Summarizing ensemble NWP forecasts for grid operators: Consistency, elicitability, and economic value

Abstract

On the one hand, grid integration of solar and wind power often requires just point (as opposed to probabilistic) forecasts at the individual plant level to be submitted to grid operators. On the other hand, solar and wind power forecasting can benefit greatly from dynamical ensemble forecasts from numerical weather prediction (NWP) models. Combining these two facts, this study is concerned with drawing out point forecasts from NWP ensembles. The scoring function for penalizing bad forecasts (or equivalently, rewarding good forecasts), in most scenarios, is specified by grid operators ex ante. The optimal point forecast therefore should be an elicitable functional of the predictive distribution, for which the specified scoring function is strictly consistent. Stated differently, the optimal way to summarize a predictive distribution depends on how the point forecast is to be penalized. Using solar irradiance forecasts issued by the ECMWF’s Ensemble Prediction System, the statistical theory on consistency and elicitability is validated empirically with extensive data. The results show that the optimal point forecasts elicited from ensembles have constantly higher accuracy than the best-guess forecasts, regardless of the choice of scoring function. Surprisingly, however, the correspondence between the two types of goodness of forecasts, namely, quality and value, is neither linear nor monotone, but depends on the penalty triggers and schemes specified by grid operators. In other words, using the optimally elicited forecasts, in many scenarios, would lead to lower economic values.