Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression
Arkadiusz Lipiecki, Bartosz Uniejewski, Rafał Weron
Abstract
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 additive explanations. 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. • Postprocessing can effectively transform point into probabilistic forecasts. • Further accuracy gains can be obtained by combining predictive distributions. • IDR contributes the most to the ensemble distribution, as measured by SHAP. • Ensemble outperforms state-of-the-art Distributional Deep Neural Networks.