Muchammad Dicky Sanjaya, C. Setianingsih, R. E. Saputra
{"title":"Electricity Usage Clustering with K-means++ Algorithm","authors":"Muchammad Dicky Sanjaya, C. Setianingsih, R. E. Saputra","doi":"10.1109/IAICT52856.2021.9532534","DOIUrl":null,"url":null,"abstract":"Global electricity consumption is increasing every year. Electricity has become a necessary need for every sector, from a household to government and industry. With today's technology, data also has become more important, including electricity data. The right tools and techniques can extract valuable information from data. Using a K-Means++ algorithm to cluster electricity data can help to determine when the usage is low, moderate, and high. In this study, there three scenarios of clustering; hourly, daily, and monthly. The silhouette score of this experiment ranges from 0.68 to 0.71, and the DB Index ranges from 0.30 to 0.51.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAICT52856.2021.9532534","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
Global electricity consumption is increasing every year. Electricity has become a necessary need for every sector, from a household to government and industry. With today's technology, data also has become more important, including electricity data. The right tools and techniques can extract valuable information from data. Using a K-Means++ algorithm to cluster electricity data can help to determine when the usage is low, moderate, and high. In this study, there three scenarios of clustering; hourly, daily, and monthly. The silhouette score of this experiment ranges from 0.68 to 0.71, and the DB Index ranges from 0.30 to 0.51.
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