{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:32:38Z","timestamp":1759332758631,"version":"3.37.3"},"reference-count":19,"publisher":"Wiley","license":[{"start":{"date-parts":[[2018,9,2]],"date-time":"2018-09-02T00:00:00Z","timestamp":1535846400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research Project of Henan Higher Education Institution","award":["70556"],"award-info":[{"award-number":["70556"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Advances in Fuzzy Systems"],"published-print":{"date-parts":[[2018,9,2]]},"abstract":"<jats:p>High-precision wind power forecast can reduce the volatility and intermittency of wind power output, which is conducive to the stable operation of the power system and improves the system's effective capacity for large-scale wind power consumption. In the wind farm, the wind turbines are located in different space locations, and its output characteristics are also affected by wind direction, wake effect, and operation conditions. Based on this, two-step ultra-short-term forecast model was proposed. Firstly, fuzzy C-means clustering (FCM) theory was used to cluster the units according to the out characteristics of wind turbines. Secondly, a prediction model of RBF neural network is established for the classification clusters, respectively, and the ultra-short-term power forecast is performed for each unit. Finally, the above results are compared with the RBF single prediction model established by unclassified g wind turbines. A case study of a wind farm in northern China is carried out. The results show that the proposed method can effectively improve the prediction accuracy of wind power and prove the effectiveness of the method.<\/jats:p>","DOI":"10.1155\/2018\/9805748","type":"journal-article","created":{"date-parts":[[2018,9,2]],"date-time":"2018-09-02T19:37:40Z","timestamp":1535917060000},"page":"1-7","source":"Crossref","is-referenced-by-count":5,"title":["Ultra-Short-Term Prediction of Wind Power Based on Fuzzy Clustering and RBF Neural Network"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6856-5644","authenticated-orcid":true,"given":"Huang","family":"Hui","sequence":"first","affiliation":[{"name":"Institute of Water Resources and Hydro-Electric Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"},{"name":"School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Rong","sequence":"additional","affiliation":[{"name":"Institute of Water Resources and Hydro-Electric Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wang","family":"Songkai","sequence":"additional","affiliation":[{"name":"Institute of Water Resources and Hydro-Electric Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2016.04.036"},{"issue":"5","key":"2","first-page":"15","volume":"28","year":"2013","journal-title":"Transactions of China Electrotechnical Society"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2017.06.075"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2015.12.082"},{"issue":"6","key":"5","first-page":"1815","volume":"41","year":"2017","journal-title":"Power System Technology"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2013.2277585"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.03.096"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/63\/1\/012005"},{"issue":"4","key":"10","doi-asserted-by":"crossref","first-page":"697","DOI":"10.3390\/en11040697","volume":"11","year":"2018","journal-title":"Energies"},{"issue":"14","key":"11","first-page":"126","volume":"38","year":"2014","journal-title":"Automation of Electric Power Systems"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2016.12.071"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2017.08.008"},{"issue":"9","key":"15","first-page":"90","volume":"41","year":"2017","journal-title":"Automation of Electric Power Systems"},{"year":"2012","key":"16"},{"key":"17","first-page":"1","volume":"3","year":"2017","journal-title":"Cluster Computing"},{"issue":"1","key":"18","first-page":"301","volume":"42","year":"2018","journal-title":"Power System Technology"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2012.2187803"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1007\/s40565-015-0171-6"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2017.01.104"}],"container-title":["Advances in Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/afs\/2018\/9805748.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/afs\/2018\/9805748.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/afs\/2018\/9805748.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,9,2]],"date-time":"2018-09-02T19:37:46Z","timestamp":1535917066000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/afs\/2018\/9805748\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,2]]},"references-count":19,"alternative-id":["9805748","9805748"],"URL":"https:\/\/doi.org\/10.1155\/2018\/9805748","relation":{},"ISSN":["1687-7101","1687-711X"],"issn-type":[{"type":"print","value":"1687-7101"},{"type":"electronic","value":"1687-711X"}],"subject":[],"published":{"date-parts":[[2018,9,2]]}}}