Multi-site Forecasting of Energy Time Series with Spatio-Temporal Graph Neural Networks

Climate change has prompted the energy sector to shift its focus to renewable energy sources, which are environmentally friendly but less in terms of cost, complexity, and plants' management. It becomes critical to have a reliable method for estimating the output power of these systems, which are dispersed across the country and vary in kind and technology, and whose output power is mostly determined by meteorological factors. In this paper, we exploit the capability of modeling dynamic graph-like data of a specific type of graph neural network, spatio-temporal graph neural network, which can process spatial information about plants' distribution in a particular region as well as temporal data on individual plant power production. Plants in the same region can share information and make more accurate forecasts in this way. The suggested model was evaluated on two types of datasets: one with data gathered from real photovoltaic systems and the other with synthesized power time series reconstructed from data acquired by satellite detection. Our studies discovered how these systems can estimate the production outputs of photovoltaic stations simultaneously and with higher accuracy with respect to previous state-of-the-art models, performing effectively even in the absence of meteorological data.