Probabilistic Storm and Electric Utility Customer Outage Prediction

Severe weather is a leading cause of electric distribution network failures and customer outages. Predictive modelling of customer outages can mitigate the economic and personal impact of adverse weather but is challenging due to the diverse causes and nonstationarity of customer outage events. Moreover, customer outages may occur during both blue skies or storm weather and only a small proportion of weather events leads to significant outage events. Current approaches for estimating customer outages either assume stationarity and thus underestimate outages in storms or rely on expensive hand labelling of historical and prospective storms, which may or may not be associated with significant outages. In this study, we develop probabilistic storm labelling and outage prediction methods guided by storm probability estimation to improve customer outage prediction in New York State. We partition blue skies and storm weather with outlier detection algorithms, and model customer outages in blue skies weather using an autoregressive neural network and time series models – with model selection based on cross validation and the Akaike information criterion. For nonstationary phenomena like severe weather, we build several machine learning regressors (random forest, k-nearest neighbors, and Poisson) to predict customer outages. Finally, blue skies and storm outage predictions are combined in a single architecture guided by the estimated storm event probability. We demonstrate that, by carefully considering the distinct signatures of storms and blue skies regions, our approach more accurately forecasts customer outages under different storm event scenarios across New York State counties when compared with competing methods.