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This paper introduces a deep learning model for wind power prediction that integrates an Attention mechanism with a convolutional neural network (CNN) and a gated recurrent unit (GRU) neural network. Addressing the randomness, intermittency, volatility and uncertainty of wind speed, we first apply swarm decomposition (SWD) to preprocess the original wind power data into subsequences. Subsequently, the CNN extracts spatial features, and the GRU identifies temporal correlations. The Attention mechanism enhances feature significance, further optimizing prediction accuracy. Complex error sequences generated by the CNN\u2013GRU\u2013Attention (CGA) model are corrected using the autoregressive integrated moving average (ARIMA). We evaluated the model\u2019s performance using three wind power datasets against 16 other models, employing six evaluation indices (MSE, RMSE, MAPE, Theil\u2019s [Formula: see text], TIC and SPL) and the Diebold\u2013Mariano (DM) test and model confidence set (MCS) for model testing. Our results demonstrate the proposed model\u2019s superior accuracy and efficiency in predicting wind power. <\/jats:p>","DOI":"10.1142\/s0218126624502840","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T14:43:20Z","timestamp":1715265800000},"source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning Wind Power Prediction Model Based on Attention Mechanism-Based Convolutional Neural Network and Gated Recurrent Unit Neural Network"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3104-4563","authenticated-orcid":false,"given":"Zai-Hong","family":"Hou","sequence":"first","affiliation":[{"name":"College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou, Gansu 730070, P. R. 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