Effects of Electricity Price Volatility, Energy Mix and Training Interval on Prediction Accuracy: An Investigation of Adaptive and Static Regression Models for Germany, France and the Czech Republic

Marek Pavlík () and Matej Bereš
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Marek Pavlík: Department of Electric Power Engineering, Technical University of Košice, 04001 Košice, Slovakia
Matej Bereš: Department of Theoretical and Industrial Electrical Engineering, Technical University of Košice, 04001 Košice, Slovakia

Energies, 2025, vol. 18, issue 15, 1-26

Abstract: Electricity markets in Europe have undergone major changes in the last decade, mainly due to the increasing share of variable renewable energy sources (RES), changing demand patterns, and geopolitical factors—particularly the war in Ukraine, tensions over energy imports, and disruptions in natural gas supplies. These changes have led to increased electricity price volatility, reducing the reliability of traditional forecasting tools. This research analyses the potential of static and adaptive linear regression as electricity price forecasting tools in the context of three countries with different energy mixes: Germany, France and the Czech Republic. The static regression approach was compared with an adaptive approach based on incremental model updates at monthly intervals. Testing was carried out in three different scenarios combining stable and turbulent market periods. The quantitative results showed that the adaptive model achieved a lower MAE and RMSE, especially when trained on data from high-volatility periods. However, models trained under turbulent conditions performed poorly in stable environments due to a shift in market dynamics. The results supported several of the hypotheses formulated and demonstrated the need for localised, flexible and continuously updated forecasting. Limitations of the adaptive approach and suggestions for future research, including changing the length of training windows and the use of seasonal models, are also discussed. The research confirms that modern markets require adaptive analytical approaches that account for changing RES dynamics and country specificities.

Keywords: adaptive regression; electricity price prediction; renewable energy; electricity market volatility (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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