A universal benchmarking method for probabilistic solar irradiance forecasting

摘要

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.