A1331
Title: Stealing accuracy: Predicting day-ahead electricity prices with temporal hierarchy forecasting (THieF)
Authors: Rafal Weron - Wroclaw University of Science and Technology (Poland) [presenting]
Arkadiusz Lipiecki - Wroclaw University of Science and Technology (Poland)
Kaja Bilinska - Wroclaw University of Science and Technology (Poland)
Nikolaos Kourentzes - University of Skovde (Sweden)
Abstract: The purpose is to introduce the concept of temporal hierarchy forecasting (THieF) in predicting day-ahead electricity prices and show that reconciling forecasts for hourly products, 2- to 12-hour blocks, and baseload contracts significantly (up to 13\%) improves accuracy at all levels. These results remain consistent throughout a challenging 4-year test period (2021-2024) in the German power market and across model architectures, including linear regression, a shallow neural network, gradient boosting, and a state-of-the-art transformer. Given that (i) trading of block products is becoming more common and (ii) the computational cost of reconciliation is comparable to that of predicting hourly prices alone, it is recommended to use it in daily forecasting practice.
