Predictive Modeling of Power Outages during Extreme Events: Integrating Weather and Socio-Economic Factors
It addresses power outage prediction for utility management and community resilience, but is incremental as it applies existing machine learning models to a new dataset.
This paper tackles predicting power outages during extreme events by integrating weather and socio-economic factors, with the LSTM model achieving higher accuracy in experimental validation on Michigan county data.
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.