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HomeTechnology
Accuracy of solar radiationAccuracy of environmental conditionsAccuracy of energy conversion losses

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.

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.

Read more in our knowledge base ->

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.

 

Download the report ->

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.

Read more in our knowledge base ->

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.

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.

 

Download the report ->

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.

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.

 

Read more in our knowledge base ->

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.

Read more in our knowledge base ->

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.

Read more in our knowledge base ->

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.

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. 

Read more in PV components catalog ->

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.

Read more in PV components catalog ->

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.