电源学报2024，Vol.22Issue(4)：133-142,10.DOI:10.13234/j.issn.2095-2805.2024.4.133
基于聚合时空图卷积网络的多风场超短期风速预测
Ultra-short-term Wind Speed Prediction for Multiple Wind Farms Based on Aggregated Spatio-temporal Graph Convolutional Networks
摘要
Abstract
In a certain environment where regional wind farms distribute irregularly,the traditional convolutional neural network prediction method cannot reflect the distribution states or influence relationship of regional wind farms,and it is difficult to accurately predict the wind speed.First,to solve this problem,the technology of graph convolutional networks is used for feature modeling,and the connected graph and weight matrix are established according to the topology of multiple wind farms and the cross-correlation coefficient of wind speed in each region.Second,depending on the time dynamic characteristics of wind speed at wind farms,an improved parallel convolution structure is used to obtain the correlation between wind speed series in multiple time periods at the same wind farm.Third,based on the spatial correlation and delay effect of wind speed at wind farms,the spatio-temporal characteristics of wind speed in different regions are aggregated by using a second-order aggregation method.Finally,the verification of data from one regional wind farm shows that the proposed method can extract the spatio-temporal characteristics and improve the performance of ultra short-term wind speed prediction for multiple wind farms on 0-4 h prediction scale.关键词
风速预测/聚合时空图卷积网络/时空相关性Key words
Wind speed prediction/aggregated spatio-temporal graph convolutional networks/spatio-temporal correlation1分类
信息技术与安全科学引用本文复制引用
徐辰晓,崔承刚,郭为民,杨宁,刘备,孟青叶..基于聚合时空图卷积网络的多风场超短期风速预测[J].电源学报,2024,22(4):133-142,10.基金项目
国家自然科学基金资助项目(51607111)This work is supported by National Natural Science Foundation of China under the grant 51607111 （51607111）
