Predicting the Short Term Electrical Energy Consumption using Dynamic Model and Genetic Algorithm

K. Eskaf, I. El-Mohr
{"title":"Predicting the Short Term Electrical Energy Consumption using Dynamic Model and Genetic Algorithm","authors":"K. Eskaf, I. El-Mohr","doi":"10.1109/ICCTA32607.2013.9529847","DOIUrl":null,"url":null,"abstract":"Residential and commercial buildings accounted for about 70% of the total electricity consumption in the world. Many researchers are working hard to reduce building electrical energy consumption. This work is concerned with managing short term electrical energy consumption by trying to predict this consumption in the near future (6 months) on the basis of the current consumption.The goal of this paper is to determine the future electrical energy consumption using a Genetic Algorithm. Unlike other approaches, which involved in questioning the users, feature extraction procedures were implemented on electrical energy consumption time series in order to extract knowledge. The Genetic Algorithm generates the future value of electrical energy consumption with an accepted accuracy.","PeriodicalId":405465,"journal":{"name":"2013 23rd International Conference on Computer Theory and Applications (ICCTA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 23rd International Conference on Computer Theory and Applications (ICCTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCTA32607.2013.9529847","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Residential and commercial buildings accounted for about 70% of the total electricity consumption in the world. Many researchers are working hard to reduce building electrical energy consumption. This work is concerned with managing short term electrical energy consumption by trying to predict this consumption in the near future (6 months) on the basis of the current consumption.The goal of this paper is to determine the future electrical energy consumption using a Genetic Algorithm. Unlike other approaches, which involved in questioning the users, feature extraction procedures were implemented on electrical energy consumption time series in order to extract knowledge. The Genetic Algorithm generates the future value of electrical energy consumption with an accepted accuracy.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于动态模型和遗传算法的短期电能消耗预测
住宅和商业建筑约占世界总用电量的70%。许多研究人员正在努力降低建筑的电能消耗。这项工作涉及管理短期电能消耗,试图在当前消耗的基础上预测近期(6个月)的电量消耗。本文的目标是利用遗传算法来确定未来的电能消耗。与其他方法不同的是,特征提取过程是在电能消耗时间序列上实现的,以提取知识。遗传算法以可接受的精度生成电能消耗的未来值。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
NC-OFDM Cognitive Radio Wireless Networks with Efficient LDPC Codes Identifying Learning Style Using Students Behavior in E-Learning Management Systems Based on JRIP Classifier Module Integrated Converter for Photovoltaic Applications with Different Control Strategies Processing of Corneal Images With A Cepstral Approach Feature Extraction for Trajectory Representation of Sign Language Recognition
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1