Dynamic Graph Machine Learning for Mult-Site Solar Forecasting

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Carrillo, Rafael E
Schubnel, Baptiste
Langou, Renaud
Alet, Pierre-Jean
In this paper, we present DIGERATI, CSEM’s solution for intraday forecasting (six-hour horizon). DIGERATI is based on graph neural networks (GNN). It extends previous algorithms developed by CSEM and enables their use in real operating conditions. These GNN algorithms outperformed the state of the art in forecasting photovoltaic production, while relying only on real, imperfect power measurements. In previous works the same nodes were present throughout training and evaluation. However, in a commercial application, nodes (e.g., photovoltaic systems) would be frequently added or removed as new customers sign up or physical assets change. To meet this constraint, DIGERATI exploits dynamic GNNs, where nodes and edges can be added or removed over time, thus providing a robust and scalable solution for real-life operations. Additionally, DIGERATI uses advanced GNNs to fuse information from heterogeneous data sources (PV power, irradiance, wind speed and temperature) measured at different locations to produce probabilistic forecasts of irradiance (or PV power) in desired locations. The live demonstrator can produce probabilistic forecasts at any location in Switzerland and the Netherlands every 15 minutes with a temporal resolution of 15 minutes.
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