用多源数据预测极端天气下的停电，提升高风险场景预判能力
Predictive Modeling of Power Outages during Extreme Events: Integrating Weather and Socio-Economic Factors
- 融合气象、社会经济等多维度数据建模
- LSTM模型在密歇根州下半岛数据上表现最佳
- 适合电网规划与应急响应人员参考
本文提出一种基于学习的新型框架，用于预测极端事件引发的停电。该方法聚焦低概率高后果的停电场景，利用公开数据源构建全面特征集，整合了2014至2024年EAGLE-I停电记录、气象数据、社会经济数据、基础设施信息及季节性事件数据。引入社会与人口指标揭示了社区脆弱性模式，深化了对极端条件下停电风险的理解。评估了四种机器学习模型：随机森林（RF）、图神经网络（GNN）、自适应提升（AdaBoost）和长短期记忆网络（LSTM）。实验在密歇根州下半岛的县级数据上进行，结果表明LSTM模型在预测精度上优于其他模型。
原文摘要 · Abstract (English)
This paper presents a novel learning based framework for predicting power outages caused by extreme events. The proposed approach targets low-probability high-consequence outage scenarios and leverages a comprehensive set of features derived from publicly available data sources. We integrate EAGLE-I outage records from 2014 to 2024 with weather, socioeconomic, infrastructure, and seasonal event data. Incorporating social and demographic indicators reveals patterns of community vulnerability and improves understanding of outage risk during extreme conditions. Four machine learning models are evaluated, including Random Forest (RF), Graph Neural Network (GNN), Adaptive Boosting (AdaBoost), and Long Short-Term Memory (LSTM). Experimental validation is performed on a large-scale dataset covering counties in the lower peninsula of Michigan. Among all models tested, the LSTM network achieves higher accuracy.
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