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

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.

arXiv comment10 pages, 7 figures, accepted for publication in IEEE Transactions on Sustainable Energy

References in corpus (9)

• A Comprehensive Survey on Graph Neural Networks
• Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
• Spectral Networks and Locally Connected Networks on Graphs
• Diffusion-Convolutional Neural Networks
• Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction
• A Survey on Graph Kernels
• Residual Gated Graph ConvNets
• Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting
• If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN

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