Forecast lead time and the timing of crew deployment for weather-driven power outages using AI weather models
Utilities request mutual aid crews to restore power after severe storms, and requesting early secures more crews but relies on less accurate forecasts. We examined how forecast lead time affects the value of outage predictions for crew deployment, using operational forecasts from two AI weather models, GraphCast and FourCastNet, to predict outage occurrence for counties across the contiguous United States 24 to 120 hours ahead. Forecasts were evaluated by the share of outage burden captured by the counties where crews would be staged. Captured burden declined with lead time: the loss between three and five days was significant in all analyses, whereas the loss within three days was signif...
| Authors | Mohammad Kian Golkar, Luciano Alves de Oliveira, Mohamad Alipour |
|---|---|
| Primary category | physics.ao-ph |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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