用梯度提升模型预测全球每小时用电量，助力能源规划。
DemandCast: Global hourly electricity demand forecasting
- 基于XGBoost融合历史用电、天气与社会经济数据
- 跨多国多年数据训练，实现小时级精准预测
- 适合能源政策制定者与电网规划人员参考
本文提出一种机器学习框架，利用梯度提升算法XGBoost，对多个地理区域的电力需求进行预测。模型整合了历史用电数据以及全面的气象和社会经济变量，用于预测归一化的电力需求曲线。为支持稳健的训练与评估，我们构建了一个涵盖多年、多国的大规模数据集，并采用时间序列分割策略，确保样本外性能的基准测试。该方法可提供准确且可扩展的用电量预测，为能源系统规划者和政策制定者应对全球能源转型挑战提供关键支持。
原文摘要 · Abstract (English)
This paper presents a machine learning framework for electricity demand forecasting across diverse geographical regions using the gradient boosting algorithm XGBoost. The model integrates historical electricity demand and comprehensive weather and socioeconomic variables to predict normalized electricity demand profiles. To enable robust training and evaluation, we developed a large-scale dataset spanning multiple years and countries, applying a temporal data-splitting strategy that ensures benchmarking of out-of-sample performance. Our approach delivers accurate and scalable demand forecasts, providing valuable insights for energy system planners and policymakers as they navigate the challenges of the global energy transition.
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