T. Senthilkumar, R. Venkatesh, J SamCharles, P. Senthil, Praveen kumar
{"title":"DEMAND FORECASTING OF INDUSTRIAL ELECTRICAL ENERGY CONSUMPTION FOR TAMILNADU STATE","authors":"T. Senthilkumar, R. Venkatesh, J SamCharles, P. Senthil, Praveen kumar","doi":"10.37255/jme.v4i2pp093-096","DOIUrl":null,"url":null,"abstract":"Energy consumption forecasting is vitally important for the deregulated electricity industry\nin India, particularly in Tamilnadu state. A large variety of mathematical methods have been\ndeveloped for energy forecasting. In this study, historical data set including population (POP), Gross state domestic Product (GSDP), Yearly peak demand (YPD), and Per Capita income (PCI) were considered from the year 2005 to 2011.Firstly, the multiple linear regression model (MLRM)has been developed. The regression model outputs were optimized using Neural network method.","PeriodicalId":38895,"journal":{"name":"Academic Journal of Manufacturing Engineering","volume":"22 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Journal of Manufacturing Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37255/jme.v4i2pp093-096","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
Energy consumption forecasting is vitally important for the deregulated electricity industry
in India, particularly in Tamilnadu state. A large variety of mathematical methods have been
developed for energy forecasting. In this study, historical data set including population (POP), Gross state domestic Product (GSDP), Yearly peak demand (YPD), and Per Capita income (PCI) were considered from the year 2005 to 2011.Firstly, the multiple linear regression model (MLRM)has been developed. The regression model outputs were optimized using Neural network method.