Predictive Modeling of Power Outages during Extreme Events: Integrating Weather and Socio-Economic Factors

• URL: http://arxiv.org/abs/2512.22699v1
• Date: Sat, 27 Dec 2025 20:30:07 GMT
• Title: Predictive Modeling of Power Outages during Extreme Events: Integrating Weather and Socio-Economic Factors
• Abstract summary: This paper presents a novel learning-based framework for predicting power outages caused by extreme events.<n>It targets low-probability, high-consequence outage scenarios and leverages a comprehensive set of features derived from publicly available data sources.<n>Four machine learning models (Random Forest (RF), Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), and Long Short-Term Memory (LSTM)) are evaluated.
• Score: 0.4640835690336653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel learning-based framework for predicting power outages caused by extreme events. The proposed approach specifically 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 (2014-2024) with weather, socio-economic, infrastructure, and seasonal event data. Incorporating social and demographic indicators reveals underlying patterns of community vulnerability and provides a clearer understanding of outage risk during extreme conditions. Four machine learning models (Random Forest (RF), Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), and Long Short-Term Memory (LSTM)) are evaluated. 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 the lowest prediction error. Additionally, the results demonstrate that stronger economic conditions and more developed infrastructure are associated with lower outage occurrence.

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