Our technology is based on scientific research applied and validated by the solar industry.

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)

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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

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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

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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

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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.

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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

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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.

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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)

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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.

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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.

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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

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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.

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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).

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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).

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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.

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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

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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.

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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.

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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

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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.

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.