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