Renewable sources and short-to-mid-term electricity price forecasting

Abstract

This study examines short-to mid-term point forecasting of daily electricity prices, with particular emphasis on the role of renewable energy sources. We use data from the market zone corresponding to the Northern region of Italy, applying both time series and machine learning methodologies. The forecasts are evaluated for two individual years, 2019 and 2024. In 2019, traditional energy variables such as electricity load, natural gas prices, and imports, were the primary drivers of forecast accuracy. During this period, adding renewable energy production data offered negligible benefits, with solar and wind contributing only marginally. By contrast, in 2024, market volatility increased greatly due to geopolitical conflicts and increased renewable energy integration. Under these conditions, while solar and wind still added limited value, hydropower improved forecast accuracy substantially. The results suggest that the role of renewable energy sources in electricity price forecasting is growing. However, their predictive power is influenced by their market share and by their variability and predictability.