Wenshan Xiao, Jun Wu, Zihui Guo, Wenxin Huang, Zichen Liu
{"title":"基于改进K-means的EV聚集建模","authors":"Wenshan Xiao, Jun Wu, Zihui Guo, Wenxin Huang, Zichen Liu","doi":"10.1117/12.2689418","DOIUrl":null,"url":null,"abstract":"An improved K-means clustering algorithm based on the initial clustering center is used to cluster the charging data of electric vehicles. The multi-classification method is studied, and the clustering effect of different number of clusters is analyzed with the contour coefficient as the evaluation standard. The simulation results show that this method can properly cluster the group characteristics of electric vehicles","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"EV aggregation modeling based on improved K-means\",\"authors\":\"Wenshan Xiao, Jun Wu, Zihui Guo, Wenxin Huang, Zichen Liu\",\"doi\":\"10.1117/12.2689418\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An improved K-means clustering algorithm based on the initial clustering center is used to cluster the charging data of electric vehicles. The multi-classification method is studied, and the clustering effect of different number of clusters is analyzed with the contour coefficient as the evaluation standard. The simulation results show that this method can properly cluster the group characteristics of electric vehicles\",\"PeriodicalId\":118234,\"journal\":{\"name\":\"4th International Conference on Information Science, Electrical and Automation Engineering\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"4th International Conference on Information Science, Electrical and Automation Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2689418\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"4th International Conference on Information Science, Electrical and Automation Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2689418","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An improved K-means clustering algorithm based on the initial clustering center is used to cluster the charging data of electric vehicles. The multi-classification method is studied, and the clustering effect of different number of clusters is analyzed with the contour coefficient as the evaluation standard. The simulation results show that this method can properly cluster the group characteristics of electric vehicles
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