基于改进K-Means的低压地区线损计算

Zhu Yun, Yao Mengting, L. Junjie, Chen Ji, He Penghui
{"title":"基于改进K-Means的低压地区线损计算","authors":"Zhu Yun, Yao Mengting, L. Junjie, Chen Ji, He Penghui","doi":"10.1109/POWERCON.2018.8601637","DOIUrl":null,"url":null,"abstract":"The low-voltage districts is complex, large in number and have various types of users, which makes the calculation of line loss more complicated. Therefore, combining the research status of line loss calculation at home and abroad with the related algorithms of data mining, based on the power consumption data of low-voltage districts, we proposed a prediction model of low-voltage districts line loss based on improved K-Means algorithm. First of all, cluster the data pre-processed. Secondly, establish multivariate linear regression prediction model for each clustered data. What's more, input the sample data and determine which type of the districts they belongs by Euclidean distance so as to predict the line loss rate. Finally, analyze the prediction error of the model by comparing the actual value with the predicted value. The analysis of the actual data of low-voltage districts shows that the improved K-Means has the characteristic of high prediction accuracy and it's simple, fast and practical. It has superior performance in analyzing and processing the data in low-voltage districts, which can effectively improve the management, standardization and refinement level of low-voltage districts.","PeriodicalId":260947,"journal":{"name":"2018 International Conference on Power System Technology (POWERCON)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Line loss calculation of low-voltage districts based on improved K-Means\",\"authors\":\"Zhu Yun, Yao Mengting, L. Junjie, Chen Ji, He Penghui\",\"doi\":\"10.1109/POWERCON.2018.8601637\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The low-voltage districts is complex, large in number and have various types of users, which makes the calculation of line loss more complicated. Therefore, combining the research status of line loss calculation at home and abroad with the related algorithms of data mining, based on the power consumption data of low-voltage districts, we proposed a prediction model of low-voltage districts line loss based on improved K-Means algorithm. First of all, cluster the data pre-processed. Secondly, establish multivariate linear regression prediction model for each clustered data. What's more, input the sample data and determine which type of the districts they belongs by Euclidean distance so as to predict the line loss rate. Finally, analyze the prediction error of the model by comparing the actual value with the predicted value. The analysis of the actual data of low-voltage districts shows that the improved K-Means has the characteristic of high prediction accuracy and it's simple, fast and practical. It has superior performance in analyzing and processing the data in low-voltage districts, which can effectively improve the management, standardization and refinement level of low-voltage districts.\",\"PeriodicalId\":260947,\"journal\":{\"name\":\"2018 International Conference on Power System Technology (POWERCON)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Power System Technology (POWERCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/POWERCON.2018.8601637\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Power System Technology (POWERCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/POWERCON.2018.8601637","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

低压区结构复杂、数量多、用户类型多,使得线损计算更加复杂。因此,结合国内外线损计算的研究现状和数据挖掘的相关算法,基于低压地区用电数据,提出了一种基于改进K-Means算法的低压地区线损预测模型。首先,对预处理后的数据进行聚类。其次,对各聚类数据建立多元线性回归预测模型。输入样本数据,根据欧氏距离判断属于哪一类区域,从而预测线路损失率。最后,通过实际值与预测值的比较,分析了模型的预测误差。对低压地区实际数据的分析表明,改进的K-Means具有预测精度高、简单、快速、实用的特点。在低压地区数据分析处理方面具有优越的性能,可有效提高低压地区的管理、规范化和精细化水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Line loss calculation of low-voltage districts based on improved K-Means
The low-voltage districts is complex, large in number and have various types of users, which makes the calculation of line loss more complicated. Therefore, combining the research status of line loss calculation at home and abroad with the related algorithms of data mining, based on the power consumption data of low-voltage districts, we proposed a prediction model of low-voltage districts line loss based on improved K-Means algorithm. First of all, cluster the data pre-processed. Secondly, establish multivariate linear regression prediction model for each clustered data. What's more, input the sample data and determine which type of the districts they belongs by Euclidean distance so as to predict the line loss rate. Finally, analyze the prediction error of the model by comparing the actual value with the predicted value. The analysis of the actual data of low-voltage districts shows that the improved K-Means has the characteristic of high prediction accuracy and it's simple, fast and practical. It has superior performance in analyzing and processing the data in low-voltage districts, which can effectively improve the management, standardization and refinement level of low-voltage districts.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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