{"title":"住宅用电量在单一低成本电表上的分解","authors":"M. Tesfaye, M. Nardello, D. Brunelli","doi":"10.1109/EESMS.2017.8052678","DOIUrl":null,"url":null,"abstract":"Demand and cost of electricity is expected to grow in the next years. This has raised interest in monitoring energy usage to reduce losses, and to provide real-time feedback about the cost of the electrical power consumed. This paper focuses on the implementation of a stand-alone system capable of real-time tracking of the power used and that provides power consumption estimation for each device from a single point of measurement. The learning activity is done by detecting the possible state of the electrical devices using a clustering algorithm, which involves k-means technique to analyze and detect the state of an appliance.","PeriodicalId":285890,"journal":{"name":"2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Residential electrical consumption disaggregation on a single low-cost meter\",\"authors\":\"M. Tesfaye, M. Nardello, D. Brunelli\",\"doi\":\"10.1109/EESMS.2017.8052678\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Demand and cost of electricity is expected to grow in the next years. This has raised interest in monitoring energy usage to reduce losses, and to provide real-time feedback about the cost of the electrical power consumed. This paper focuses on the implementation of a stand-alone system capable of real-time tracking of the power used and that provides power consumption estimation for each device from a single point of measurement. The learning activity is done by detecting the possible state of the electrical devices using a clustering algorithm, which involves k-means technique to analyze and detect the state of an appliance.\",\"PeriodicalId\":285890,\"journal\":{\"name\":\"2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EESMS.2017.8052678\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EESMS.2017.8052678","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Residential electrical consumption disaggregation on a single low-cost meter
Demand and cost of electricity is expected to grow in the next years. This has raised interest in monitoring energy usage to reduce losses, and to provide real-time feedback about the cost of the electrical power consumed. This paper focuses on the implementation of a stand-alone system capable of real-time tracking of the power used and that provides power consumption estimation for each device from a single point of measurement. The learning activity is done by detecting the possible state of the electrical devices using a clustering algorithm, which involves k-means technique to analyze and detect the state of an appliance.