[2601.02856] Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning

Computer Science > Machine Learning arXiv:2601.02856 (cs) [Submitted on 6 Jan 2026 (v1), last revised 25 Mar 2026 (this version, v2)] Title:Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning Authors:Btissame El Mahtout, Florian Ziel View a PDF of the paper titled Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning, by Btissame El Mahtout and Florian Ziel View PDF HTML (experimental) Abstract:Precise day-ahead forecasts for electricity prices are crucial to ensure efficient portfolio management, support strategic decision-making for power plant operations, enable efficient battery storage optimization, and facilitate demand response planning. However, developing an accurate prediction model is highly challenging in an uncertain and volatile market environment. For instance, although linear models generally exhibit competitive performance in predicting electricity prices with minimal computational requirements, they fail to capture relevant nonlinear relationships. Nonlinear models, on the other hand, can improve forecasting accuracy with a surge in computational costs. We propose a novel multivariate neural network approach that combines linear and nonlinear feed-forward neural structures. Unlike previous hybrid models, our approach integrates online learning and forecast combination for efficient training and accuracy improvement. It also incorporates all relevant characteristics, particularly t...