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Firstly, the original wind energy data are decomposed into subsequences of natural mode functions with different frequencies by using the variational mode decomposition (VMD) algorithm. The VMD algorithm relies on a decision support system for the decomposition of the data into natural mode functions. Once the decomposition is performed, the nonlinear and dynamic behavior are extracted from each natural mode function. Next, the BiLSTM network is chosen as the generation model of the generative adversarial network (WGAN-GP) to obtain the data distribution characteristics of wind power\u2019s output. The convolutional neural network (CNN) is chosen as the discrimination model, and the semi-supervised regression layer is utilized to design the discrimination model to predict wind power. The minimum\u2013maximum game is formed by the BiLSTM and CNN network models to improve the quality of sample generation and further improve the prediction accuracy. Finally, the actual data of a wind farm in Jiuquan City, Gansu Province, China is taken as an example to prove that the proposed method has superior performance compared with other prediction algorithms.<\/jats:p>","DOI":"10.1007\/s00500-021-06725-x","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T10:03:42Z","timestamp":1642413822000},"page":"10607-10621","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Short-term prediction of wind power based on BiLSTM\u2013CNN\u2013WGAN-GP"],"prefix":"10.1007","volume":"26","author":[{"given":"Ling","family":"Huang","sequence":"first","affiliation":[]},{"given":"Linxia","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiaoyuan","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Dongsheng","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"6725_CR1","unstructured":"Arjovsky M, Bottou L (2017) Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862"},{"issue":"2","key":"6725_CR2","doi-asserted-by":"publisher","first-page":"103","DOI":"10.12720\/sgce.8.2.103-110","volume":"8","author":"U Cali","year":"2019","unstructured":"Cali U, Sharma V (2019) Short-term wind power forecasting using long-short term memory based recurrent neural network model and variable selection. 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