Spatio-Temporal Graph Neural Networks for Aggregate Load Forecasting

Accurate forecasting of electricity demand is a core component of the modern electricity infrastructure. Several approaches exist that tackle this problem by exploiting modern deep learning tools. However, most previous works focus on predicting the total load as a univariate time series forecasting task, ignoring all fine-grained information captured by the smart meters distributed across the power grid. We introduce a methodology to account for this information in the graph neural network framework. In particular, we consider spatio-temporal graphs where each node is associated with the aggregate load of a cluster of smart meters, and a global graph-level attribute indicates the total load on the grid. We propose two novel spatio-temporal graph neural network models to process this representation and take advantage of both the finer-grained information and the relationships existing between the different clusters of meters. We compare these models on a widely used, openly available, benchmark against a competitive baseline which only accounts for the total load profile. Within these settings, we show that the proposed methodology improves forecasting accuracy