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

arXiv: https://arxiv.org/abs/2512.06644 · HTML: https://mlsanity.com/paper/2512.06644/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2025.

## Built on by (most cited first, 1 of 1)

- [From AI Weather Prediction to Infrastructure Resilience: A Real-Time Correction-Downscaling Framework for Tropical Cyclone Impact Forecasting](https://mlsanity.com/paper/2603.12828/) (2026)

## Abstract (arXiv)

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.