Solcast vs PVGIS SARAH
The SARAH dataset available via PVGIS is a specification-limited free source of medium-quality satellite-derived irradiance data, limited to Europe Africa and the Middle East, which provides data that ends in 2020. Compared to PVGIS SARAH, Solcast is bankable, has lower uncertainty, is global, is updated in real time, and offers more data features.
About PVGIS SARAH
SARAH (Surface Solar Radiation Data Set - Heliosat) is a dataset created by CMSAF/EUMETSAT/DWD. The dataset, which runs through to the end of 2020, is regionally limited to Europe, Africa and the Middle East.
Data Features and Capabilities
Solcast | PVGIS | |
|---|---|---|
Free trial with instant access and data download? | | |
Download wait time | <1-30 seconds | 1 minute |
Comprehensive, global, independent validation | | |
Satellite based estimation | | |
Global Coverage | | |
Resolution of satellite data used | 1-2 km | 2-5 km |
15+ years of satellite data at full temporal resolution | | |
| |
Inputs and Algorithms
The PVGIS SARAH and Solcast methodology is relatively similar. Both are semi-empirical and are satellite-derived. Both rely on validated, published models to build a clear sky model, and use proprietary cloud detection. The PVGIS-SARAH cloud model has a lower resolution, based around a 5 km grid, compared to Solcast's upres 90 metre resolution. The cloud detection algorithm only processes one image per hour, instead of the native satellite time step. This large time step between satellite images can provide misleading information on cloud formation, tracking, and opacity. Solcast has real-time data available, whereas PVGIS SARAH ends in 2020.
Validation and Accuracy
Meta analysis of Europe and Africa validation results: GHI results
Solcast | PVGIS (SARAH 2.1) | PVGIS (SARAH 2.1) | |
|---|---|---|---|
Performed by | DNV | IEA PVPS | Yang & Bright 2020 |
Year published | 2023 | 2023 | 2020 |
No. of sites | 73 | 54 | 20 |
Mean Bias | +0.26% | +0.45% | +0.76% |
Bias Std. Dev. | ±2.17% | ±6.60% | ±12.44% |
Mean nMAD (nMAE) | 10.92% | 11.04% | Not Published |
Mean nRMSD (nRMSE) | 16.97% | 16.88% | 28.24% |
Meta analysis of Europe and Africa validation results: DNI results
Solcast | PVGIS (SARAH 2.1) | |
|---|---|---|
Performed by | DNV | IEA PVPS |
Year published | 2023 | 2023 |
No. of sites | 48 | 54 |
Mean Bias | +0.10% | +1.77% |
Bias Std. Dev. | ±5.33% | ±13.19% |
Mean nMAD (nMAE) | 18.71% | 24.96% |
Mean nRMSD (nRMSE) | 29.48% | 35.23% |
References
EU Science Hub (2023). PVGIS data sources & calculation methods: Validation of the satellite-based solar radiation data
Yang, D., 2018. A correct validation of the national solar radiation data base (NSRDB). Renewable and Sustainable Energy Reviews, 97, pp.152-155.
Yang, D. and Bright, J.M., 2020. Worldwide validation of 8 satellite-derived and reanalysis solar radiation products: A preliminary evaluation and overall metrics for hourly data over 27 years. Solar Energy, 210, pp.3-19.
Forstinger, A., et al. (2023). Worldwide benchmark of modelled solar irradiance data (2023 PVPS Task 16): Solar resource for high penetration and large-scale applications. ResearchGate.
Cuevas-Agulló, E., et al. (2023). A new global high-resolution solar resource dataset. Zenodo.
Historic Data Products
Time Series
The complete suite of irradiance and weather data required for effective monitoring, operation, and forecasting at your large-scale solar farm.
Typical Meteorological Year (TMY)
The complete suite of irradiance and weather data required for effective monitoring, operation, and forecasting at your large-scale solar farm
