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

• URL: http://arxiv.org/abs/2107.13875v1
• Date: Thu, 29 Jul 2021 10:15:01 GMT
• Title: Spatio-temporal graph neural networks for multi-site PV power forecasting
• Authors: Jelena Simeunovi\'c, Baptiste Schubnel, Pierre-Jean Alet and Rafael E. Carrillo
• Abstract summary: We present two novel graph neural network models for deterministic multi-site forecasting. The proposed models outperform state-of-the-art multi-site forecasting methods for prediction horizons of six hours ahead.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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