## [Why to use satellite-based solar resource data in PV performance assessment](https://solargis.com/resources/blog/best-practices/why-to-use-satellite-based-solar-resource-data-in-pv-performance-assessment) It is widely accepted that high-standard pyranometers operated under rigorously controlled conditions are to be used for bankable performance assessment of photovoltaic (PV) power systems. ## [How Solargis is improving accuracy of solar power forecasts](https://solargis.com/resources/blog/best-practices/improving-accuracy-of-solar-power-forecasts) Just as there are horses for courses, different forecasting techniques are more suitable depending on the intended forecast lead time. ## [WEBINAR: Solar resources data applications for utility planning and operations](https://solargis.com/resources/blog/best-practices/webinar-solar-resources-data-applications-for-utility-planning-and-operations) On Monday 23 Feb 2015 at 16:00 UTC, Marcel Suri (Solargis) and Tom Hoff (Clean Power Research) present the use of weather satellite data -------------------------------------------------------------------------------- ## Surface Albedo – most frequent questions | Solargis Source: https://solargis.com/resources/blog/product-updates/surface-albedo-most-frequent-questions # Surface Albedo – most frequent questions Nov 19, 2019 Due to the impact that surface albedo has on PV yield calculations, particularly for bifacial systems, there has been an increased interest in learning about this parameter. These are the most typical questions we are receiving and their corresponding answers: ### Why are solar developers thinking about bifacial modules for their projects? [Bifacial PV modules](https://solargis.com/solutions/optimizing-power-plant-design) is not a new technology, but it is gaining popularity amongst project developers. Bifacial modules offer several advantages in comparison with monofacial modules: - **Bifacial gain** – with no system change you can expect higher yields. Design modification (increased height, row spacing, type of construction….) is required for the optimisation of the gain. - **Price** – bifacial are only slightly more expensive than monofacial modules. However, bifacial gain usually outweighs the increased cost of panels ### Which factors influence the bifacial gain? There are a few major factors that influence the bifacial gain. To get the most out of your bifacial modules you should consider: - **Surface albedo** (discussed further in detail) - Installation** height** of the panels – the higher the better. NREL recommends at least 0.5m above ground. Over 3m there is very little impact. This, however, increases the costs of the installation - **Spacing** of the panels – the wider the better but again more expensive and unpopular - **Tilt angle** – slightly higher tilt angle than what is optimal for monofacial depending on local conditions ### What does surface albedo represent? Surface albedo (from the Latin word albus, which means white) is the measure of **diffuse reflection of solar radiation from the ground back to space of incidence**. It is dimensionless and measured on a scale from 0 (a black surface that absorbs all radiation) to 1 (a surface that reflects 100% of received radiation to space of incidence). So, for instance, a surface albedo value of 0.2 means that the surface reflects 20% of the received radiation. This seemingly simple definition has relatively complex physical implications for [PV simulation](https://solargis.com/products/evaluate). Albedo is **not a constant surface property of a particular surface**. It is also dependent on atmospheric parameters and illuminating conditions. Albedo values vary in different temporal scales: minute, daily, seasonal and even interannual. Typical daily variation of albedo (purple line) as measured at the clear-sky day of the 2nd of January 2011, in BSRN Bondville station, US: Since albedo values are not constant, a typical range of values can only serve as a **first preliminary estimate**: ### How to characterize the surface albedo for a particular site? The historical values of surface albedo are a critical input for accurate yield simulation of bifacial PV projects. There are two main ways of obtaining historical albedo values at any location globally: - **Climate reanalysis **provides data on the recent history of the atmosphere, land surface, and oceans by combining numerical weather prediction (NWP) models with observations. These datasets are constantly updated and can provide globally gridded data with a high temporal resolution. They provide data with no gaps, but the spatial resolution is low. - **Satellite-based models** use inputs from sensors installed on satellites, which continuously provide new information. One of the best-known sensors and whose derived data are widely used, is called Moderate Resolution Imaging Spectroradiometer (MODIS), an instrument able to cover the globe every 1 to 2 days while making measurements in 36 spectral bands (7 for albedo). The spatial resolution achieved is noticeably higher than on the reanalysis, but the data contains gaps due to the presence of persistent clouds and/or fails in the detection of snow. ### Why is it usual to find gaps in the time series of Albedo? Weather events are often accompanied by clouds and satellites cannot "see" the Earth’s surface in the presence of clouds. In particular, **data gaps due to snow events** are especially challenging. We have observed under-detection and over-detection of snow albedo in all the sources we have checked. The reasons for this are: - **Cloud-screening algorithms** are not perfect and they present difficulties some times to accurately distinguish between clouds and snow. - **Change in the snow properties** along the time: fresh snow is very bright (albedos over 0.8), but it may smelt and become dirty in the short term and the albedo can be as low as 0.5 or even lower. To solve this, the strategy to generate (daily) albedo is based on a weighted **average of 16-days time window** centred on the day of interest. This approach, followed by satellite-based MODIS products, allows to partially fill the gaps. However, in some regions and moments of the year, this is not possible to do because of the strong cloud persistence. In such cases, for running a complete gap-filling we need to consider additional sources such as: - **complementary satellite-based **products - additional **data from** **reanalysis** When combining different sources, we want to reach an optimum balance between both accuracy and spatial representation. ### Any recommendations for a full albedo characterization and validation of the satellite-based estimations? Several years of data is required to capture the full temporal variability of the surface albedo of a particular site. Having information about the land use on that particular site is also of great interest to interpret any changes in the data and assess the representativeness of the area covered. Surface albedo can be measured with an **albedometer**, a combination of two individual pyranometers -optimally identical- facing up to the sky in the horizontal position, as usual for measuring GHI (Global Horizontal Irradiance), and facing down to the ground for the GRI (Global Reflected Irradiance). The quotient of both magnitudes is the surface albedo. Hukseflux SRA11 albedometer Although installing the sensor in the POA (plane-of-array) is useful to know the GTI (Global Tilted Irradiance) that is received on the backside of the panels, surface albedo cannot be measured in such position. Ground-based albedo measurements, covering only a few days is not enough for having a robust one-to-one comparison between albedometers and satellite-based data. To cover full seasonal behaviour, we recommend measuring albedo for at** least one-year period**. A slightly shorter measurement period can be considered depending on the location. ### Does Solargis provide surface albedo values? Yes, Solargis has prepared an albedo database that is tailored to the needs of the solar power industry. The database has been prepared by filtering available sources (coming from both satellite-based sources like MODIS and NWP like ERA5), properly combining them, applying downscaling methods, and running additional corrections. The data are available via Solargis products Prospect (as a new feature in the Professional Plan) and Evaluate (as an add-on to the Professional time series): On the left, representation of surface albedo database in lower resolution with gaps. On the right, representation of Solargis albedo database: Table with a description of the albedo data products offered by Solargis: You can know more about technical specification of Albedo products in Solargis here: https://kb.solargis.com/docs/satellite-based-albedo-data ### How accurate is Solargis albedo database? The Solargis albedo database has been **validated** using ground measurements. At the date of publication of this text, we have validated the database at8 sites across the USA belonging to AMERIFLUX and SURFRAD ground stations networks. The period of evaluation we have used was five years, from 2011 to 2015. After running the comparison, the validation results of Solargis albedo versus ground measured data are (in terms of the estimated uncertainty): Solargis database obtained the **lowest bias** in comparison to other sources of data (some of them used as an input for the database). Additional validation and local adjustment of the values provided by the database can be done if there are sufficient ground measurements available at the project site. ### Are there differences between Solargis Albedo monthly averages (Prospect) and the averages calculated from Solargis Albedo time series (Evaluate)? The averages of the Solargis Albedo time series ([Evaluate](https://solargis.com/products/evaluate)) **may not be coincident** with Solargis Albedo monthly averages ([Prospect](https://solargis.com/products/prospect)). The reasons for this are: - In the **daily time-series** product, gaps in the data are not filled - In the **monthly averages** product, gaps are filled before calculating the averages to have a better statistical representation; we have also used different satellite-based MODIS product and the final resolution is lower. Representation of Solargis monthly averages of Albedo for a sample location in Plataforma Solar de Almería, Spain: Representation of Solargis time series of Albedo for the same sample location in Plataforma Solar de Almería, Spain: If you want to request your sample of Solargis albedo data, or you have any questions please [contact us](https://solargis.com/contact-us) Pablo Caballero — Technical Writer Pablo is an industrial engineer with extensive experience in the renewable energy and software development sectors. He specializes in technical writing and content marketing and is driven by a passion for bridging gaps between audiences, technology, and business. With a diverse background, he is dedicated to promoting sustainability ideas and education in STEM fields. ## Keep reading ## [Improved monitoring and forecasting service for Indian Ocean region](https://solargis.com/resources/blog/product-updates/meteosat-8-implementation) The meteorological satellite Meteosat-7, which had been providing satellite imagery for the Indian Ocean region has now been decommissioned. ## [GOES-East satellite covering Americas is replaced by the new generation GOES-R](https://solargis.com/resources/blog/product-updates/goes-east-satellite-covering-americas-is-replaced-by-the-new-generation-goes-r) The old GOES-East satellite, also known as GOES-13, was launched to orbit in 2006. The data coverage is North and South Americas. ## [New 3D tool for configuring and visualising tracker movement](https://solargis.com/resources/blog/product-updates/new-tracker-configurator) We’ve released a new tool that will allow Solargis users to request time series data for PV systems with trackers. You can choose from 4 types of tracker options: 1-axis horizontal, 1-axis inclined, 1-axis vertical, and 2-axis tracking. It is also possible to specify tracker rotation limits and enable/disable backtracking. -------------------------------------------------------------------------------- ## What is lowest expected operating temperature (TLEO)? Source: https://solargis.com/resources/blog/product-updates/lowest-expected-operating-temperature # Lowest expected operating temperature (TLEO) & Solargis approach Oct 4, 2023 Extreme cold conditions can lead to increased voltages in photovoltaic (PV) modules, potentially causing system overload. Learn about the critical importance of understanding the lowest expected operating temperature parameter (or TLEO in Solargis language) for optimal PV array sizing. In the article, we’ll discuss: - Why consider low temperatures in your PV projects - Definition of TLEO (according to IEC 62738) and technical background - Our process for mapping global TLEO data, drawing from both climate models and meteorological observations - Where to find TLEO in Solargis Prospect - How we validate the TLEO data layer - Notes on standardization Whether you’re an investor, designer, operator, or any other professional in the solar industry, this article will provide a clear understanding of how to think about PV arrays in cold conditions. This can lead to reduced system failures, longer operational lifespan, and fewer maintenance headaches. ## Why consider situations when PV plants experience low temperatures? A critical aspect that often gets overlooked within PV projects? Performance of PV systems in extremely cold conditions. The lowest temperature typically occurs shortly before or during the sunrise. The combination of the extremely low ambient temperature of PV modules and abundant solar radiation during sunrise can trigger a sudden rise in the voltage in the PV string. When certain critical levels are exceeded, it has the potential to lead to system overload. The lowest expected operating temperature is central to this concern. Accurately determining TLEO is essential for the safe and efficient operation of PV arrays. ## The technical background According to best practices in sizing of PV arrays (introduced among others in the IEC 62548 and IEC 62738 standards), the maximum number of PV modules in the string is determined by the maximum voltage rating of the components such as the modules and inverters. The open circuit voltage (Voc) for a PV string is calculated based on the module’s Voc rating and the lowest expected operating temperature (TLEO), as per industry standards. TLEO is defined in the IEC 62738 [1] standard as the mean of the lowest annual values of air temperature. If enough data is available, it can be limited to the hours of sunlight and a low irradiance threshold. The calculated maximum PV string length is often the most cost-effective option because it reduces losses in the cables and the overall cable length. In cases where the calculated maximum voltage is slightly above the maximum system rating, further analysis may be necessary to assess the risk of overvoltage. ## How we create the global TLEO data layer at Solargis The best option to obtain air temperature for a given location is through continuous long-term measurements, using high-accuracy, calibrated, and well-maintained temperature sensors, mounted according to WMO standards [2]. Unfortunately, this method is restricted to locations where long-term meteorological observations are conducted, normally as part of national meteorological services or other observation networks. On a global level, the only alternative is to derive historical air temperature data from available climate models. In this project, the development of the TLEO global map begins with the processing of ERA5 climate reanalysis data (operated by ECMWF and Copernicus services [3]), provided with a native resolution of 0.25° (nominally 28 km). Here, for each year the absolute minimum air temperature at 2 meters height is derived from a 1-hourly time series, spanning the period of 2001-2020. The resulting TLEO value is then averaged from 20 yearly values. To overcome the coarse spatial resolution of ERA5 data (particularly in mountains with significant vertical terrain dissection and coastal areas), we introduce a spatial disaggregation based on calculated lapse-rate corrections, derived from terrain elevation (Fig X2) and air-temperature data from various height levels. The result is a fine-resolution data layer (~1 km pixel size) with TLEO values available globally for any location in the world (see Fig 1 and 2). ## Find TLEO maps and data in Solargis Prospect Starting from September 2023, we made available the lowest expected operation temperature (TLEO) data in Solargis Prospect. With the TLEO variable, the calculation of the maximum voltage fluctuations and PV string sizing decisions are more accurate, ensuring the safe and efficient operation of the PV system under diverse weather conditions. Fig 1. High-resolution TLEO map, long term average, period 2001 – 2020 Fig 2. High-resolution TLEO map, long term average, period 2001 – 2020. Focus on the regions with high TLEO gradients: Pacific coast in South America (left), Central Europe (middle) and South Asia (right) ## Validation of the TLEO layer The final TLEO data layer is validated with time-series data measured at more than 8500 stations worldwide and made available in the Integrated Surface Database (ISD) by the National Oceanic and Atmospheric Administration (NOAA) [4] for years 2001-2021. The overall bias, calculated as the difference between the modeled and measured TLEO, is 1.2°C, standard deviation of 2.4°C. The most extreme values (presented here as percentile values P5 and P95) drop below -2.6°C and exceed 5.1°C, respectively. In general, the results meet the purpose of the PV array sizing in most of the world. However, regions with microclimate anomalies were identified and potential recurring problems were found in the modeled dataset (Fig 3). As expected, the ERA5 model resolution is not detailed enough for some mountainous regions worldwide. The majority of meteorological stations are located in valleys where the ERA5 representation of elevation is typically higher than the actual elevation of the sites, resulting in underestimated temperature values. The lapse-rate correction helps to some extent to address these discrepancies, but the magnitude of the correction is often not sufficient – e.g. Central Alps (Fig 3A), Scandinavian Mountains (Fig 3C), Tibet, Himalayas, West Iran, Southeast Turkey, etc. Another recurring issue is that ERA5 underestimates the continental influence, particularly along the coastal areas (e.g. Southeast Australia in Fig 3B). The model overestimates the influence of sea/ocean, which can lead to inaccurate temperature predictions. For example, cold air falling from the mountains towards water bodies can have a significant impact on the temperature of mountainous islands, but this effect is not always well-captured by models with coarse resolution (e.g. Corsica island in Fig 3A). Another problem identified was the temperature inversion in cold valleys. Inversions happen when a layer of cold air gets trapped in the valley, sealed off by a layer of warm air above. The geography of the area prevents the cold air from flowing out of the valley, and the surrounding mountains inhibit winds from easily clearing it out and bringing in new air. Fig. 3: Selected areas with significant differences between calculated TLEO and TLEO derived from NOAA ISD meteorological stations ## Further notes on standardization As already described above, TLEO is defined in the IEC 62738 standard as the mean of the lowest annual values of air temperature. On the one hand, averaging filters out incidental extremes. On the other hand, the averaging introduces an error associated with estimating absolute TLEO for the entire period of 2001-2020 and the TLEO calculation as a mean as prescribed in the IEC standard. Fig 4 illustrates the comparison of the error distribution introduced by monthly lapse rate simplification against the distribution of standard deviations of annual TLEO and the distribution of the absolute difference between record high and record low annual TLEO. A total of 30,000 points were evaluated globally by comparing the TLEO calculated using a full time-series approach against raster calculations using a random distribution of sites. Fig 4: Comparison of distribution in differences in TLEO calculation method (time-series vs raster), standard deviation of annual TLEO and the distribution of the absolute difference between record high and record low annual TLEO The relatively high value of the standard deviation of annual TLEO and the high difference between the record low and record high annual TLEO is an important issue to be considered in sizing PV power plants based on the average TLEO map alone. It must be also noted that the temperature of PV modules facing the open sky can be 5°C lower than the ambient air temperature in some locations. Analogically, NOAA ISD measurement data was analyzed. Fig 5 shows the difference between absolute TLEO and average TLEO calculated for the period 2010-2019. In many cases the values exceed 5°C, occasionally 10°C. Fig 5. Analysis of 10 years (period 2010-2019): Difference between the absolute minimum of air temperature and TLEO (the average of absolute annual minimums of air temperature) ### References [1] Zawaydeh, Samer. (2019). IEC 62738:2018 Ground-mounted photovoltaic power plants-Design guidelines and recommendations. — [2] Guide to instruments and methods of observation, World Meteorological Organisation (WMO-No. 8), 2021/2018 edition, ISBN: 978-92-63-10008-5 — [3] Hans Hersbach, Bill Bell, Paul Berrisford, et.al. The ERA5 global reanalysis First published: 17 May 2020 — [4] Smith, A., N. Lott, and R. Vose, The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 2011, 92, 704–708 Artur Skoczek Artur is an electronic engineer who holds a Ph.D. from the University of Science and Technology in Krakow. With many years of experience in scientific programming and numerical methods, he specializes in photovoltaic systems simulation and PV module characterization. His expertise encompasses processing meteorological data and solar power production forecasting, leveraging a solid understanding of applied numerical weather prediction models. He is a recognized authority in the field, actively contributing to the advancement of renewable energy technologies. ## Keep reading ## [Improved monitoring and forecasting service for Indian Ocean region](https://solargis.com/resources/blog/product-updates/meteosat-8-implementation) The meteorological satellite Meteosat-7, which had been providing satellite imagery for the Indian Ocean region has now been decommissioned. ## [GOES-East satellite covering Americas is replaced by the new generation GOES-R](https://solargis.com/resources/blog/product-updates/goes-east-satellite-covering-americas-is-replaced-by-the-new-generation-goes-r) The old GOES-East satellite, also known as GOES-13, was launched to orbit in 2006. The data coverage is North and South Americas. ## [New 3D tool for configuring and visualising tracker movement](https://solargis.com/resources/blog/product-updates/new-tracker-configurator) We’ve released a new tool that will allow Solargis users to request time series data for PV systems with trackers. You can choose from 4 types of tracker options: 1-axis horizontal, 1-axis inclined, 1-axis vertical, and 2-axis tracking. It is also possible to specify tracker rotation limits and enable/disable backtracking. # SECTION: Company -------------------------------------------------------------------------------- ## Bankable solar data, software & services for PV projects Source: https://solargis.com/ # Most accurate data and software for the entire PV power plant lifecycle We provide solutions for all phases of solar energy projects, from initial planning and securing financing to daily operations, management, and maintenance. ## [Unlimited possibilities for limited budgets](https://solargis.com/resources/blog/solargis-news/unlimited-15-minute-tmy-p50-simulations-in-solargis-evaluate) A €12,000 Solargis Evaluate subscription now includes: - 60 Early Stage projects​ - Unlimited number of 15-minute TMY P50 simulations for every project​ - Unlimited number of designs for every project​ - Unlimited number of collaborators for every project - Solar, meteo and environmental data for every project​ ## Why Solargis ### [High level of detail](https://solargis.com/products/evaluate) High-resolution data (250 m spatial resolution and 1-minute or 15-minute temporal resolution) better represents typical and extreme weather and improve the accuracy of solar energy simulations. [Solargis sub-hourly Time Series](https://solargis.com/products/evaluate) ### Extensive validation Solargis data has been validated at more than 1500 public and commercial locations globally, and the model validation is systematically expanding. Uncertainty of Solargis data can be easily estimated for different climate regions. ### [Historical, recent, and forecast data](https://solargis.com/products) Solargis data is available for past, present, and future periods and is updated in real time. We can meet your energy assessment needs from project conception to routine daily management. ### [Long-term experience](https://solargis.com/about/about-us) Our team has more than 20 years of experience in solar resource assessment and PV energy modeling. [More about Solargis](https://solargis.com/about/about-us) ### Most accurate and reliable data Multiple independent studies have confirmed Solargis to be the most reliable solar database. ### Independent and transparent Quality of our data can be transparently verified at any location. Our methodologies are peer-reviewed and published. ### Global coverage Solargis data and services are available for 99% of the world’s population and cover a 30-year history. ### Gap-free data We fill the occasional gaps in the data flow thanks to our proprietary intelligent algorithms. ## Solargis' solution PV power plant developers, operators, and other solar industry stakeholders need accurate data, an industry-specific software solution, and consultancy support to achieve the best possible results throughout the entire PV plant’s lifecycle. — We cover all three. ## [Most accurate data](https://solargis.com/technology/expertise) We provide solar industry professionals with a reliable source of solar radiation, meteorological, and environmental parameters. The source includes a detailed archive of rigorously validated, high-resolution historical data. The real-time operation data services are fully customizable to fit your project needs. ## [Software platform](https://solargis.com/products) Our software provides high-performance PV energy simulation, capabilities for data quality control, data analysis, and assembling bankable data for evaluating solar assets. As a performance evaluation solution, it integrates on-site validated measured and modeled data as well as solar power forecasting. It’s designed to provide precise and actionable insights for effective solar project management and planning. ## [Expert consultancy](https://solargis.com/services) Leverage our solar industry know-how based on experience working on 1500+ solar energy projects worldwide. With Solargis' expert and hands-on consultancy services, you will better understand, manage, and reduce weather-related risks in solar power projects. We offer bankable energy assessment and risk evaluation at the project level or a broader regional analysis for all strategic initiatives. ## Use cases Discover Solargis' solutions for all stages of the solar power plant lifecycle. From site selection and yield simulation to designing, monitoring, and forecasting, our software and data ensure you get the right insights. ### [Find the right solar project location](https://solargis.com/solutions/site-selection) Scan and compare tens or even hundreds of potential sites. Get an in-depth analysis of those with the highest solar potential. ### [Analyze potential gains](https://solargis.com/solutions/energy-yield-simulation) Simulate the yield potential of your next project with the most accurate solar and weather data on the market. ### [Find optimal power plant design](https://solargis.com/solutions/optimizing-power-plant-design) Get deep insights into potential power plant setups. Evaluate environmental data that will affect an asset’s efficiency and output. ### [Discover true output](https://solargis.com/solutions/real-power-plant-performance) Compare the actual performance of your power plant to the predictions. Identify the reasons for any lower production. ### [Predict your solar project energy output](https://solargis.com/solutions/power-output-forecast) Manage variability with 14-day power output forecasts. Get insights for trading, reduce grid penalties, and plan maintenance through accurate energy output predictions. ### [Improve data quality and reduce uncertainty](https://solargis.com/solutions/ground-data-verification) Cross-reference your ground measurements with satellite data. Uncover potential issues such as sensor soiling, shading, or equipment error. -------------------------------------------------------------------------------- ## About us | Solargis Source: https://solargis.com/about/about-us # About Solargis "We maximize the value of solar power by enhancing the industry’s knowledge of the solar resource. We provide reliable weather data, energy evaluation and forecasting software, and specialized knowledge to accelerate the integration of solar technology in the energy market." **Marcel Suri — ***CEO & Co-founder* ## Solargis highlights We offer an online platform that includes solar design, energy simulation, data analytics, and forecasting software, along with accurate and validated solar and meteo data to support every stage of your solar power project, from the pre-feasibility and planning to operation and management. Our [proprietary technology](https://solargis.com/technology/expertise) is developed in-house as a result of long-term R&D initiatives. ### 1200+ Active commercial and public customers in 90+ countries ### 2010 Founded in Bratislava, Slovakia, by Marcel Suri and Tomas Cebecauer ### 99% Coverage of the world’s population with 30+ years of solar and meteorological data ### 9 000+ Large solar projects supported yearly by our bankable solar and meteo data, software, and services ### 3 Offices with headquarters in Slovakia and subsidiaries in Canada and Singapore ### 130+ Enthusiastic experts working at Solargis ## Founders ## Marcel Suri CEO & Co-founder Marcel is an entrepreneur and solar energy specialist with a background in geography. Holding a PhD in Geoinformatics, he began his career as a researcher at the Institute of Geography, Slovak Academy of Sciences. In 2001, he joined the European Commission’s Joint Research Centre in Ispra, Italy, to work in photovoltaics, helping to develop and promote the PVGIS software. In this role, he combined his expertise in geoscience with renewable energies. His journey led to the founding of GeoModel Solar, where he acted as Managing Director before becoming CEO of the company rebranded to Solargis. Under his leadership, Solargis has emerged as a key player in providing solar resource and weather data, along with photovoltaic software and consultancy services. Through a dedicated approach to innovation, Marcel continually pushes for enhanced standards, aiming to boost the efficiency of the solar energy industry and reduce weather-related risks. ## Tomas Cebecauer CTO & Co-founder Tomas has a background in geography, earned his PhD in Cartography and Geoinformatics from the Institute of Geography, Slovak Academy of Sciences in Bratislava, Slovakia. In 2006, Tomas joined the European Commission’s Joint Research Centre in Ispra, Italy, where he significantly contributed to the development of the PVGIS online portal, broadening the reach of solar resource data and software. Transitioning from public research to the private sector, Tomas served as Technical Director at GeoModel Solar, before taking on the role of Chief Technology Officer at Solargis. Since 2010, he has been leading the development and operation of Solargis' global solar resource and meteorological database, alongside online simulation, management, and control solutions for solar photovoltaics systems. ## Leadership ## Nada Suriova COO ## Daniel Ranusa Head of Sales ## Branislav Schnierer Head of Consultancy ## Marketa Hulik Jansova Head of Customer Data ## Ales Buban Head of Development ## Miroslav Majtaz Head of Product ## Juraj Vanko Head of Marketing ## Our story Since its inception, Solargis has leveraged satellite image processing and atmospheric and meteorological models to develop proprietary algorithms and deliver high-quality solar data and energy evaluation software. After years spent in research around Europe, two Slovak scientists—Marcel Suri and Tomas Cebecauer—started to see the increasing gap between scientific theory and engineering applications. Their dedication to geoscience and solar research and the creation of sophisticated models began, at one point, intertwining with increasing market demand for such solutions. In 2010, Marcel and Tomas established a venture to apply and commercialize the research outcomes, aiming to support the industry in developing and operating solar power plants more efficiently. First known as GeoModel Solar, in 2016 the company changed its name to Solargis. As the years progressed, the founders expanded a multidisciplinary team of specialists. They increased the capacity of technical and customer engagement teams to meet the demand for investment-grade solar and weather data and provision of specialized solar energy software and advisory services. The Solargis team is equipped with the knowledge and experience accumulated from supplying Solargis data for tens of thousands of large-scale projects worldwide and working with solar and meteorological measurements at over 1500 locations. These insights help the team profoundly understand the challenges stakeholders face across the solar energy value chain. Solargis' commitment to quality and innovation has earned it recognition and trust from organizations and individuals all around the world. ## Timeline 1990-2008 Marcel Suri and Tomas Cebecauer work as geoscientists at research facilities in Slovakia (Slovak Academy of Sciences in Bratislava) and Italy (EC JRC in Ispra). 2008 In response to the growing demand from the solar industry for quality solar irradiance and meteo data, development activities started within GeoModel and moved to GeoModel Solar in 2010. 2010 ClimData (the predecessor of [Solargis Evaluate](https://solargis.com/products/evaluate)) is launched with the goal of providing access to global solar radiation and meteorological time series data starting in 1994. The database offers access to Typical Meteorological Year data based on in-house developed methodology. 2011 pvPlanner and iMaps software online apps are launched (the predecessor of [Solargis Prospect](https://solargis.com/products/prospect)), providing access to the first high-resolution database of solar and meteorological parameters, maps, PV energy simulation, and reporting solutions. 2012 PVSpot (the predecessor of [Solargis Monitor](https://solargis.com/products/monitor)) is launched. For the first time, solar power plant operators get and use satellite data almost in real-time, with a delay of only a few hours. 2013 Solargis database available globally. 2013 Solargis' satellite-based solar radiation model is explained in a book on Solar Energy Forecasting and Resource Assessment. The solar models are being constantly reviewed and improved. 2014 [Solargis Forecast](https://solargis.com/products/forecast) is launched, based on the processing of data from global numerical weather prediction models adding short-term forecasts to the spectrum of Solargis solutions and thus covering the full lifecycle of solar power projects. 2014 A satellite-based solar radiation data validation study by the University of Geneva has been published. The study, in a transparent and scientifically rigorous approach, identifies Solargis data as the most accurate. More independent studies published later confirmed these findings. Solargis data is validated at 1500+ public and private measurement sites. 2015 Solargis methodology on TMY data is published in Energy Procedia. The TMY data based on this methodology became an industry standard. Solargis delivered TMY data for approx. 10,000 large-scale projects yearly. 2015 Nowcast service provides very short-term forecasting for the next 3 hours based on the processing of geostationary satellite images. This method became an integral part of Solargis Forecast services. 2016 Solargis methodology on solar model site adaptation has been published in AIP Conference Proceedings. The method is routinely used for large-scale projects and has become an industry standard. 2016 GeoModel Solar [rebranded](https://solargis.com/resources/blog/solargis-news/geomodel-solar-renamed-to-solargis) to Solargis. 2017 [Launch of the Global Solar Atlas](https://solargis.com/resources/blog/solargis-news/solargis-develops-global-solar-atlas-for-the-public-good) developed by Solargis as part of the World Bank Group’s ESMAP initiative. 2018 [Solargis API](https://solargis.com/products/integration) is released. 2019 The Solargis Prospect app is [launched](https://solargis.com/resources/blog/product-updates/prospect-launch), making the pre-feasibility phase easier and more reliable. The app also features a set of high-resolution maps that help users understand the geographic context of solar, meteorological, and environmental conditions relevant to the development and operation of solar power assets. 2021 Solargis [joins](https://solargis.com/resources/blog/solargis-news/serendi-pv-solargis-joins-large-european-rd-initiative-to-increase-penetration-and-integration-of-photovoltaics) the European R&D initiative SERENDI-PV to improve the lifetime, reliability, performance, and profitability of PV power generation and increase the penetration and integration of photovoltaics in Europe. 2021 Solargis [opens](https://solargis.com/resources/blog/solargis-news/solargis-launches-north-american-office-to-tackle-growing-resource-risks-in-american-solar) an office in Toronto to serve the needs of growing markets in the Americas. 2022 [Solargis Analyst](https://solargis.com/products/analyst) is [launched](https://solargis.com/resources/blog/product-updates/solargis-launches-markets-first-specialist-software-for-solar-data-quality-management), the first specialist software in the solar industry for visualization, comparison, analysis, and quality management of solar and meteorological time series data . 2023 IEA Technology Collaboration Program Task 16 presents its Worldwide Benchmark study [showing](https://solargis.com/resources/blog/solargis-news/iea-worldwide-benchmark-highest-accuracy-solargis-model) the highest overall GHI and DNI accuracy for the Solargis model. 2023 Solargis [opens](https://solargis.com/resources/blog/solargis-news/solargis-opens-singapore-office-targeting-apacs-complex-growing-solar-market) an APAC office in Singapore to better manage the growing needs of customers in the region. ## Values We strive to unleash the global potential of solar energy as we transition to a net-zero future. ### We are Scientific Everything we do builds on the latest scientific knowledge and is underpinned by rigorous in-house development. We avoid shortcuts and do not release innovation without in-depth validation. ### We are Practical We listen to our customers and build reliable solutions and services that respond directly to their needs. ### We are Unbiased We never adjust numbers for short-term gain or let commercial pressures influence results. ### We are Honest We understand the limits of our knowledge and communicate these openly to our customers. ### We are Transparent Every aspect of our work is extensively validated, traceable, and can be verified independently. ## Work with us We are a group of geoscientists, engineers, meteorologists, cartographers, programmers, and business professionals. — We are growing and looking to have more resourceful and passionate individuals who can help in developing innovative and problem-solving products and services for the solar energy industry. ## Offices ### Headquarters,Sales EMEA and India **Call us** — Mon - Fri 9 AM to 6 PM CET — phone: +421 2 431 91 708 **Visit us** — Solargis s.r.o. — Bottova 2A — 811 09 Bratislava — Slovakia ### Americas office **Call us** — Mon - Fri 9 AM to 6 PM EDT — phone: +1 647 458 9605 **Visit us** — Solargis Americas Inc. — 150 King St. W, Suite #200 — Toronto, ON M5H 1J9 — Canada ### Asia Pacific office **Call us** — Mon - Fri 9 AM to 6 PM Singaporean Time Zone (UTC+8) — phone: +65 9396 7410 **Visit us** — Solargis APAC Pte. Ltd. — 6 Battery Road, #03-326 — Singapore 049909 -------------------------------------------------------------------------------- ## Customers | Solargis Source: https://solargis.com/customers [NOTE: This page has no "## Content" section / no fenced ```md block in the source file. The file ends after the Structured data (JSON-LD) BreadcrumbList section — there is no rendered page body to extract.] -------------------------------------------------------------------------------- ## Contact us | Solargis Source: https://solargis.com/contact-us # Contact us Do you need more information about our solutions and services? ## Headquarters, Sales EMEA and India ** — Solargis s.r.o.** — Bottova 2A — 811 09 Bratislava — Slovakia Mon - Fri 9 AM to 6 PM CET — phone: +421 2 431 91 708 ## Americas office ** — Solargis Americas Inc. — **150 King St. W, Suite #200 — Toronto, ON M5H 1J9 — Canada Mon - Fri 9 AM to 6 PM EDT — phone: +1 647 472 1588 — fax: +1 647 277 4682 ## Asia Pacific office ** — Solargis APAC Pte. Ltd.** — 6 Battery Road, #03-326 — Singapore 049909 Mon - Fri 9 AM to 6 PM — Singaporean Time Zone (UTC+8) — phone: +65 9396 7410 # SECTION: Optional -------------------------------------------------------------------------------- ## Technology - Our expertise | Solargis Source: https://solargis.com/technology/expertise # Our technology is based on scientific research applied and validated by the solar industry. Solargis expertise meets at the crossroads of three fields: meteorology, engineering, and data science. **[1. Modeling site conditions](https://solargis.com/technology/expertise#modeling-site-conditions) | [2. Designing and simulating PV plants](https://solargis.com/technology/expertise#designing-and-simulating-pv-plants) | [3. Analyzing solar power metrics](https://solargis.com/technology/expertise#analyzing-solar-power-metrics)** ## Modeling site conditions At Solargis, we provide extensive and accurate weather information, with a specific focus on those developing or operating PV power plants. The site’s solar and weather conditions have a direct impact on the performance throughout the entire lifecycle of a PV project—from site selection to design, financing, and power plant operations and maintenance. The following parameters describe these conditions: solar irradiance, air temperature, wind, humidity, ground albedo, and others. ### Satellite-based irradiance ### What for - Provides irradiance values reaching ground with the assumption of the absence of clouds and attenuation from aerosols (due to desert dust, smoke from wildfires, volcanoes, etc.). - Calculates the attenuation effect of clouds on solar irradiance and provides a continuous stream of long periods of data in sub-hourly resolution. - Allows having values from very recent periods in near real-time. ### How it works - Using an accurate sun position model, it calculates extraterrestrial irradiance and its variation throughout time, taking into account key factors like altitude, atmospheric optical depth, water vapor, and ozone concentrations. - Quantifies the presence of clouds from satellite data acquired by geostationary satellites on several spectral channels. - Combining the previous steps, Global Horizontal Irradiance (GHI) values are obtained as an outcome. ### Main outputs - Global Horizontal Irradiance, GHI - Global Horizontal Irradiance for clear-sky, GHIc - Spatial resolution: 250 m - Time granularity: Sub-hourly - Period: since the satellite launch date (oldest satellite available since 1994) ### Irradiance splitting ### What for - Global irradiance is decomposed into two main components: direct and diffuse. - Required for obtaining global tilted irradiance (GTI) and expected power output (PVOUT) at a later stage. ### How it works - Relationships between components are derived from radiative transfer or empirically from observations. - Based on the well-known relationship between the Global Horizontal, Direct Normal, and Diffuse clearness indices. ### Main outputs - Direct Normal Irradiance, DNI - Diffuse Horizontal Irradiance, DIF ### Meteo conditions ### What for - Provides meteo parameters like air temperature, wind, humidity, etc. - Used to know conditions required by PV simulation models to calculate energy conversion efficiency and estimation of system losses. ### How it works - Ingestion of data from numerical weather models (NWP) received by weather data centers, e.g. ECMWF. - Enhancement of original spatial resolution of the NWP inputs to a higher resolution by spatial disaggregation and use of Digital Elevation Models (DEM). ### Main outputs - Temperature, TEMP - Wind Speed, WS - Wind Direction, WD - Wind Gust, WG - Relative Humidity, RH - Precipitation, PREC - Snow water equivalent, SWE - Precipitable water, PWAT - Spatial resolution: 1km - 25km - Time granularity: hourly ### Ground albedo ### What for - Provides values of ground albedo for any site. - Albedo values are essential to calculate reflected irradiance accurately. ### How it works - Data from satellite instrument MODIS is used as the primary data source. - It is combined with data from NWP (Numeric Weather Prediction) models to prepare a gap-free for all kinds of land surfaces (including ephemeral snow). ### Main outputs - Ground albedo, ALB - Spatial resolution: 500 m - Time granularity: Daily - Period: since the year 2000 ### Model site-adaptation ### What for - If ground measurements are available on the project site and comply with the required length and quality requirements, the irradiance model can be adapted to achieve higher accuracy. - Information from other measured meteorological parameters can also be used to obtain a new long-term data stream that is more representative of the site. ### How it works - Adaptation of satellite-based GHI and DNI values. This method corrects the bias (systematic deviation) and fits the cumulative distribution functions. - Adaptation of the input parameters and data used in the solar radiation model. More complex parameters, such as Aerosol Optical Depth and/or Cloud Index, are adjusted using this approach. ### Main outputs - New multi-year time series with lower uncertainty. ### Time series and TMY ### What for - Collects all parameters affecting PV performance in one dataset for the entire available period. - Generates a summarized dataset of 8760 hourly values representing a "typical year". - Provides other summarized datasets according to P90, or any other PXX scenario. ### How it works - Generates TMY by selecting representative months based on statistical alignment and similarity of cumulative distribution functions (CDFs). - Parameters are weighted according to the application. ### Main outputs - Time series dataset. - Typical Meterological Year (TMY) datasets - Spatial resolution: 250 m - Time granularity: Sub-hourly, hourly ### 1-minute data generation ### What for - Able to estimate higher-resolution solar irradiance data of up to 1-minute granularity. - It helps understand full resource variability and achieve optimum PV plant design. ### How it works - Collects a database of high-resolution ground measurements of similar characteristics of the site of interest. - By applying Markov process methods, 1-minute stochastic profiles are generated. ### Main outputs - Stochastic 1-minute Time Series dataset of solar irradiance. ### Terrain features ### What for - Generates site-specific topography. - Provide insights to the PV simulator for the calculation of incident irradiance and energy yield. ### How it works - Processing of digital terrain models, a comprehensive terrain characterization is conducted for the site - Calculation of surface slope, and surface azimuth. Horizon data is generated by aggregating points from the surrounding terrain into a 360º orientation series around the site. ### Main outputs - Elevation (ELE) - Slope (SLO) - Surface azimuth (AZI) - Horizon (HOR) ## Designing and simulating PV plants It’s not just about accurately knowing the site conditions. You must also understand how the PV power plant will react under specific conditions. This means understanding and developing research on every energy conversion step from solar photons to electricity. How will outside conditions affect cell temperature and conversion efficiency? How does shading impact my specific field of modules? How much soiling shall I expect? How much loss is on the DC and AC sides? ### Incident irradiance ### What for - Calculates the energy collected by solar modules of a PV plant. - It is able to obtain incident irradiance for fixed and tracking systems of all kinds, azimuth, and orientation. - It has the capability to calculate bifacial module gains. ### How it works - Uses a high-resolution terrain model for calculation of the far shading effect. - Near shading effect can be applied through 3D modeling of nearby objects and buildings. PV plant self-shading is also calculated. - For the calculation of diffuse tilted irradiance, it combines isotropic and anisotropic sky models. - Takes into account the ground albedo to obtain gains from reflected irradiance at the front and rear side of the modules (raytracing model). - Applies the effect of soling and snow on the incident energy calculation. ### Main outputs - Global Tilted Irradiance, GTI. ### Expected soiling ### What for - Provides an estimate of the expected soiling loss on the PV plant due to dust deposition. ### How it works - Collects data on expected particles that are likely to lay on PV modules. - Add the natural effect of precipitation on the modules. - Calculates the expected soiling loss. ### Main outputs - Monthly values of soiling loss for the PV plant. ### Snow loss ### What for - Estimates snow accumulation on PV panels - Helps with the calculation of related energy production losses with higher accuracy ### How it works - Collects meteorological data from global reanalysis models - Estimates snow coverage on PV panels, accounting for accumulation, melting, and sliding ### Main outputs - Snow loss factor - Time granularity: monthly ### PV conversion ### What for - Gives the energy generated by the PV modules for each instant of time. - Able to run simulations for all types of PV modules, including all manufacturers and technologies. - It can provide estimates of system losses on the DC side components. ### How it works - Takes into account non-linear, voltage-current dependency or I-V curve (single diode model). - Applies the effect of cell temperature on the conversion efficiency. - Considers the PV plant string configuration to calculate the estimated output and takes into account mismatch due to different MPP operating points of modules connected. - Calculates expected heat losses in the combiner boxes, interconnections, and cables on the DC side. ### Main outputs - PV output, PVOUT (after PV conversion in DC) - Module temperature, TMOD. ### Inverter ### What for - Calculates the losses on the inverter when transforming DC into AC. ### How it works - Application of each type of inverter efficiency function (dependence of the inverter efficiency on the inverter load and inverter input voltage). - Use of high-granularity input data to provide more accurate results. ### Main outputs - PV output, PVOUT (after conversion from DC to AC). ### Power transmission ### What for - Calculates the system losses on the AC side components. ### How it works - Calculates expected heat losses in the combiner boxes, interconnections, and cables on the AC side. - Takes into account additional losses due to transformers and grid availability. ### Main outputs - PV output, PVOUT (after losses on the AC side). ## Analyzing solar power metrics To unlock the value of data, we need to understand the challenges that solar developers, asset managers, and operators face every day. This means running geographical and temporal analyses to obtain valuable information for PV plant stakeholders across all project stages: long-term yield analysis to unlock finance opportunities, short-term forecasting to operate in energy markets, performance assessment for O&M planning, and many other use cases. ### Variability and extremes ### What for - Characterizes variability of energy generation and site meteorology. - Helps understand extreme operating conditions for the PV plant. ### How it works - Statistical analysis covering interannual variability, seasonal, intraday, and sub-hourly variations. - Identification of extreme situations. ### Main outputs - Maximum and minimum values. - Key variability statistics. ### Uncertainty estimates ### What for - Enables calculation of expected deviation ranges of solar resource. - Helps make robust financial plans for PV projects. ### How it works - Collects extensive validation using reference measurements and models. - Analysis of contributing factors that lead to deviations, combining regional and site-specific analyses. ### Main outputs - Estimated uncertainty for solar irradiance inputs - Estimated uncertainty for PV simulation ### P50, P90 and other scenarios ### What for - Estimates expected energy during the PV plant lifetime. - Able to provide estimates for most expected and least expected scenarios. ### How it works - Analyzes full-time series of data available. - Based on extensive data validation, accounts for uncertainties across the yield calculation chain. ### Main outputs - Values for P50, P90, or any scenario. - Data uncertainty for specific sites. ### Data quality ### What for - Identifies non-valid data records using quality control procedures. - Detects possible issues with the sensors. ### How it works - Identification and correction of time shifts, time drifts, and other time-related issues based on testing diurnal symmetry, critical for all subsequent quality tests. - Detection of issues on solar irradiance data and flagging invalid values related to nighttime/daytime, artificial static values, breaking physical limits, and consistency of irradiance components. - Detection of issues on meteo data. - Detection of other specific data problems, such as instrument shading or tracker malfunction detection. ### Main outputs - Filtered dataset with flagged values. - Related recommendations for measuring instruments. ### Verification of PV components ### What for - Validates the technical characteristics of modules and inverters through expert checks. - Assures that PV designers work with precise values and accurate simulation results. ### How it works - The verification process involves completing required parameters, passing critical validations, and verifying the authenticity of datasheets, certificates, and lab reports. - All data must be provided by verified contributors to ensure credibility. ### Main outputs - Confidence class of each analyzed PV component ### Performance ### What for - Allows regular reporting of theoretical achievable production for existing PV plants. - Identifies underperforming situations on the PV plant. ### How it works - Calculation and analysis of key indicators (after filtering non-valid measured data records). - Characterization of differences between the measured and modeled data. ### Main outputs - Real vs. expected energy data comparison. - Performance ratio (PR). - Actual vs. average solar irradiation values. ### Short-term forecast ### What for - Provides energy estimations for the next hours and days. - Used for energy management purposes at the grid level. - Allows a better plan of O&M activities of solar power plants. - Energy systems designers use historical forecasts for battery sizing and optimization. ### How it works - Receives inputs from several Numerical Weather Prediction models(NPW) and selects the best stream of data based on historical comparisons. - Cloud Motion Vector models based on satellite imagery for predictions for the next hours’ horizon. ### Main outputs - 14-days energy predictions. - Subhourly updates. - Historical forecasts. ## Useful resources ### [Webinars](https://solargis.com/resources/webinars) Learn how to use Solargis solutions in your everyday job or explore current solar industry topics and insights. ### [Blog](https://solargis.com/resources/blog) Industry best practices, Solargis news, and product updates – all in one place. ### [Ebooks & Whitepapers](https://solargis.com/resources/ebooks) Free to download, our ebooks and whitepapers dive deeper into trending or demanded themes of the solar industry. -------------------------------------------------------------------------------- ## Solargis Data Accuracy: Validation Across 320+ Global Sites Source: https://solargis.com/technology/accuracy-and-validation # Our datasets and models are rigorously validated Explore the key findings from validation and independent studies that prove the accuracy and reliability of our solar energy platform. ## Accuracy of solar radiation The received irradiance represents the first step in the energy conversion process, as it defines the maximum theoretical amount of solar energy available before further losses are considered. Ensuring the accuracy of solar radiation modeling is therefore crucial for reliable energy yield predictions. ### Solargis validated with minimal bias in 320 sites ### What we did - Collect data from 320 publicly available ground stations measuring GHI. - Collect data from 235 publicly available ground stations measuring DNI. - Compare measurements with the same periods from our satellite-based irradiance model. ### Results we obtained - Mean of all biases for GHI was 0.5%, with a standard deviation of 3%. - Mean of all biases for DNI was 2.2%, with a standard deviation of 6%. ### Conclusions - High accuracy of Solargis irradiance satellite-based model. - The most extensive validation done so far by a solar data provider. ### Consistent accuracy across different climates ### What we did - Classify each validation site under the main categories of Köppen Geigen climate classification. - Sort all collected validation statistics for GHI and DNI accordingly by climate type ### Results we obtained We calculated the bias of modeled vs measured data and RMSD for all available stations, for both GHI and DNI series: - Bias follows expected probability for irradiance models for all climates. - RMSD for monthly, daily and hourly data showing consistent values. ### Conclusions - Solargis satellite-based model works consistently for all climates. - As expected by the nature of the models, it shows higher performance for arid and temperate climates. ### Albedo data accuracy proved with albedometers ### What we did - Solargis monthly albedo data was compared with ground-based measurements from albedometers. - The availability of measurements was limited to stations in North America. ### Results we obtained - The validation exercise demonstrated a mean bias of -0.01. ### Conclusions - Validation results demonstrate the high accuracy of Solargis albedo data for PV simulations. - While this validation exercise covers a relatively small number of locations, it represents a diverse range of climatic conditions. - Using monthly albedo values instead of constant values improves the accuracy of PV modeling. ### Higher accuracy with Raytracing + anisotropic sky ### What we did - Select diverse sites and configurations to cover a wide range of lighting conditions, including clear skies, overcast scenarios, and complex shading setups. - Compare Solargis ray-tracing algorithm with Radiance, a reference model known for its robust optical simulations. ### Results we obtained - Under clear sky conditions, Solargis and Radiance results demonstrated strong alignment. - In overcast scenarios, the outputs were equally consistent. - Complex shading and rear-side irradiance simulations highlighted minor deviations due to differences in ray-tracing configurations. ### Conclusions - The tests confirmed the accuracy of the Solargis ray-tracing algorithm and Perez sky model. - The model works consistently for both the front and rear sides of modules. - Major improvement in the accuracy of simulations of systems using bifacial technology in comparison to other existing approaches. ### Most accurate irradiance model according to IEA ### What we did - Solargis participated in the "Worldwide Benchmark of Modeled Solar Irradiance Data 2023," conducted as part of an International Energy Agency (IEA) task within the Photovoltaic Power Systems Programme (PVPS). - This benchmark report evaluated and compared the performance of ten distinct solar irradiance models against high-quality ground-based measurements. ### Results obtained by IEA - The IEA study looked at 129 ground stations after data quality control. This included visual inspections covering aspects such as shading assessment, closure test, AM/PM symmetry check for GHI, and calibration check using the clear-sky index. - A set of complete performance metrics was calculated, including bias, RMSD, KSI, and other indicators to show the relative frequency of exceedance situations and a combined performance index. ### Conclusions - The benchmark results show “noticeable deviations in performance between the various modeled data sets” that were evaluated. - In particular, deviation metrics of data sets based mainly on geostationary satellite imagery are closer to each other than to the NWP-based and polar satellite-based data sets. Specifically, the report mentions that “lowest average deviation metrics are often achieved by a single data set (Solargis)”. - The results align with trends observed in earlier independent studies. ## Accuracy of environmental conditions Environmental conditions define the operating environment in which the system functions. Therefore, accurate validation of temperature, wind, humidity, and other meteorological parameters is essential for assessing efficiency losses' accuracy and estimating long-term degradation effects. ### Air temperature data validation ### What we did - Collect data from reference meterological stations from over 11,000 sites across diverse climate zones. - Compare air temperature at 2 meters (TEMP) values included in Solargis datasets for the same sites, and calculate the bias of modeled vs measured data for all available stations. ### Results we obtained - For TEMP, we calculated a mean bias of -0.1°C (24 h) and standard deviation of 1°C. - In general, night-time deviations are slightly higher, but daytime values —relevant for solar power generation— are estimated with higher accuracy. ### Conclusions - The methodology for deriving meteorological parameters in Solargis time series, which integrates global NWP models with advanced post-processing techniques to enhance temperature data resolution, has been successfully validated. - Solargis air temperature are highly reliable and well-suited for calculating PV cell temperature, a critical factor in assessing thermal losses in PV systems. - Although air temperature data derived from global NWP models represent broader regions and may not fully capture localized microclimates, they consistently demonstrate high reliability, making them highly effective for PV simulations. ### Wind speed data validation ### What we did - Collect data from reference meterological stations from over 11,000 sites across diverse climate zones. - Compare wind speed (WS) values included in Solargis datasets for the same sites, and calculate the bias of modeled vs measured data for all available stations. ### Results we obtained - For WS, we calculated a mean bias of 0.1 m/s (24 h) and standard deviation of 1.1 m/s. - In general, night-time deviations are slightly higher, but daytime values —relevant for solar power generation— are estimated with higher accuracy. ### Conclusions - Solargis wind speed data are highly reliable and well-suited for calculating PV cell temperature in combination with other parameters like air temperature and solar radiation. - Although wind speed data from global NWP models represent broader regions and may not fully capture localized microclimates, they consistently demonstrate high reliability, making them highly effective for PV simulations. ### Relative humidity data validation ### What we did - Collect data from reference meteorological stations from over 11,000 sites across diverse climate zones. - Compare relative humidity (RH) values included in Solargis datasets for the same sites, and calculate the bias of modeled vs measured data for all available stations. ### Results we obtained - For RH, the calculated mean bias is 0% (24 h) and standard deviation of 7% reflect solid agreement with ground measurements. ### Conclusions - Solargis relative humidity data provides accurate inputs for further calculations of the expected soiling loss using physical models. - Although relative humidity data from global NWP models represent broader regions and may not fully capture localized microclimates, they consistently demonstrate high reliability, making them highly effective for PV simulations. ## Accuracy of energy conversion losses Accurate simulation algorithms are essential for optimizing PV system performance during the design process. Critical factors include the quality of the input data used to model energy conversion—from solar radiation to DC in PV modules, from DC to AC in inverters—and the estimation of energy losses during subsequent transmission and distribution. ### Verification of PV modules' technical specifications ### What we did - Collect technical specifications sheets of the most popular PV modules in the industry. - Check general data, mechanical characteristics, electrical characteristics, diode model characteristics, optical characteristics, PV module characteristics, and reference conditions, among other parameters. ### Result we obtained - After verification of collected information, Solargis experts found discrepancies and missing information on datasheets, certificates, and lab reports. - The impact of running simulations using unverified PV modules can be significantly high. ### Conclusions - The accuracy and reliability of PV module’s technical details are key to assuring precise PV simulation results. - A rigorous component verification process is required, assigning a confidence class to each PV module through expert checks. - The verification process involves completing the required parameters, passing critical validations, and verifying the authenticity of datasheets, certificates, and lab reports. ### Verification of inverters' technical specifications ### What we did - Collect technical specifications sheets of the most popular solar inverters in the industry. - Check general data, input and output characteristics, operating performance, mechanical characteristics, efficiency, and advanced functionality, among other specific parameters. ### Result we obtained - After verification of collected information, Solargis experts found discrepancies and missing information on datasheets, certificates, and lab reports. - The impact of running simulations using unverified inverters can be significantly high. ### Conclusions - The accuracy and reliability of inverter’s technical details are key to assuring precise PV simulation results. - A rigorous component verification process is required, assigning a confidence class to each inverter through expert checks. - The verification process involves completing the required parameters, passing critical validations, and verifying the authenticity of datasheets, certificates, and lab reports. ### More accurate results with higher time resolution inputs ### What we did - Collect high-resolution solar datasets for 20 sites covering diverse climate zones. - Compare calculated inverter’s clipping losses and energy yield using solar irradiance input data at time resolutions of 1-minute, 15-minute, and 60-minute intervals. ### Results we obtained - Tropical sites show up to 2.5% overestimation of annual production when using 60-minute data instead of 1-minute. - Larger values were obtained for DC/AC ratios of 1.5. ### Conclusions - Using 1-minute resolution data in PV system design enhances accuracy by minimizing production overestimation and providing a more precise assessment of clipping losses. - The development and use of accurate 1-minute models are highly recommended for optimizing the DC/AC ratio, analyzing grid stability, sizing energy storage systems, and ensuring grid compliance. -------------------------------------------------------------------------------- ## Solargis Collaterals Source: https://solargis.com/resources/collaterals # Collaterals ## Brochures & leaflets - Company Brochure - Most accurate data and software for the entire PV power plant lifecycle - Solargis Prospect & Evaluate - Overview of solutions for PV power plant development phase - Solargis Monitor & Forecast - Overview of solutions for operational phase of PV power plant - Solargis Evaluate - extended version - Solar and meteo data, PV design & energy yield simulation - in one cloud-based solution - Solargis Analyst - Software for effective visualization, quality management, - and analysis of solar data - PV Components Catalog - Independent and trusted platform for PV component technical specifications - High resolution weather data - High resolution weather data for storage application - and advanced loss assessment - Surface albedo - Surface albedo and yield assessment - for bifacial PV projects - Model site adaptation - Model site adaptation: Minimize the underperformance risk - of your PV assets - PV spatial smoothing - PV spatial smoothing: Tackle the PV production ramps - for the entire power plant field - Variability analysis - Variability analysis: Simulate the power output with 1-minute - historical solar and meteorological data ## Success stories - Success story - candi solar - Solargis Evaluate - candi solar scales its business sustainably with Solargis - data services - Success story - candi solar - Solargis Monitor - candi solar optimizes portfolio performance with Solargis Monitor - Success story - Convergent Energy and Power - Site adaptation of high resolution satellite data using ground-based - measurements from GroundWork Renewables, Inc. - Success story - Shift Energy Japan - SEJ integrates Solargis Monitor across 450 systems in Japan - Success story - Cleantech Solar - Solargis validates ground-based measurements - for Cleantech Solar - Success story - Iberdrola - Iberdrola rolls out Solargis Analyst software to support - global solar operations ## [candi solar optimizes portfolio performance with Solargis Monitor](https://solargis.com/resources/success-stories/candi-solar-solargis-monitor) "Solargis’ bankable and credible data underpins our decision-making throughout the project lifecycle." ## [candi solar turned to Solargis Evaluate to provide robust and accurate irradiation data](https://solargis.com/resources/success-stories/candi-solar) “We had a suspicion of overestimation in the solar resource and when we downloaded our first Solargis file, we saw it was 3-10% less than expected.” ## [Site adaptation of high resolution satellite data using ground-based measurements](https://solargis.com/resources/success-stories/site-adaptation-of-high-resolution-satellite-data-using-ground-based-measurements) Convergent turned to Solargis and GroundWork for highly accurate and site-specific resource assessments for its solar-plus-storage systems to better understand expected performance and maximize confidence for project stakeholders. ## [Shift Energy Japan uses Solargis Monitor to track and assess its projects’ financial performance](https://solargis.com/resources/success-stories/shift-energy-japan-uses-solargis-monitor-to-track-and-assess-its-projects-financial-performance) Shift Energy Japan uses Solargis Monitor as a replacement for the scarce, publicly available data to track and assess its projects’ financial performance and deliver key insights into operations. ## [Iberdrola rolls out Solargis Analyst software to support global solar operations](https://solargis.com/resources/success-stories/iberdrola-rolls-out-solargis-analyst-software-to-support-global-solar-operations) Solargis Analyst is helping Iberdrola make more informed investment and financial decisions, which are bolstered by secure and granular data. ## [Solargis provides data for Apex Clean Energy’s 10GW U.S. solar portfolio](https://solargis.com/resources/success-stories/apex-clean-energy) Apex Clean Energy has bolstered its bifacial projects with Solargis’ albedo data, and to optimize its storage assets, it has invested in 1- and 5- minute data provided by Solargis. ## [SunSource is able to confidently verify its own GHI and meteorological data](https://solargis.com/resources/success-stories/sunsource-energy) With greater insight into irradiance levels, SunSource Energy is able to confidently bid for contracts, while ensuring that its bifacial and storage assets are designed and operated to the highest standard. ## [Solargis assessment supports optimized asset value for First Solar](https://solargis.com/resources/success-stories/first-solar) At all 10 projects, Solargis irradiation data closely matched on-site measurements, giving First Solar and other project stakeholders full confidence in the accuracy of Solargis estimates. ## [Solargis validates ground-based measurements for Cleantech Solar](https://solargis.com/resources/success-stories/cleantech-solar) Solargis supported Cleantech Solar with API-based daily irradiation estimates, facilitating strategic decision making by the management to increase financial performance.