A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets

Electricity price forecasting plays a fundamental role in ensuring efficient market operation and informed decision making. With the growing integration of renewable energy, prices have become more volatile and difficult to predict, increasing the necessity of accurate forecasting in bidding, scheduling, and risk management. This paper provides a comprehensive review of point forecasting models for electricity markets, covering classical statistical approaches both with and without exogenous inputs, and modern machine learning and deep learning techniques, including ensemble methods and hybrid architectures. Unlike standard reviews focused solely on the day-ahead market, we assess model performance across day-ahead, intra-day, and balancing markets, with each posing unique challenges due to differences in time resolution, data availability, and market structure. Through this market-specific lens, the paper merges insights from a broad set of studies; identifies persistent challenges, such as data quality, model interpretability, and generalisability; and outlines promising directions for future research. Our findings highlight the strong performance of hybrid and ensemble models in the day-ahead market, the dominance of recurrent neural networks in the intra-day market, and the relative effectiveness of simpler statistical models such as LEAR in the balancing market, where volatility and data sparsity remain critical challenges.