From Forecast to Action: A Deep Learning Model for Predicting Power Outages During Tropical Cyclones

Abstract

Power outages caused by tropical cyclones (TCs) pose serious risks to electric power systems and the communities they serve. Accurate, high-resolution outage forecasting is essential for enabling both proactive mitigation planning and real-time emergency response. This study introduces the SpatioTemporal Outage ForeCAST (STO-CAST) model, a deep learning framework developed for real-time, regional-scale outage prediction during TC events with high-resolution outputs in both space and time. STO-CAST integrates static environmental and infrastructure attributes with dynamic meteorological and outage sequences using gated recurrent units (GRUs) and fully connected layers, and is trained via a Leave-One-Storm-Out (LOSO) cross-validation strategy along with holdout grid experiments to demonstrate its preliminary generalization capability to unseen storms and grids. The model produces hourly outage forecasts at a 4 km * 4 km resolution and supports dual forecasting modes: short-term nowcasting with a 6-hour lead time via assimilation of real-time observations, and long-term forecasting with a 60-hour lead time based on evolving meteorological projections. A case study on Typhoon Muifa (2022) demonstrates STO-CAST's operational effectiveness, including error decomposition across model design, meteorological uncertainty, and observation gaps, while highlighting the value of real-time data assimilation and the model's capacity to identify evolving outage hotspots. STO-CAST offers a scalable, data-driven solution to support risk-informed emergency response and enhance power system resilience under intensifying TC threats.