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

• Authors: Weronika Nitka , Rafał Weron

Abstracts

EN

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.

Keywords

EN

decision support; day-ahead electricity bidding; predictive distribution; combining forecast; CRPS learning

• Publisher: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
• Journal: Operations Research and Decisions
• Year: 2023
• Volume: 33
• Issue: 3

Dates

published

2023

Contributors

author

Weronika Nitka

• Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, Wrocław, Poland

author

Rafał Weron

• Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, Wrocław, Poland

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Identifiers

DOI

10.37190/ord230307

Biblioteka Nauki

27315321