A universal benchmarking method for probabilistic solar irradiance forecasting

Abstract

Probabilistic solar irradiance forecasting is often benchmarked using the clear-sky persistence ensemble (PeEn). By comparing the continuous ranked probability score (CRPS) of a forecasting model to that of PeEn, the skill score can be obtained. Such skill score can be interpreted as the percentage improvement over the baseline model—PeEn. However, the CRPS of PeEn depends heavily on the model parameters and forecast setup, e.g., the number of ensemble members. The skill score is meant to provide a possibility for universal forecast comparison, but because of the different PeEn implementations, the score can be hard, if not impossible, to interpret. On this point, the complete-history PeEn (CH-PeEn) is herein proposed as a universal benchmarking method for probabilistic solar forecasting. CH-PeEn utilizes the entire history of measurements, and forms empirical distributions of the forecast clear-sky index that only depend on the time of day. The CRPS calculated based on CH-PeEn only depends on the location and temporal resolution of the data, not on forecast horizon nor lead time. Hence, CH-PeEn can lead to a near unique CRPS, and such uniqueness greatly improves the interpretability of the skill scores.