🤖 AI Summary
This study addresses the challenge of balancing prediction accuracy and computational efficiency in highly volatile electricity markets, where linear models fail to capture nonlinear dynamics and complex nonlinear models incur prohibitive computational costs. To overcome this limitation, the authors propose a novel multivariate architecture that deeply integrates linear and nonlinear feedforward neural networks, synergistically leveraging their respective strengths. The framework incorporates online learning and a forecast combination mechanism to effectively model the dynamic relationships between electricity prices and multiple exogenous factors—including wind and solar generation, load demand, fuel prices, and carbon prices. Extensive experiments on six years of data from six major European electricity markets demonstrate that the proposed method reduces RMSE by 12–13% and MAE by 15–18% compared to state-of-the-art models, while simultaneously achieving significantly lower computational overhead.
📝 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 the fundamental relationships arising from wind and solar generation, electricity demand patterns, related energy fuel and carbon markets, in addition to autoregressive dynamics and calendar effects. Compared to the current state-of-the-art benchmark models, the proposed forecasting method significantly reduces computational cost while delivering superior forecasting accuracy (12-13% RMSE and 15-18% MAE reductions). Our results are derived from a six-year forecasting study conducted on major European electricity markets.