CLUSTER SAMPLING FOR THE DEMAND SIDE MANAGEMENT OF POWER BIG DATA

Yongxin Zhang, Shi Shen
{"title":"CLUSTER SAMPLING FOR THE DEMAND SIDE MANAGEMENT OF POWER BIG DATA","authors":"Yongxin Zhang, Shi Shen","doi":"10.17781/P002208","DOIUrl":null,"url":null,"abstract":"In view of the DSM (Demand Side Management) under the big data environment, an improved FCM (Fuzzy C-Mean) clustering with Gauss data preprocessing is proposed, and the daily load curve of the whole study area was obtained with the electricity data. According to the formulation of the TOU (Time of Use) price, which is consistent with the characteristics of local users is given. The electricity suggestions based on the specific user load curve is provided, including the return of the DR (Demand Response). Subsequently, the sampling division is put forward to expand the improved model. Finally, the method is tested by the actual data, and the results show that it has a processing speed 10 times of the direct processing when the data is more","PeriodicalId":211757,"journal":{"name":"International journal of new computer architectures and their applications","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of new computer architectures and their applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17781/P002208","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

In view of the DSM (Demand Side Management) under the big data environment, an improved FCM (Fuzzy C-Mean) clustering with Gauss data preprocessing is proposed, and the daily load curve of the whole study area was obtained with the electricity data. According to the formulation of the TOU (Time of Use) price, which is consistent with the characteristics of local users is given. The electricity suggestions based on the specific user load curve is provided, including the return of the DR (Demand Response). Subsequently, the sampling division is put forward to expand the improved model. Finally, the method is tested by the actual data, and the results show that it has a processing speed 10 times of the direct processing when the data is more
电力大数据需求侧管理的聚类抽样
针对大数据环境下的需求侧管理(Demand Side Management, DSM),提出了一种基于高斯数据预处理的改进FCM (Fuzzy C-Mean)聚类方法,利用用电数据得到整个研究区域的日负荷曲线。根据制定的TOU (Time of Use)价格,给出符合当地用户特点的价格。根据具体的用户负荷曲线给出用电建议,包括返回DR (Demand Response)。随后,提出了抽样划分,对改进模型进行了扩展。最后,通过实际数据对该方法进行了测试,结果表明,当数据量较大时,该方法的处理速度是直接处理的10倍
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
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