Expectile regression averaging method for probabilistic forecasting of electricity prices

2024/02/12 by Joanna Janczura, Janczura, Joanna · 2 citations
Engineering · #Applications (stat.AP) #Energy Load and Power Forecasting #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.2402.07559

openalex publication_date 2024/02/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper we propose a new method for probabilistic forecasting of electricity prices. It is based on averaging point forecasts from different models combined with expectile regression. We show that deriving the predicted distribution in terms of expectiles, might be in some cases advantageous to the commonly used quantiles. We apply the proposed method to the day-ahead electricity prices from the German market and compare its accuracy with the Quantile Regression Averaging method and quantile -- as well as expectile-based historical simulation. The obtained results indicate that using the expectile regression improves the accuracy of the probabilistic forecasts of electricity prices, but a variance stabilizing transformation should be applied prior to modelling.

Cited by

• Variance Stabilizing Transformations for Electricity Price Forecasting in Periods of Increased Volatility
• The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Markets

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