用短数据窗口预测电力市场电价，轻量级模型表现更优。
Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows
- 采用7至90天的短期历史数据训练，评估模型适应能力。
- LightGBM在45和60天窗口下准确率最高，能有效捕捉价格峰值。
- 适合数据有限、需快速响应的电力市场预测场景。
本研究探究机器学习模型在使用短历史训练窗口时对欧洲三国（希腊、比利时、爱尔兰）电力日前市场（DAM）电价的预测性能，重点关注季节性趋势与价格尖峰的识别。评估了四种模型：带前馈误差校正的LSTM（FFEC）、XGBoost、LightGBM与CatBoost，基于ENTSO-E预测数据构建特征集。训练窗口长度为7至90天，以评估模型在数据受限条件下的适应性。结果表明，LightGBM在45天和60天训练窗口下始终表现出最高精度与鲁棒性，且在检测季节性变化和峰值价格事件方面优于LSTM及其他集成模型。研究显示，结合短窗口训练与提升类方法，可在波动性强、数据稀缺的环境中有效支持DAM电价预测。
原文摘要 · Abstract (English)
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.
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