title: Improving Hurricane Power Outage Prediction Models Through the Inclusion of Local Environmental Factors.
abstract: Tropical cyclones can significantly damage the electrical power system, so an accurate spatiotemporal forecast of outages prior to landfall can help utilities to optimize the power restoration process. The purpose of this article is to enhance the predictive accuracy of the Spatially Generalized Hurricane Outage Prediction Model (SGHOPM) developed by Guikema et al. (2014). In this version of the SGHOPM, we introduce a new two-step prediction procedure and increase the number of predictor variables. The first model step predicts whether or not outages will occur in each location and the second step predicts the number of outages. The SGHOPM environmental variables of Guikema et al. (2014) were limited to the wind characteristics (speed and duration of strong winds) of the tropical cyclones. This version of the model adds elevation, land cover, soil, precipitation, and vegetation characteristics in each location. Our results demonstrate that the use of a new two-step outage prediction model and the inclusion of these additional environmental variables increase the overall accuracy of the SGHOPM by approximately 17%.
authors: Department of Geography, Texas A&M University, College Station, TX, USA.
authors: D Brent
authors: DB
authors: McRoberts
authors: Department of Geography, Ohio State University, Columbus, OH, USA.
authors: Steven M
authors: SM
authors: Quiring
authors: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.
authors: Seth D
authors: SD
authors: Guikema
keywords: Data mining; hurricanes; outage model; outage prediction; reliability; storm response planning