Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows
This work addresses forecasting challenges for energy market participants in volatile, data-scarce environments, but it is incremental as it compares existing methods on new data.
This study tackled forecasting electricity Day-Ahead Market prices using short training windows, finding that LightGBM achieved the highest accuracy and robustness, particularly with 45-60 day windows, and outperformed other models in detecting seasonal trends and price spikes.
This study investigates the performance of machine learning models in forecasting electricity Day-Ahead Market (DAM) prices using short historical training windows, with a focus on detecting seasonal trends and price spikes. We evaluate four models, namely LSTM with Feed Forward Error Correction (FFEC), XGBoost, LightGBM, and CatBoost, across three European energy markets (Greece, Belgium, Ireland) using feature sets derived from ENTSO-E forecast data. Training window lengths range from 7 to 90 days, allowing assessment of model adaptability under constrained data availability. Results indicate that LightGBM consistently achieves the highest forecasting accuracy and robustness, particularly with 45 and 60 day training windows, which balance temporal relevance and learning depth. Furthermore, LightGBM demonstrates superior detection of seasonal effects and peak price events compared to LSTM and other boosting models. These findings suggest that short-window training approaches, combined with boosting methods, can effectively support DAM forecasting in volatile, data-scarce environments.