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Overhead distribution lines play a vital role in distributing electricity,however, their freestanding nature makes them vulnerable to extreme weatherconditions and resultant disruption of supply. The current UK regulation ofpower networks means preemptive mitigation of disruptions avoids financialpenalties for distribution companies, making accurate fault predictions ofdirect financial importance. Here we present predictive models developed for aUK network based on gradient-boosted location, scale, and shape models,providing spatio-temporal predictions of faults based on forecast weatherconditions. The models presented are based on (a) tree base learners or (b)penalised smooth and linear base learners -- leading to a Generalised AdditiveModel (GAM) structure, with the latter category of models providing bestperformance in terms of out-of-sample log-likelihood. The models are fitted tofifteen years of fault and weather data and are shown to provide good accuracyover multi-day forecast windows, giving tangible support to power restoration.

Forecasting overhead distribution line failures using weather data and gradient-boosted location, scale, and shape models