DeepAI AI Chat
Log In Sign Up

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

by   Jelena Simeunović, et al.

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.


page 1

page 5

page 6

page 7


Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures

To improve the security and reliability of wind energy production, short...

Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand

Electric vehicles can offer a low carbon emission solution to reverse ri...

Short-term photovoltaic generation forecasting using multiple heterogenous sources of data

Renewable Energies (RES) penetration is progressing rapidly: in France, ...

Dense Forecasting of Wildfire Smoke Particulate Matter Using Sparsity Invariant Convolutional Neural Networks

Accurate forecasts of fine particulate matter (PM 2.5) from wildfire smo...

Graph Neural Networks for Improved El Niño Forecasting

Deep learning-based models have recently outperformed state-of-the-art s...

Deep Graph Convolutional Networks for Wind Speed Prediction

Wind speed prediction and forecasting is important for various business ...