Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression

Arkadiusz Lipiecki, Bartosz Uniejewski and Rafał Weron

Energy Economics, 2024, vol. 139, issue C

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 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.

Keywords: Day-ahead electricity price; Quantile regression; Conformal prediction; Isotonic distributional regression; Combining probabilistic forecasts; Shapley values (search for similar items in EconPapers)
JEL-codes: C21 C22 C45 C53 Q47 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:139:y:2024:i:c:s014098832400642x

DOI: 10.1016/j.eneco.2024.107934

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