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The water turbine bearing temperature forecasting is a challenging problem due to the running data being nonlinear and non-stationary. This work proposes a hybrid SE-CNN-LSTM forecasting model by integrating the advantages of convolutional neural Networks (CNN), long short-term memory (LSTM) networks and squeeze-and-excitation (SE) attention mechanism. CNN is designed to extract local features from raw temperature time series data to capture short-term dependencies in temperature data. LSTM is designed by incorporating a state-space representation to better model the dynamic characteristics of long-term correlations in temperature data. SE attention mechanism is integrated into the CNN-LSTM to form the SE-CNN-LSTM network for accurate prediction of the water turbine\u2019s guide bearing temperature. Experiments are conducted by using real operational data from a pumped-storage power station. Experimental results demonstrated that the proposed SE-CNN-LSTM model outperforms traditional prediction methods in terms of multiple evaluation metrics, exhibiting superior prediction accuracy and robustness.<\/jats:p>","DOI":"10.1177\/01423312251352885","type":"journal-article","created":{"date-parts":[[2025,8,17]],"date-time":"2025-08-17T11:31:15Z","timestamp":1755430275000},"page":"2508-2517","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid SE-attention-CNN-LSTM network for hydraulic turbine water guide bearing temperature prediction in hydropower generation station"],"prefix":"10.1177","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4362-8946","authenticated-orcid":false,"given":"Yu","family":"Gong","sequence":"first","affiliation":[{"name":"School of Electric Power, South China University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2179-7526","authenticated-orcid":false,"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Branch Company of Maintenance &amp; Test, China Southern Power Grid Energy Storage Co., Ltd., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5188-6672","authenticated-orcid":false,"given":"Junhuang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Guangzhou Power Electrical Technology Co., Ltd., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1024-1399","authenticated-orcid":false,"given":"Langwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1135-6788","authenticated-orcid":false,"given":"Yongjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electric Power, South China University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,8,17]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.102068"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/app12094184"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101819"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/01423312241279701"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/app132212341"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-05958-z"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-023-08172-2"},{"key":"e_1_3_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3180482"},{"key":"e_1_3_2_10_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.3024672"},{"key":"e_1_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/19475705.2022.2102942"},{"key":"e_1_3_2_12_1","doi-asserted-by":"publisher","DOI":"10.37868\/sei.v3i2.id146"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.1177\/01423312241273855"},{"key":"e_1_3_2_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.05.230"},{"key":"e_1_3_2_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2023.109047"},{"key":"e_1_3_2_16_1","doi-asserted-by":"publisher","DOI":"10.1177\/0142331220966425"},{"issue":"11","key":"e_1_3_2_17_1","first-page":"109","article-title":"Applications and challenges of AI technology in power plant intelligent construction","volume":"37","author":"Liao M","year":"2024","unstructured":"Liao M, Hu L, Zhang Y (2024a) Applications and challenges of AI technology in power plant intelligent construction. 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