Electricity Price Volatility and the Performance of Machine Learning Forecasting Models in European Energy Markets

Electricity is fundamental to the functioning of modern economies, yet its price volatility presents significant challenges for both long-term investment planning and short-term operational decision-making. In this study we examine electricity price dynamics across seven diverse European bidding zones, selected through principal component analysis to reflect a broad spectrum of energy mix characteristics. The analysis explores the relationship between the structure of national energy mixes—classified according to the controllability of generation sources—and the volatility and predictability of electricity prices during 2023–2024. Using ENTSO-E data, adaptive machine learning models were developed to forecast day-ahead electricity prices, with the Random Forest algorithm consistently achieving the highest predictive accuracy. The results indicate that bidding zones dominated by low-controllability renewable generation exhibit greater price volatility and reduced forecast accuracy, whereas zones with a higher share of controllable sources, such as natural gas, demonstrate more stable prices and improved model performance. These findings underscore the crucial role of the energy mix composition in shaping market dynamics and highlight the necessity of adopting adaptive, mix-sensitive forecasting methodologies in increasingly diversified electricity systems.