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      Spatio-temporal graph neural networks for multi-site PV power forecasting

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          Abstract

          Accurate 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 machine learning 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.

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          Author and article information

          Journal
          29 July 2021
          Article
          2107.13875
          d8f94d01-a7d8-496c-8ba5-b12a5247fd77

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          10 pages, 6 figures, submitted to IEEE Transactions on Sustainable Energy
          cs.LG eess.SP

          Artificial intelligence,Electrical engineering
          Artificial intelligence, Electrical engineering

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