A1289
Title: Electricity price forecasting in the day-ahead market: Averaging forecasts vs breakpoint detection
Authors: Piotr Zaborowski - Wroclaw University of Science and Technology (Poland) [presenting]
Rafal Weron - Wroclaw University of Science and Technology (Poland)
Abstract: The profitability of a battery energy storage system (BESS) in the day-ahead market is determined by the precise timing of buying (charging) and selling (discharging) electricity. The latter requires reliable electricity price forecasts for the next day. Two approaches are compared using regression-based models. The first averages forecasts across calibration windows of varying lengths, balancing short-term responsiveness with long-term stability. The second uses structural break detection with the pruned exact linear time (PELT) algorithm, calibrating models only within homogeneous segments. The approaches are evaluated using both statistical (MAE, RMSE) and economic criteria (average opportunity cost of a BESS trading strategy and the Sharpe ratio) on four European day-ahead markets: Germany, Finland, Poland, and Spain. The test period includes the COVID-19 pandemic and the 2022 energy crisis. The results show that averaging-based models outperform breakpoint-based approaches in both accuracy and profitability.
