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AuthorYu, Guangzheng
AuthorLiu, Chengquan
AuthorTang, Bo
AuthorChen, Rusi
AuthorLu, Liu
AuthorCui, Chaoyue
AuthorHu, Yue
AuthorShen, Lingxu
AuthorMuyeen, S.M.
Available date2023-02-26T08:29:58Z
Publication Date2022
Publication NameRenewable Energy
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.renene.2022.08.142
URIhttp://hdl.handle.net/10576/40377
AbstractAccurate regional wind power prediction is of great significance to the wind farm clusters integration and the economic dispatch of the regional power grid. The complex spatiotemporally coupled characteristics between multiple wind farms bring challenges to wind power prediction (WPP) of regional wind farm clusters. In this context, this paper proposes a regional WPP method using spatiotemporally multiple clustering algorithm and hybrid neural network to learn the potential spatial-temporal dependencies of regional wind farms. In which, a long-term daily power curve similarity method is proposed to identify spatially correlative wind power plants in long-term. Furthermore, the spatio-temporal wind farm sub-clusters are dynamically recognized by the similar fluctuation trend of short-term power sequences. On this basis, a spatial-temporal integrated prediction model consisting of the improved convolutional neural network (I-CNN) and the bidirectional long short-term memory (BILSTM) network is established for spatio-temporal sub-cluster based on point clouds distribution. Finally, the effectiveness of the proposed regional wind power forecasting framework is validated by using the Wind Integration National Dataset Toolkit, and the results show that the method improves accuracy effectively. 2022 Elsevier Ltd
SponsorThis work is supported by the National Natural Science Foundation of China (No. 52207121 and No. 52007167 ) and the technology project of Electric Power Research Institute of State Grid Hubei Electric Power Co ., Ltd. (Grant number: B31532225680 ).
Languageen
PublisherElsevier Ltd
SubjectBILSTM
I-CNN
Regional wind farms
Spatial-temporal correlation
Wind power prediction
TitleShort term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution
TypeArticle
Pagination599-612
Volume Number199
dc.accessType Abstract Only


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