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Probabilistic price forecasting has recently gained attention in powertrading because decisions based on such predictions can yield significantlyhigher profits than those made with point forecasts alone. At the same time,methods are being developed to combine predictive distributions, since no modelis perfect and averaging generally improves forecasting performance. In thisarticle we address the question of whether using CRPS learning, a novelweighting technique minimizing the continuous ranked probability score (CRPS),leads to optimal decisions in day-ahead bidding. To this end, we conduct anempirical study using hourly day-ahead electricity prices from the German EPEXmarket. We find that increasing the diversity of an ensemble can have apositive impact on accuracy. At the same time, the higher computational cost ofusing CRPS learning compared to an equal-weighted aggregation of distributionsis not offset by higher profits, despite significantly more accuratepredictions.

Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?