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

Abstract

Precise day-ahead forecasts for electricity prices are crucial to ensure efficient portfolio management, support strategic decision-making for power plant operations, and enable effective battery optimization. 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 employ a multivariate hybrid neural architecture that combines linear and nonlinear feed-forward neural structures. We then integrate this architecture with a novel partial online learning strategy that combines warm-starting with distinct hyperparameter configurations for each training stage. This strategy, which substantially reduces computational time, constitutes our key contribution. Unlike previous hybrid models, our framework also incorporates forecast combination through Bernstein Online Aggregation (BOA) to further improve forecasting accuracy. Compared with the considered state-of-the-art benchmark implementations in the reported setup, the proposed forecasting method significantly reduces computational cost while delivering better forecasting accuracy (11-12% RMSE and 14-17% MAE reductions). Our results are derived from a six-year forecasting study conducted on major European electricity markets.