电力系统保护与控制2025，Vol.53Issue(19)：79-88,10.DOI:10.19783/j.cnki.pspc.241190
基于图神经网络的短期风电功率群体预测方法
Short-term wind power group forecasting method based on graph neural networks
摘要
Abstract
To reduce the impact of wind power fluctuations on power systems,a short-term power forecasting method for large-scale wind farm clusters is proposed,which accounts for spatiotemporal correlation and simultaneously outputs short-term power predictions for all wind farms.First,an evaluation index that comprehensively considers the spatial correlation of wind speed and direction is proposed,and a graph topology structure is further established to characterize the spatiotemporal correlation of wind farm clusters.Then,a deep residual graph attention network is constructed to mine the spatiotemporal correlation features between multiple wind farms,preserving the valuable spatiotemporal information embedded in the data during training.Finally,a false prediction evaluation index is proposed to assess the false prediction components of the predicted power at the station when aggregated into cluster prediction power,making a fairer evaluation of cluster prediction results.Experiments are conducted using a cluster composed of 20 wind farms in Jilin province,China.Results show that the proposed wind power forecasting model achieves a day-ahead power prediction accuracy rate of 91.68%.关键词
图注意力网络/深度残差网络/时空相关性/短期风电功率预测/误差评估Key words
graph attention network/deep residual network/spatiotemporal correlation/short-term wind power forecasting/error evaluation引用本文复制引用
杨茂,郭镇鹏,王达,张薇,王勃,江任贤,苏欣..基于图神经网络的短期风电功率群体预测方法[J].电力系统保护与控制,2025,53(19):79-88,10.基金项目
This work is supported by the National Key Research and Development Program of China(No.2022YFB2403000). 国家重点研发计划项目资助(2022YFB2403000) （No.2022YFB2403000）
