{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T17:59:01Z","timestamp":1767981541932,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:00:00Z","timestamp":1684454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shandong Provincial Nature Science Foundation of China","award":["ZR2021QF115"],"award-info":[{"award-number":["ZR2021QF115"]}]},{"name":"Shandong Provincial Nature Science Foundation of China","award":["62203249"],"award-info":[{"award-number":["62203249"]}]},{"name":"Shandong Provincial Nature Science Foundation of China","award":["202228077"],"award-info":[{"award-number":["202228077"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZR2021QF115"],"award-info":[{"award-number":["ZR2021QF115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62203249"],"award-info":[{"award-number":["62203249"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202228077"],"award-info":[{"award-number":["202228077"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"\u201c20 New Universities\u201d Project of Jinan City","award":["ZR2021QF115"],"award-info":[{"award-number":["ZR2021QF115"]}]},{"name":"\u201c20 New Universities\u201d Project of Jinan City","award":["62203249"],"award-info":[{"award-number":["62203249"]}]},{"name":"\u201c20 New Universities\u201d Project of Jinan City","award":["202228077"],"award-info":[{"award-number":["202228077"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wind speed prediction is very important in the field of wind power generation technology. It is helpful for increasing the quantity and quality of generated wind power from wind farms. By using univariate wind speed time series, this paper proposes a hybrid wind speed prediction model based on Autoregressive Moving Average-Support Vector Regression (ARMA-SVR) and error compensation. First, to explore the balance between the computation cost and the sufficiency of the input features, the characteristics of ARMA are employed to determine the number of historical wind speeds for the prediction model. According to the selected number of input features, the original data are divided into multiple groups that can be used to train the SVR-based wind speed prediction model. Furthermore, in order to compensate for the time lag introduced by the frequent and sharp fluctuations in natural wind speed, a novel Extreme Learning Machine (ELM)-based error correction technique is developed to decrease the deviations between the predicted wind speed and its real values. By this means, more accurate wind speed prediction results can be obtained. Finally, verification studies are conducted by using real data collected from actual wind farms. Comparison results demonstrate that the proposed method can achieve better prediction results than traditional approaches.<\/jats:p>","DOI":"10.3390\/s23104905","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T10:08:55Z","timestamp":1684490935000},"page":"4905","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Wind Speed Prediction Based on Error Compensation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1584-695X","authenticated-orcid":false,"given":"Xuguo","family":"Jiao","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China"},{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Daoyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9501-6723","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center, Qilu University of Technology, Shandong Academy of Sciences, Jinan 250014, China"}]},{"given":"Yanbing","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China"}]},{"given":"Wenfeng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China"}]},{"given":"Liping","family":"Xin","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109422","DOI":"10.1016\/j.rser.2019.109422","article-title":"Approaches to wind power curve modeling: A review and discussion","volume":"116","author":"Wang","year":"2019","journal-title":"Renew. 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