Spatio-Temporal Graph Neural Networks for Multi-Site PV Power Forecasting

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dc.contributor.authorSimeunović, Jelena
dc.contributor.authorSchubnel, Baptiste
dc.contributor.authorAlet, Pierre-Jean
dc.contributor.authorCarrillo, Rafael E
dc.date.accessioned2024-02-19T10:48:56Z
dc.date.available2024-02-19T10:48:56Z
dc.date.issued2022-04
dc.description.abstractAccurate forecasting of solar power generation with fine temporal and spatial resolution is vital for the operation of the power grid. However, state-of-the-art approaches that combine machinelearning with numerical weather predictions (NWP) have coarse resolution. In this paper, we take a graph signal processing perspective and model multi-site photovoltaic (PV) production time series as signals on a graph to capture their spatio-temporal dependencies and achieve higher spatial and temporal resolution forecasts. We present two novel graph neural network models for deterministic multi-site PV forecasting dubbed the graph-convolutional long short term memory (GCLSTM) and the graph-convolutional transformer (GCTrafo) models. These methods rely solely on production data and exploit the intuition that PV systems provide a dense network of virtual weather stations. The proposed methods were evaluated in two data sets for an entire year: 1) production data from 304 real PV systems, and 2) simulated production of 1000 PV systems, both distributed over Switzerland. The proposed models outperform state-of-the-art multi-site forecasting methods for prediction horizons of six hours ahead. Furthermore, the proposed models outperform state-of-the-art single-site methods with NWP as inputs on horizons up to four hours ahead.
dc.identifier.citationIEEE Transactions on Sustainable Energy
dc.identifier.doi10.1109/TSTE.2021.3125200
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1346
dc.language.isoen
dc.titleSpatio-Temporal Graph Neural Networks for Multi-Site PV Power Forecasting
dc.typeJournal Article
dc.type.csemdivisionsBU-V
dc.type.csemresearchareasData & AI
dc.type.csemresearchareasDigital Energy
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