A Risk Model for Predicting Powerline-induced Wildfires in Distribution System (Proposals Track)

Mengqi Yao (University of California Berkeley)



The power grid is one of the most common causes of wildfires that result in tremendous economic loss and significant life risk. In this study, we propose to use machine learning techniques to build a risk model for predicting powerline-induced wildfires in distribution system. We collect weather, vegetation, and infrastructure data for all feeders in Pacific Gas & Electricity territory. This study will contribute to a deeper understanding of powerline-induced wildfire prediction and provide valuable suggestions for wildfire mitigation planning.