Improved quantitative prediction of power outages caused by extreme weather events

Power outages caused by extreme weather events cost the economy of the United States billions of dollars every year and endanger the lives of the people affected by them. These types of events could be better managed if accurate predictions of storm impacts were available. While empirical power outage prediction models have been in development for many years, accurate operational predictions of the most extreme and impactful weather-related outage events have proven difficult to achieve for several reasons. In this paper, we describe a data intensive modeling approach specifically designed for forecasting the impacts of extreme weather events on power distribution grids. To that end, methods for developing datasets that include a large number of example storms and predictors are described. In addition, we test several methods of managing the extreme value distribution of the target variable via statistical transformation and balancing of the dataset. The best performing outage prediction model developed here is capable of predicting storm impacts across four orders of magnitude with R2 and Nash-Sutcliffe Efficiency scores of 0.82. Also, by investigating the model's sensitivities and predictions for the highest impact events, we find that there is significant diversity in the meteorological factors that drive the predictions for the most severe events, suggesting that the weather hazards are more complex than they often treated in empirical analyses of their impacts. The accuracy of the outage model, together with the importance of various meteorological variables that contribute to that accuracy, validate the described methodology and suggest that future empirical analysis of the impacts of extreme weather should include multifaceted descriptions of the hazard to better represent the complex factors which contribute to the most impactful events.