{"title":"混合密度网每月每小时用于风力发电预测","authors":"D. Vallejo, R. Chaer","doi":"10.1109/urucon53396.2021.9647384","DOIUrl":null,"url":null,"abstract":"In this work, the training of a set of Mixture Density Networks (MDNs) type of Neural Networks (NNs) is presented. This set of networks is used to forecast the power generated by a wind farm in Uruguay. The advantages and challenges of using a MDN per hour-month against a single MDN are discussed.","PeriodicalId":337257,"journal":{"name":"2021 IEEE URUCON","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mixture Density Networks per hour-month applied to wind power generation forecast\",\"authors\":\"D. Vallejo, R. Chaer\",\"doi\":\"10.1109/urucon53396.2021.9647384\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, the training of a set of Mixture Density Networks (MDNs) type of Neural Networks (NNs) is presented. This set of networks is used to forecast the power generated by a wind farm in Uruguay. The advantages and challenges of using a MDN per hour-month against a single MDN are discussed.\",\"PeriodicalId\":337257,\"journal\":{\"name\":\"2021 IEEE URUCON\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE URUCON\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/urucon53396.2021.9647384\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE URUCON","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/urucon53396.2021.9647384","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mixture Density Networks per hour-month applied to wind power generation forecast
In this work, the training of a set of Mixture Density Networks (MDNs) type of Neural Networks (NNs) is presented. This set of networks is used to forecast the power generated by a wind farm in Uruguay. The advantages and challenges of using a MDN per hour-month against a single MDN are discussed.
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