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.

翻译：本文提出了一种基于学习的新型框架，用于预测极端事件引起的电力中断。所提出的方法针对低概率高后果的停电场景，并利用从公开可用数据源中提取的全面特征集。我们将2014年至2024年的EAGLE-I停电记录与天气、社会经济、基础设施及季节性事件数据相结合。纳入社会和人口统计指标揭示了社区脆弱性的模式，并提升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，包括随机森林（RF）、图神经网络（GNN）、自适应提升（AdaBoost）和长短期记忆网络（LSTM）。实验验证在一个覆盖密歇根州下半岛各县的大规模数据集上进行。在所有测试模型中，LSTM网络取得了更高的准确率。