Statistical learning for NWP post-processing: A benchmark for solar irradiance forecasting

The share of solar power in the global and local energy mixes has increased dramatically in the past decade. Consequently, there has been a significant rise in the interest for solar power forecasting, for different time horizons, ranging from few minutes to seasons. For day-ahead forecasts, combination of Numerical Weather Prediction (NWP) models and post-processing algorithms is the most popular approach. Many recent publications have proposed innovative NWP post-processing methods. However, because different works use different datasets, metrics, and even cross-validation methods, it is rarely possible to fairly compare results across several papers.