Deep graph temporal convolutional neural networks for short-term wind speed prediction

Due to the high randomness of wind speed in the natural environment, accurate wind speed prediction is of great significance for wind energy. With the tremendous potential demonstrated by deep learning techniques in various fields, researchers have applied this technology to wind speed prediction an...

Full description

Saved in:
Bibliographic Details
Published in2024 4th International Conference on Neural Networks, Information and Communication (NNICE) pp. 213 - 216
Main Authors Yu, Jingjia, Liu, Xin, Gong, Lin, Liu, Minxia, Xiang, Xi, Xie, Jian, Zhang, Yongyang
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.01.2024
Subjects
Data models
graph neural networks
Network topology
Predictive models
temporal convolutional network
Time series analysis
Wind energy
Wind farms
Wind speed
Online AccessGet full text

Cover

More Information
Summary:Due to the high randomness of wind speed in the natural environment, accurate wind speed prediction is of great significance for wind energy. With the tremendous potential demonstrated by deep learning techniques in various fields, researchers have applied this technology to wind speed prediction and achieved promising results. In this paper, to improve the accuracy of wind speed prediction, a novel network called Graph Temporal Convolutional Network (GTCN) is proposed, which effectively exploits historical wind speed features from both temporal and spatial dimensions. Based on wind speed data collected from 11 turbines in a wind farm, five baseline models are used to conduct comparative studies. The results demonstrate that GTCN exhibits superior performance in wind speed prediction.
DOI:10.1109/NNICE61279.2024.10498379