{"title":"Feedforward neural network with a specialized architecture for estimation of the temperature influence on the electric load","authors":"Y. Bodyanskiy, S. Popov, T. Rybalchenko","doi":"10.1109/IS.2008.4670444","DOIUrl":null,"url":null,"abstract":"The problem of temperature-load relationship revealing is considered. A specialized architecture of a feedforward neural network is proposed that provides separation of temperature influence from other factors and its analysis in an explicit form. The proposed approach is illustrated by computational experiments with data from two locations with different climatic conditions.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 4th International IEEE Conference Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IS.2008.4670444","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The problem of temperature-load relationship revealing is considered. A specialized architecture of a feedforward neural network is proposed that provides separation of temperature influence from other factors and its analysis in an explicit form. The proposed approach is illustrated by computational experiments with data from two locations with different climatic conditions.
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