电网技术2024，Vol.48Issue(5)：2064-2073,中插44-中插46,13.DOI:10.13335/j.1000-3673.pst.2023.0899
基于时空注意力卷积模型的超短期风电功率预测
Ultra-short-term Wind Power Prediction Based on Spatiotemporal Attention Convolution Model
摘要
Abstract
With the continuous improvement of wind power utilization,accurate prediction of the wind power output power is of great significance for the scheduling and stable operating of the power systems.However,the randomness and volatility of the wind power generation easily affects the accuracy of the power prediction results.In this paper a wind power prediction based on the spatiotemporal correlation is proposed,consisting of a spatiotemporal attention module and a spatiotemporal convolution module.First,the spatial attention layer and the temporal attention layer are used to aggregate and extract the spatiotemporal correlations between different wind turbines.Second,the spatial features and the temporal evolution patterns among the wind power data are effectively captured by the spatial convolution layer and the temporal convolution layer.Finally,the prediction method is experimentally validated using the operational data from two actual wind farms in China.The results indicate that compared to the traditional prediction methods,the fusion of the spatiotemporal attention and the spatiotemporal convolution enables the proposed prediction to have a higher accuracy and a better stability.关键词
风电功率预测/时空相关性/图神经网络/时空注意力模块/时空卷积模块Key words
wind power forecast/spatiotemporal correlation/graph neural network/spatiotemporal attention module/spatiotemporal convolution module分类
信息技术与安全科学引用本文复制引用
吕云龙,胡琴,熊俊杰,龙敦华..基于时空注意力卷积模型的超短期风电功率预测[J].电网技术,2024,48(5):2064-2073,中插44-中插46,13.基金项目
国网江西省电力有限公司科技项目(521820220007).Project Supported by State Grid Jiangxi Electric Power Co.,Ltd.(521820220007). （521820220007）
