The interaction of severe weather, overhead lines and surrounding trees is the leading cause of outages to electric distribution networks in forested areas. In this paper, we show how utility-specific infrastructure and land cover data, aggregated around overhead lines, can improve outage predictions for Eversource Energy (formerly Connecticut Light and Power), the largest electric utility in Connecticut. Eighty-nine storms from different seasons (cold weather, warm weather, transition months) in the period 2005-2014, representing varying types (thunderstorms, blizzards, nor'easters, hurricanes) and outage severity, were simulated using the Weather Research and Forecasting (WRF) atmospheric model. WRF simulations were joined with utility outage data to calibrate four types of models: a decision tree (DT), random forest (RF), boosted gradient tree (BT) and an ensemble (ENS) decision tree regression that combined predictions from DT, RF and BT. The study shows that the ENS model forced with weather, infrastructure and land cover data was superior to the other models we evaluated, especially in terms of predicting the spatial distribution of outages. This framework could be used for predicting outages to other types of critical infrastructure networks with benefits for emergency-preparedness functions in terms of equipment staging and resource allocation.

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This article compares two nonparametric tree-based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high-resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2-km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree-leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources.

Hurricane Sandy (2012, referred to as Current Sandy) was among the most devastating storms to impact Connecticut’s overhead electric distribution network, resulting in over 15 000 outage locations that affected more than 500 000 customers. In this paper, the severity of tree-caused outages in Connecticut is estimated under future-climate Hurricane Sandy simulations, each exhibiting strengthened winds and heavier rain accumulation over the study area from large-scale thermodynamic changes in the atmosphere and track changes in the year ~2100 (referred to as Future Sandy). Three machine-learning models used five weather simulations and the ensemble mean of Current and Future Sandy, along with land-use and overhead utility infrastructure data, to predict the severity and spatial distribution of outages across the Eversource Energy service territory in Connecticut. To assess the influence of increased precipitation from Future Sandy, two approaches were compared: an outage model fit with a full set of variables accounting for both wind and precipitation, and a reduced set with only wind. Future Sandy displayed an outage increase of 42%–64% when using the ensemble of WRF simulations fit with three different outage prediction models. This study is a proof of concept for the assessment of increased outage risk resulting from potential changes in tropical cyclone intensity associated with late-century thermodynamic changes driven by the IPCC AR4 A2 emissions scenario.

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