A novel AI-assisted forecasting strategy reveals the energy imbalance sign for the day-ahead electricity market

An advanced artificial intelligence (AI)-assisted methodology for predicting the sign of energy imbalances within the day-ahead energy market is introduced in this study, with a focus on the integration of renewable energy sources. By leveraging deep learning techniques and Numerical Weather Prediction (NWP) models, a nuanced understanding of energy market dynamics over a comprehensive five-year period is provided by the research. The findings reveal the substantial predictive advantage of the AI model over traditional forecasting methods, with fold-averaged Area Under the Curve (AUC) values of about 0.7 achieved for the two distinct macro-zones N and S. Economically, the model indicates potential for significant market participant gains, with mean efficiencies reaching 16% and 11% for macro-zones N and S, respectively. The implications extend beyond the Italian market, suggesting transformative potentials for European energy markets at large. This work not only fills a critical gap in the literature but also sets a new benchmark for predictive accuracy and economic viability in energy market forecasting.