Abstract:  Aiming at the problem that the spatial information cannot be effectively extracted due to the influence of spatiotemporal 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 decomposition on the original features， and the feature information on different time scales is extracted. At the same time， the selection of core parameters （penalty factor and K value） of VMD is optimized by using northern goshawk optimization （NGO）. Secondly， 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 dynamic space-time dependence， and effectively improve the accuracy of medium and long-term wind power forecasting.

Key words:  , variational mode decomposition, spatiotemporal attention mechanism, Informer model, northern goshawk optimization, graph convolutional network