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

翻译：概率电价预测近来在电力交易中受到关注，因为基于此类预测做出的决策能比仅依赖点预测带来显著更高的利润。与此同时，由于没有模型是完美的，平均化通常能提升预测性能，因此开发预测分布组合方法也成为研究热点。本文探讨了是否使用CRPS学习（一种最小化连续排序概率评分（CRPS）的新型加权技术）能在日前投标中产生最优决策。为此，我们利用德国EPEX市场的每小时日前电价进行实证研究。研究发现，提高集成模型的多样性对预测准确性有积极影响。同时，尽管CRPS学习能显著提升预测精度，但其相较于等权分布聚合方法带来的更高计算成本并未被更高利润所抵消。