{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T20:04:35Z","timestamp":1784232275666,"version":"3.55.0"},"reference-count":28,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T00:00:00Z","timestamp":1713139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Applied Research and Development Project of Gansu Academy of Sciences","award":["2021JK-04"],"award-info":[{"award-number":["2021JK-04"]}]},{"name":"Applied Research and Development Project of Gansu Academy of Sciences","award":["2023JSCX0004"],"award-info":[{"award-number":["2023JSCX0004"]}]},{"name":"Applied Research and Development Project of Gansu Academy of Sciences","award":["HX2023C50800001"],"award-info":[{"award-number":["HX2023C50800001"]}]},{"name":"Science and Technology Planning Project of Chengguan District of Lanzhou","award":["2021JK-04"],"award-info":[{"award-number":["2021JK-04"]}]},{"name":"Science and Technology Planning Project of Chengguan District of Lanzhou","award":["2023JSCX0004"],"award-info":[{"award-number":["2023JSCX0004"]}]},{"name":"Science and Technology Planning Project of Chengguan District of Lanzhou","award":["HX2023C50800001"],"award-info":[{"award-number":["HX2023C50800001"]}]},{"name":"Lanzhou University of Technology and Longnan Power Supply Company of State Grid Gansu Electric Power Company","award":["2021JK-04"],"award-info":[{"award-number":["2021JK-04"]}]},{"name":"Lanzhou University of Technology and Longnan Power Supply Company of State Grid Gansu Electric Power Company","award":["2023JSCX0004"],"award-info":[{"award-number":["2023JSCX0004"]}]},{"name":"Lanzhou University of Technology and Longnan Power Supply Company of State Grid Gansu Electric Power Company","award":["HX2023C50800001"],"award-info":[{"award-number":["HX2023C50800001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Soft sensors have been extensively utilized to approximate real-time power prediction in wind power generation, which is challenging to measure instantaneously. The short-term forecast of wind power aims at providing a reference for the dispatch of the intraday power grid. This study proposes a soft sensor model based on the Long Short-Term Memory (LSTM) network by combining data preprocessing with Variational Modal Decomposition (VMD) to improve wind power prediction accuracy. It does so by adopting the isolation forest algorithm for anomaly detection of the original wind power series and processing the missing data by multiple imputation. Based on the process data samples, VMD technology is used to achieve power data decomposition and noise reduction. The LSTM network is introduced to predict each modal component separately, and further sum reconstructs the prediction results of each component to complete the wind power prediction. From the experimental results, it can be seen that the LSTM network which uses an Adam optimizing algorithm has better convergence accuracy. The VMD method exhibited superior decomposition outcomes due to its inherent Wiener filter capabilities, which effectively mitigate noise and forestall modal aliasing. The Mean Absolute Percentage Error (MAPE) was reduced by 9.3508%, which indicates that the LSTM network combined with the VMD method has better prediction accuracy.<\/jats:p>","DOI":"10.3390\/s24082521","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T08:08:12Z","timestamp":1713168492000},"page":"2521","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["LSTM Short-Term Wind Power Prediction Method Based on Data Preprocessing and Variational Modal Decomposition for Soft Sensors"],"prefix":"10.3390","volume":"24","author":[{"given":"Peng","family":"Lei","sequence":"first","affiliation":[{"name":"Network & Information Center, Lanzhou University of Technology, Lanzhou 730050, China"},{"name":"School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanglan","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China"},{"name":"Institute of Sensing Technology, Gansu Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changsheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"126283","DOI":"10.1016\/j.energy.2022.126283","article-title":"A novel prediction model for wind power based on improved long short-term memory neural network","volume":"265","author":"Wang","year":"2023","journal-title":"Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.egyr.2023.02.061","article-title":"VMD-CAT: A hybrid model for short-term wind power prediction","volume":"9","author":"Zheng","year":"2023","journal-title":"Energy Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103053","DOI":"10.1016\/j.jprocont.2023.103053","article-title":"Novel complex-valued long short-term memory network integrating variational mode decomposition for soft sensor","volume":"129","author":"Hu","year":"2023","journal-title":"J. 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