Empowering Power Outage Prediction with Spatially Aware Hybrid Graph Neural Networks and Contrastive Learning
2026-04-06 • Machine Learning
Machine Learning
AI summaryⓘ
The authors created a new system to better predict power outages caused by extreme weather like storms and hurricanes. Their model, called SA-HGNN, uses special math tools that consider where things are located and how weather changes over time. They also used a technique called contrastive learning to improve predictions, especially for different types of weather events that happen less often. Tests in four regions showed their method predicts outages better than older models. This helps energy companies prepare ahead of bad weather to keep power running.
power outage predictionextreme weathergraph neural networksspatial relationshipscontrastive learningelectric distribution networksdynamic featuresstatic featuresclimate change impactsmachine learning
Authors
Xuyang Shen, Zijie Pan, Diego Cerrai, Xinxuan Zhang, Christopher Colorio, Emmanouil N. Anagnostou, Dongjin Song
Abstract
Extreme weather events, such as severe storms, hurricanes, snowstorms, and ice storms, which are exacerbated by climate change, frequently cause widespread power outages. These outages halt industrial operations, impact communities, damage critical infrastructure, profoundly disrupt economies, and have far-reaching effects across various sectors. To mitigate these effects, the University of Connecticut and Eversource Energy Center have developed an outage prediction modeling (OPM) system to provide pre-emptive forecasts for electric distribution networks before such weather events occur. However, existing predictive models in the system do not incorporate the spatial effect of extreme weather events. To this end, we develop Spatially Aware Hybrid Graph Neural Networks (SA-HGNN) with contrastive learning to enhance the OPM predictions for extreme weather-induced power outages. Specifically, we first encode spatial relationships of both static features (e.g., land cover, infrastructure) and event-specific dynamic features (e.g., wind speed, precipitation) via Spatially Aware Hybrid Graph Neural Networks (SA-HGNN). Next, we leverage contrastive learning to handle the imbalance problem associated with different types of extreme weather events and generate location-specific embeddings by minimizing intra-event distances between similar locations while maximizing inter-event distances across all locations. Thorough empirical studies in four utility service territories, i.e., Connecticut, Western Massachusetts, Eastern Massachusetts, and New Hampshire, demonstrate that SA-HGNN can achieve state-of-the-art performance for power outage prediction.