The Solcast dataset has been extensively validated, comparing Solcast data with publicly available surface measurements from around the world, across all climate types except polar climates.
The following tables present the summary error statistics and uncertainty in the Solcast dataset. The validation compared Solcast data with ground measurements from BSRN stations.
|Bias||0.00%||0.21%||0.56%||Tendency to over or underestimate irradiance, on average|
|Bias Std. Dev||2.07%||4.69%||5.07%||Measure of the variation in bias|
|MAE (hourly)||11.67%||24.47%||24.70%||Mean absolute error, the average size of error between measurements and satellite estimate|
|RMSE (hourly)||18.32%||41.27%||37.84%||Root mean square error, a measure of error that weights larger errors more heavily|
The most common statistical indicators in the solar industry used to evaluate solar radiation models fall into two categories; bias measures and error measures.
Bias measures are important for long term energy yield calculations, providing a base to estimate the accuracy of energy simulations by understanding a possible error in the long-term estimate. Mean bias error provides information about the aggregate difference between the Solcast dataset compared against ground observations. It is a measure of the dataset's systematic tendency to over- or underestimate the solar resource. Deviation of bias error indicates the spread of this error. It indicates the likelihood of the bias error at a specific site occurring within a confidence interval.
Error measures are important for short term variability work. They help to develop an understanding of the accuracy of operational calculations based on energy simulations, covering recent performance and evaluation, equipment calibration, plant monitoring and forecasting. Mean absolute error (MAE) measures the average magnitude of the difference between the ground measurements and the model. Root mean squared error (RMSE) is another measure of these differences, but a uses quadratic weighting. This means larger errors have a disproportionately larger effect on RMSE, and consequently this measure is very sensitive to large errors.
For validation of satellite-derived irradiance estimates, granular time series data from uniform, high quality, well maintained, long-period surface measurement sites are required. The Baseline Surface Radiation Network (BSRN) is used in these validation statistics, selected to ensure the consistency of sensor specifications and the measurement calibration and quality control approach. A number of BSRN stations are equipped to measure solar and atmospheric radiation, using sensors of the highest available accuracy and a high sample rate.
The BSRN network was set up specifically to provide high-quality observational data to the scientific community, making it ideal for validating satellite-based estimates of solar radiation. The use of a single, research-grade dataset for validation ensures the below statistics are of the highest possible quality and an accurate representation of the Solcast database.
The inclusion of up to approximately 200 stations rather than 46 was considered, however a single global network was selected for the reason of consistency of sensor specifications and the measurement calibration and quality control approach. An investigation of a number of other, smaller and regional sensor networks found considerable unphysical inconsistencies of data properties within the same site - immediately invalidating the use of such data. Further, to support the transparency and replicability of these results, the use of publicly available sites was preferred.
Factors Affecting Uncertainty Measures
The Solcast satellite model is spatially and temporally consistent, and thus validation across a geographic or climate region provides a robust indication of the model uncertainty in geographically or climatologically comparable regions elsewhere.
While these error measures are representative of most sites in most situations, sites with the following characteristics may experience model error to be higher than expected; significant changes in local topography affecting true horizon (Solcast does not apply any horizon shading), dynamic and high levels of aerosols, close proximity to large bodies of waters, equatorial humid climates, or dynamic surface albedo (sites with rapid changes in snow cover).
The Solcast API Toolkit
Solcast takes on the many challenges of producing live and forecast solar data, so that you don’t have to. And that means making the data as easy to access, validate and integrate as possible, which is made possible through our API Toolkit. We provide instant access to live and forecast data products via this web interface, which is free to try. These include direct estimates of global, direct and diffuse solar radiation, as well as PV power output.