DEMAND FORECASTING OF INDUSTRIAL ELECTRICAL ENERGY CONSUMPTION FOR TAMILNADU STATE

Q4 Engineering
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.
泰米尔纳德邦工业用电需求预测
能源消耗预测对印度放松管制的电力行业至关重要,尤其是在泰米尔纳德邦。各种各样的数学方法已经被开发出来用于能源预测。本研究采用2005年至2011年的历史数据集,包括人口(POP)、国家国内生产总值(GSDP)、年峰值需求(YPD)和人均收入(PCI)。首先,建立了多元线性回归模型(MLRM)。采用神经网络方法对回归模型输出进行优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Academic Journal of Manufacturing Engineering
Academic Journal of Manufacturing Engineering Engineering-Industrial and Manufacturing Engineering
CiteScore
0.40
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信