Spatio-temporal forecasting of weather-driven damage in a distribution system

Abstract

A major ongoing effort by utilities is to improve their emergency preparedness process for weather events, in order to: 1) reduce outage time 2) reduce repair and restoration costs and 3) improve customer satisfaction. This paper proposes a method for forecasting the number of damages of different types that will result from a weather event, up to 3 days before the event actually occurs. The proposed method overcomes practical issues with sparsity of historical damage and weather records by 1) using a spatial clustering-based scheme to work even in cases where there are very few historical incidents of damage, 2) combining data from multiple weather observation networks, 3) using weather hindcast data and 4) accounting for variability in damage susceptibility across different substation regions. The performance of the method is evaluated on real utility data.