  This paper presents a deep learning-based approach for hourly power outage
probability prediction within census tracts encompassing a utility company's
service territory. Two distinct deep learning models, conditional Multi-Layer
Perceptron (MLP) and unconditional MLP, were developed to forecast power outage
probabilities, leveraging a rich array of input features gathered from publicly
available sources including weather data, weather station locations, power
infrastructure maps, socio-economic and demographic statistics, and power
outage records. Given a one-hour-ahead weather forecast, the models predict the
power outage probability for each census tract, taking into account both the
weather prediction and the location's characteristics. The deep learning models
employed different loss functions to optimize prediction performance. Our
experimental results underscore the significance of socio-economic factors in
enhancing the accuracy of power outage predictions at the census tract level.
