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

TL;DR

This paper investigates whether CRPS-based weighting of predictive distributions improves decision-making in day-ahead electricity bidding, finding that increased ensemble diversity enhances accuracy but does not necessarily lead to higher profits.

Contribution

It introduces and empirically evaluates CRPS learning for combining probabilistic forecasts in electricity markets, comparing its effectiveness to simple averaging.

Findings

Ensemble diversity improves forecast accuracy.

CRPS learning yields more accurate predictions.

Higher computational cost does not translate into increased profits.

Abstract

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.