Recognizing the intricate spatiotemporal correlation(STC)among wind farms(WFs)is critical to achieving better predictions for wind farm clusters(WFCs).To describe the STC accurately,this paper em-ploys the wind angula...Recognizing the intricate spatiotemporal correlation(STC)among wind farms(WFs)is critical to achieving better predictions for wind farm clusters(WFCs).To describe the STC accurately,this paper em-ploys the wind angular field method to transform the wind series of WFs into different 2-D feature maps,and then construct homogeneous and heterogeneous STC graphs from these maps.The graphs are dynamically updated at the frequency of data update to capture time-varying STC among WFs.Finally,a dynamic graph attention network,designed according to the STC graphs,is established for WFC prediction.Through the above process,dynamic and accurate descriptions of STC are realized in WFC prediction.From the case study of a large-scale WFC with a capacity over 5800 MW in Northeast China,the proposed method reduces the root mean square error of the prediction in the next 24 hours by 2.67%.展开更多
基金supported by the National Key R&D Program of China(No.2022YFB2403000).
文摘Recognizing the intricate spatiotemporal correlation(STC)among wind farms(WFs)is critical to achieving better predictions for wind farm clusters(WFCs).To describe the STC accurately,this paper em-ploys the wind angular field method to transform the wind series of WFs into different 2-D feature maps,and then construct homogeneous and heterogeneous STC graphs from these maps.The graphs are dynamically updated at the frequency of data update to capture time-varying STC among WFs.Finally,a dynamic graph attention network,designed according to the STC graphs,is established for WFC prediction.Through the above process,dynamic and accurate descriptions of STC are realized in WFC prediction.From the case study of a large-scale WFC with a capacity over 5800 MW in Northeast China,the proposed method reduces the root mean square error of the prediction in the next 24 hours by 2.67%.
