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The increased usage of solar energy places additional importance on forecastsof solar radiation. Solar panel power production is primarily driven by theamount of solar radiation and it is therefore important to have accurateforecasts of solar radiation. Accurate forecasts that also give information onthe forecast uncertainties can help users of solar energy to make better solarradiation based decisions related to the stability of the electrical grid. Toachieve this, we apply statistical post-processing techniques that determinerelationships between observations of global radiation (made within the KNMInetwork of automatic weather stations in the Netherlands) and forecasts ofvarious meteorological variables from the numerical weather prediction (NWP)model HARMONIE-AROME (HA) and the atmospheric composition model CAMS. Thoserelationships are used to produce probabilistic forecasts of global radiation.We compare 7 different statistical post-processing methods, consisting of twoparametric and five non-parametric methods. We find that all methods are ableto generate probabilistic forecasts that improve the raw global radiationforecast from HA according to the root mean squared error (on the median) andthe potential economic value. Additionally, we show how important thepredictors are in the different regression methods. We also compare theregression methods using various probabilistic scoring metrics, namely thecontinuous ranked probability skill score, the Brier skill score andreliability diagrams. We find that quantile regression and generalized randomforests generally perform best. In (near) clear sky conditions thenon-parametric methods have more skill than the parametric ones.

Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation