Predicting Power Outages Using Graph Neural Networks

Power outages have a major impact on economic development due to the dependence of (virtually all) productive sectors on electric power. Thus, many resources within the scientific and engineering communities have been employed to improve the efficiency and reliability of power grids. In particular, we consider the problem of predicting power outages based on the current weather conditions. Weather measurements taken by a sensor network naturally fit within the graph signal processing framework since the measurements are related by the relative position of the sensors. We deploy novel graph neural networks to adequately process weather measurements in order to determine the likelihood of a power outage. Tests on weather measurements taken in the region of New York City show a 1.04% error in the prediction.