Solcast vs Solargis
Solargis is a Slovak company specialising in historical irradiance data, consulting, and increasingly in PV software. The company’s solar data was first developed around 2008 to 2011.
Solargis is a source of satellite-derived irradiance data.
Solcast and Solargis are the most extensively and independently validated global datasets available.
Compared with Solargis, Solcast has slightly lower uncertainty, and is generally cheaper and easier to use. Solcast focuses solely on accurate, high-spec data, whereas Solargis spreads its focus across software, data and consulting.
Data Features and Capabilities
Solcast | Solargis | |
|---|---|---|
Free trial with instant access and data download? | | |
Download wait time | 1-3 seconds | 5 minutes to 48 hours |
Comprehensive, global, independent validation | | |
Finest time resolution of satellite-based irradiance | 5 minutes | 15 minutes |
Real time data available? | | |
| |
Inputs and Algorithms
The SolarGIS 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 models. The input satellite imagery is similar, with SolarGIS using approx. 3-4km grids and Solcast using approx. 1-2km grids. Both datasets downscale solar irradiance parameters and can perform far horizon shading, Solcast to 90m and SolarGIS 250m. SolarGIS and Solcast both offer near-global data coverage (polar and remote ocean regions are excluded or have lower data quality). The SolarGIS database is compiled using high-resolution new satellites, and lower-resolution, pre-21st century satellites. Data coverage begins at 1994/1999/2006 depending on location. Solcast only uses data from recent generation geostationary meteorological satellites (GMS). We do not use data prior to 2007 due to climate change and satellite data quality issues. This maximises data quality and validity, while still providing 15+ years of data history from which to sample for interannual variability.
Validation and Accuracy
Solargis and Solcast are both extensively and independently validated, globally at 200+ sites. The differences in accuracy and uncertainty are small, with Solcast performing slightly better on GHI bias spread, GHI RMSE and DNI RMSE, and Solargis performing slightly better on DNI bias spread.
Meta analysis of large global validation studies: GHI results
Solcast | SolarGIS (2023) | SolarGIS (2019) | |
|---|---|---|---|
Performed by | DNV | IEA PVPS | Solargis |
Year published | 2023 | 2023 | 2019 |
No. of sites | 207 | 119 | 228 |
Mean Bias | +0.33% | +0.07% | +0.3% |
Bias Std. Dev. | ±2.47% | ±2.93% | ±3.00% |
80% CI Bias (10% to 90%) | -2.84% to 3.50% | -3.69% to +3.83% | -3.6% to +4.2% |
90% CI Bias (5% to 95%) | -3.74% to 4.40% | -4.76% to +4.90% | -4.7% to +5.3% |
Mean nMAD (nMAE) | 10.33% | 9.03% | Not Published |
Std. Dev. nMAD (nMAE) | ±3.72% | ±2.86% | Not Published |
Mean nRMSD (nRMSE) | 15.99% | 14.09% | 16.08% |
Std. Dev. nRMSD (nRMSE) | ±5.74% | ±4.12% | ±6.1% |
Meta analysis of large global validation studies: DNI results
Solcast | SolarGIS (2023) | SolarGIS (2019) | |
|---|---|---|---|
Performed by | DNV | IEA PVPS | Solargis |
Year published | 2023 | 2023 | 2019 |
No. of sites | 117 | 119 | 166 |
Mean Bias | +1.50% | -0.42% | +2.2% |
Bias Std. Dev. | ±5.75% | ±4.90% | ±5.3% |
80% CI Bias (10% to 90%) | -5.87% to 8.86% | -6.70% to +5.87% | -4.6% to +9.0% |
90% CI Bias (5% to 95%) | -7.96% to 10.95% | -8.48% to +7.65% | -6.5% to +10.9% |
Mean nMAD (nMAE) | 19.97% | 18.66% | Not Published |
Std. Dev. nMAD (nMAE) | ±5.94% | ±5.08% | Not Published |
Mean nRMSD (nRMSE) | 31.51% | 28.30% | 32.10% |
Std. Dev. nRMSD (nRMSE) | ±9.99% | ±8.71% | ±11.30% |
Solargis. (2023, November 27). Validation and uncertainty of solar resource data.
Solargis. (2019, November). Validation Report: Global Solar Atlas 2.0. World Bank - ESMAP.
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
