时空特性下基于图卷积神经网络的风电集群功率短期预测方法

乔宽龙; 董存; 车建峰; 蒋建东; 王勃

doi:10.19912/j.0254-0096.tynxb.2023-0009

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (5) : 95-103. DOI: 10.19912/j.0254-0096.tynxb.2023-0009

时空特性下基于图卷积神经网络的风电集群功率短期预测方法

乔宽龙^1,2, 董存^1,3, 车建峰², 蒋建东¹, 王勃²

作者信息 +

SHORT-TERM PREDICTION METHOD OF WIND POWER CLUSTERS BASED ON GRAPH CONVOLUTION NEURAL NETWORK UNDER SPITIO-TEMPORAL CHARACTERISTICS

Qiao Kuanlong^1,2, Dong Cun^1,3, Che Jianfeng², Jiang Jiandong¹, Wang Bo²

Author information +

文章历史 +

摘要

为解决传统风电集群功率预测方法忽略了不同位置点气象关联特性及单场预测无法快速得到风电集群整体功率的问题,并充分考虑到风电集群耦合的复杂时空特性,提出一种融合注意力机制的时空图卷积神经网络的风电集群功率短期预测方法。首先,计算区域内风电场站历史功率之间的互信息,提取特征邻接矩阵,并结合影响集群功率的气象特征变量转化为气象图数据。其次,构建图卷积神经网络（GCN）模型,从非欧式空间提取气象图节点关联特征。并馈入融合注意力机制（AM）的门控循环单元网络（GRU）增强时序特征中关键信息对风电集群功率的贡献程度。最后,基于中国西部某省风电集群的实际运行数据,验证所提方法的先进性和适应性。

Abstract

In order to solve the problems that the traditional wind power clusters prediction methods ignore the meteorological correlation characteristics of different locations and the single site prediction cannot quickly obtain the overall power of the wind power clusters, and fully consider the complex spatio-temporal characteristics of wind power clusters coupling, a short-term prediction method of wind power clusters based on attention mechanism and spatio-temporal graph convolution neural network is proposed. Initially, the mutual information between the historical power of wind farms in the region is calculated, the feature adjacency matrix is extracted, and the meteorological characteristic variables that affect the cluster power, which are converted into meteorological graph data. Furthermore, a graph convolution network （GCN） model is constructed to extract the correlation characteristics of meteorological graph nodes from non-European space. The gated recurrent unit （GRU） network, which incorporates the attention mechanism （AM）, is fed to enhance the contribution of key information in the temporal features to the power of wind power clusters. Finally, the progressiveness and adaptability of the proposed method is verified based on the actual operation data of the wind power cluster in a certain province in Western China.

导出引用

乔宽龙, 董存, 车建峰, 蒋建东, 王勃. 时空特性下基于图卷积神经网络的风电集群功率短期预测方法[J]. 太阳能学报. 2024, 45(5): 95-103 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0009

Qiao Kuanlong, Dong Cun, Che Jianfeng, Jiang Jiandong, Wang Bo. SHORT-TERM PREDICTION METHOD OF WIND POWER CLUSTERS BASED ON GRAPH CONVOLUTION NEURAL NETWORK UNDER SPITIO-TEMPORAL CHARACTERISTICS[J]. Acta Energiae Solaris Sinica. 2024, 45(5): 95-103 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0009

中图分类号： TM614

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