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

TL;DR

This study develops a machine learning framework that combines weather, socio-economic, and infrastructure data to predict power outages during extreme events, emphasizing low-probability, high-impact scenarios.

Contribution

It introduces an integrated approach using diverse data sources and evaluates multiple models, highlighting LSTM's superior performance in outage prediction.

Findings

LSTM outperforms other models in accuracy

Incorporating socio-economic data improves prediction quality

The framework effectively identifies vulnerable communities

Abstract

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.