Operational decisions relying on predictive distributions of electricity prices can result in significantly higher profits compared to those based solely on point forecasts. However, the majority of models developed in both academic and industrial settings provide only point predictions. To address this, we examine three postprocessing methods for converting point forecasts of day-ahead electricity prices into probabilistic ones: Quantile Regression Averaging, Conformal Prediction, and the recently introduced Isotonic Distributional Regression. We find that while the latter demonstrates the most varied behavior, it contributes the most to the ensemble of the three predictive distributions, as measured by Shapley values. Remarkably, the performance of the combination is superior to that of state-of-the-art Distributional Deep Neural Networks over two 4.5-year test periods from the German and Spanish power markets, spanning the COVID pandemic and the war in Ukraine.

中文翻译：

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对点预测进行后处理，以便对前一天的电价进行概率预测：使用等渗分布回归的好处


与仅基于点预测的运营决策相比，依赖于电价预测分布的运营决策可以带来更高的利润。但是，在学术和工业环境中开发的大多数模型都仅提供点预测。为了解决这个问题，我们研究了三种后处理方法，用于将前一天电价的点预测转换为概率预测：分位数回归平均、共形预测和最近引入的等渗分布回归。我们发现，虽然后者表现出最多样化的行为，但它对三种预测分布的集成贡献最大，如 Shapley 值所测量的那样。值得注意的是，在德国和西班牙电力市场的两个 4.5 年测试期间，该组合的性能优于最先进的分布式深度神经网络，跨越了 COVID 大流行和乌克兰战争。