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.

基于深度学习的条件和无条件MLP模型，利用公开数据源中的气象数据、气象站位置、电力基础设施地图、社会经济和人口统计数据以及停电记录，预测了每个人口普查区的停电概率，并强调了社会经济因素在提高人口普查区停电预测准确性方面的重要性。

基于深度学习的天气相关停电预测：社会经济和电力基础设施数据分析