{"title":"使用混合增强海鸥优化算法和支持向量机预测短期风力发电量：一种高精度方法","authors":"Yuwei Liu, Lingling Li, Jiaqi Liu","doi":"10.1080/15435075.2024.2334498","DOIUrl":null,"url":null,"abstract":"Affected by the uncertainty of external environmental factors, wind power generation has significant characteristics of randomness and non-stationarity. Accurately predicting wind power is a necess...","PeriodicalId":14000,"journal":{"name":"International Journal of Green Energy","volume":"32 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Short-term wind power output prediction using hybrid-enhanced seagull optimization algorithm and support vector machine: A high-precision method\",\"authors\":\"Yuwei Liu, Lingling Li, Jiaqi Liu\",\"doi\":\"10.1080/15435075.2024.2334498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Affected by the uncertainty of external environmental factors, wind power generation has significant characteristics of randomness and non-stationarity. Accurately predicting wind power is a necess...\",\"PeriodicalId\":14000,\"journal\":{\"name\":\"International Journal of Green Energy\",\"volume\":\"32 1\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Green Energy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/15435075.2024.2334498\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Green Energy","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/15435075.2024.2334498","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Short-term wind power output prediction using hybrid-enhanced seagull optimization algorithm and support vector machine: A high-precision method
Affected by the uncertainty of external environmental factors, wind power generation has significant characteristics of randomness and non-stationarity. Accurately predicting wind power is a necess...
期刊介绍：
International Journal of Green Energy shares multidisciplinary research results in the fields of energy research, energy conversion, energy management, and energy conservation, with a particular interest in advanced, environmentally friendly energy technologies. We publish research that focuses on the forms and utilizations of energy that have no, minimal, or reduced impact on environment, economy and society.
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