Search for: All records

Public Safety Power Shutoffs (PSPS) are a critical yet disruptive wildfire mitigation strategy used by electric utilities to reduce ignition risk during periods of elevated fire danger. However, current PSPS decisions often lack transparency and consistency, prompting the need for data-driven tools to better understand utility behavior. This paper presents a Support Vector Machine (SVM) framework to model and interpret PSPS decision-making using post-event wildfire reports. Forecast-based weather and fire behavior features are used as model inputs to represent decision-relevant variables reported by utilities. The model is calibrated using Platt scaling for probabilistic interpretability and adapted across utilities using importance- weighted domain adaptation to address feature distribution shifts. A post-hoc clustering segments PSPS events into wildfire risk zones based on ignition risk metrics excluded from model train- ing. Results demonstrate that the proposed framework supports interpretable, transferable analysis of PSPS decisions, offering insight into utility practices and informing more transparent de- energization planning.