{"title":"具有专门结构的前馈神经网络用于估计温度对电力负荷的影响","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":"{\"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}","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}
Feedforward neural network with a specialized architecture for estimation of the temperature influence on the electric load
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.