Aiming at the problem that the spatial information cannot be effectively extracted due to the influence of spatiotempo-ral fluctuation and randomness in wind power forecasting,resulting in insufficient prediction accuracy,a model named STAGCN-Informer-DCP is proposed based on Variational Mode Decomposition(VMD),fusion of Spatiotemporal Attention Graph Convolutional Network(STAGCN)and improved Informer combination model.Firstly,VMD is used to perform modal de-composition on the original features,and the feature information on different time scales is extracted.At the same time,the selec-tion of core parameters(penalty factor and K value)of VMD is optimized by using northern goshawk optimization(NGO).Sec-ondly,the STAGCN module that integrates spatio-temporal attention is used to dynamically capture the spatio-temporal features of the target wind turbine and its neighbors,and fuses them with the original signal components to obtain a feature vector carrying spatial scale information.Finally,the improved Informer model is used to extract the long-term dependencies of temporal context and realizes multi-step output prediction.The experimental results show that the combination model can better capture the dy-namic space-time dependence,and effectively improve the accuracy of medium and long-term wind power forecasting.
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