arXiv — cs.AI preprintsInternational16 October 2026
Forecast lead time and the timing of crew deployment for weather-driven power outages using AI weather models
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.20918v1 Announce Type: cross Abstract: 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
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock16 October 2026
- Evaluating Model Capabilities After Benchmark Saturation16 October 2026
- Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion16 October 2026
- AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks16 October 2026
- When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus16 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.