Volatility in the modern world and electricity Day-Ahead Markets (DAMs) usually makes long-term historical data irrelevant or even detrimental for accurate forecasting. This study directly addresses this challenge by proposing a novel forecasting paradigm centered on extremely short training windows, ranging from 7 to 90 days, to maximize responsiveness to recent market dynamics. This volatility-driven approach intentionally creates a data-scarce environment where the suitability of deep learning models is limited. Building on the hypothesis that shallow machine learning models, and more specifically boosting trees, are better adapted to this reality, we evaluate four models, namely LSTM with feed-forward error correction, XGBoost, LightGBM, and CatBoost, across three European energy markets (Greece, Belgium, Ireland) using feature sets derived from ENTSO-E forecast data. Results consistently demonstrate that LightGBM provides superior forecasting accuracy and robustness, particularly when trained on 45–60 day windows, which strike an optimal balance between temporal relevance and learning depth. Furthermore, a stronger capability in detecting seasonal effects and peak price events is exhibited. These findings validate that a short-window training strategy, combined with computationally efficient shallow models, is a highly effective and practical approach for navigating the volatility and data constraints of modern DAM forecasting.
Michalakopoulos et al. (2026) studied this question.