{"title":"基于广义回归神经网络的风预报","authors":"Chun-Yao Lee, Yan-Lou He","doi":"10.1109/ISDEA.2012.520","DOIUrl":null,"url":null,"abstract":"This study adopts the general regression neural network (GRNN) to predict wind speeds. The training data sets are the real wind speeds obtained from CKS International Airport. The 5 days (120 hours) of the three year from 2006 to 2008 is selected as an example to appraise the prediction performance by using GRNN. Comparing to the traditional linear time-series-based model, the superiority of GRNN method to wind prediction can be valid.","PeriodicalId":267532,"journal":{"name":"2012 Second International Conference on Intelligent System Design and Engineering Application","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Wind Prediction Based on General Regression Neural Network\",\"authors\":\"Chun-Yao Lee, Yan-Lou He\",\"doi\":\"10.1109/ISDEA.2012.520\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study adopts the general regression neural network (GRNN) to predict wind speeds. The training data sets are the real wind speeds obtained from CKS International Airport. The 5 days (120 hours) of the three year from 2006 to 2008 is selected as an example to appraise the prediction performance by using GRNN. Comparing to the traditional linear time-series-based model, the superiority of GRNN method to wind prediction can be valid.\",\"PeriodicalId\":267532,\"journal\":{\"name\":\"2012 Second International Conference on Intelligent System Design and Engineering Application\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-01-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Second International Conference on Intelligent System Design and Engineering Application\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISDEA.2012.520\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Second International Conference on Intelligent System Design and Engineering Application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISDEA.2012.520","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wind Prediction Based on General Regression Neural Network
This study adopts the general regression neural network (GRNN) to predict wind speeds. The training data sets are the real wind speeds obtained from CKS International Airport. The 5 days (120 hours) of the three year from 2006 to 2008 is selected as an example to appraise the prediction performance by using GRNN. Comparing to the traditional linear time-series-based model, the superiority of GRNN method to wind prediction can be valid.
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