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

arXiv: https://arxiv.org/abs/2512.22699 · HTML: https://mlsanity.com/paper/2512.22699/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 27 Dec 2025.

## Builds on (most cited first, 3 of 3)

- [A Review of Graph Neural Networks and Their Applications in Power Systems](https://mlsanity.com/paper/2101.10025/) (2021)
- [Real-Time Prediction of the Duration of Distribution System Outages](https://mlsanity.com/paper/1804.01189/) (2018)
- [Deep Learning-Based Weather-Related Power Outage Prediction with Socio-Economic and Power Infrastructure Data](https://mlsanity.com/paper/2404.03115/) (2024)

## Abstract (arXiv)

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.