Open AccessA robust spatio‐temporal prediction approach for wind power generation based on spectral temporal graph neural network
Author(s) -
He Yuqin,
Chai Songjian,
Zhao Jian,
Sun Yuxin,
Zhang Xian
Publication year - 2022
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12449
Subject(s) - computer science , correlation , graph , wind power , fourier transform , artificial intelligence , artificial neural network , pattern recognition (psychology) , data mining , algorithm , mathematics , engineering , theoretical computer science , mathematical analysis , geometry , electrical engineering
At present, the penetration of wind power generation is increasing remarkably worldwide, and the accurate wind power forecasting (WPF) is essential to ensure the reliability and economy of the power system. Most of the current work of WPF only capture temporal correlation in the time domain but ignore the spatial correlation. In this study, a spectral time graph neural network based on the maximum correlation criterion (MCC‐Stem‐GNN) is proposed to improve the accuracy of WPF for multiple sites and horizons. The self‐attentive mechanism in the MCC‐Stem‐GNN automatically learns the correlations between the multivariate sequences. Besides, this model combines the Graph Fourier Transform (GFT) to model spatial correlation and the Discrete Fourier Transform (DFT) to model temporal correlation. The effectiveness of the proposed robust deep learning framework is verified on the simulated wind energy dataset over 16 locations in Ohio, US through considering different sample contamination types and levels, comprehensive case study is carried out to show the superiority of the MCC‐Stem‐GNN over the benchmarks.