Work

Loss functions in regression models: Impact on profits and risk in day-ahead electricity trading

Year: 2025

Type: article

Abstract: We study the impact of the loss function used to estimate the parameters of a regression-type model on profits and risk in day-ahead electricity trading. To provide practical insights, we consider a strategy that incorporates battery storage and includes realistic operating costs in the calculation of revenues. Using 2021-2024 data from the German market as the testing ground, we provide evidence that minimizing a loss function that combines absolute errors with a quadratic penalty for price spread predictions of the opposite sign is the preferred option. Forecasts based on the introduced directional loss function repeatedly and in the majority of cases yield trading decisions that outperform those based on predictions from models estimated using squared, absolute, percentage, or asymmetric losses, as measured by the Sharpe ratio and profits per trade.

Source: Energy Economics

DOI: 10.1016/j.eneco.2025.108596

Authors: Tomasz Serafin, Rafał Weron

Institution: Wrocław University of Science and Technology

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: hybrid

Estimated APC paid: $4,280