Data-Driven Wildfire Risk Model and Optimal Grid De-Energization Strategies
Date:
Invited presentation for the Department of Energy Office of Electricity (DOE OE) Wildfire Mitigation Webinar series. The talk focused on data-driven approaches for wildfire risk modeling and optimal grid de-energization strategies.
Key topics included machine learning techniques for wildfire risk prediction, optimization models for public safety power shutoffs, and integration of meteorological, topographical, and infrastructure data to support utility decision-making during high-risk wildfire conditions.