Spatio-Temporal Photovoltaic Power Prediction with Fourier Graph Neural Network.

Jing, Shi; Xi, Xianpeng; Su, Dongdong; Han, Zhiwei; Wang, Daxing

The strong development of distributed energy sources has become one of the most important measures for low-carbon development worldwide. With a significant quantity of photovoltaic (PV) power generation being integrated to the grid, accurate and efficient prediction of PV power generation is an essential guarantee for the security and stability of the electricity grid. Due to the shortage of data from PV stations and the influence of weather, it is difficult to obtain satisfactory performance for accurate PV power prediction. In this regard, we present a PV power forecasting model based on a Fourier graph neural network (FourierGNN). Firstly, the hypervariable graph is constructed by considering the electricity and weather data of neighbouring PV plants as nodes, respectively. The hypervariance graph is then transformed in Fourier space to capture the spatio-temporal dependence among the nodes via the discrete Fourier transform. The multilayer Fourier graph operator (FGO) can be further exploited for spatio-temporal dependence information. Experiments carried out at six photovoltaic plants show that the presented approach enables the optimal performance to be obtained by adequately exploiting the spatio-temporal information.

GRAPH neural networks; ENERGY development; FOURIER transforms; METEOROLOGICAL stations; ELECTRICITY

Electronics (2079-9292), 2024, Vol 13, Issue 24, p4988

2079-9292

Academic Journal

10.3390/electronics13244988