Deokwoo Jung, V. Krishna, Ngo Quang Minh Khiem, H. Nguyen, David K. Y. Yau
{"title":"EnergyTrack:传感器驱动的能源使用分析系统","authors":"Deokwoo Jung, V. Krishna, Ngo Quang Minh Khiem, H. Nguyen, David K. Y. Yau","doi":"10.1145/2528282.2528289","DOIUrl":null,"url":null,"abstract":"Demand side management (DSM) has emerged as a promising way to balance the electrical grid's demand and supply in an economical and environmentally friendly manner. For successful DSM, it is crucial to automate the analysis of building energy usage with respect to important factors that drive it, such as occupancy. In this paper, we present a sensor-driven energy use analysis system, EnergyTrack, that continuously analyzes, evaluates, and interprets building energy use in real-time. We develop an energy usage model in EnergyTrack that simultaneously considers device-specific energy consumption, occupancy changes, and occupant utility. We also design a low-cost occupancy estimation algorithm with a lightweight training requirement. The EnergyTrack testbed is implemented in a commercial building office space. Through this testbed, we demonstrate the performance of our occupancy estimation algorithm and the application of EnergyTrack in energy use analysis.","PeriodicalId":184274,"journal":{"name":"Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings","volume":"284 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"EnergyTrack: Sensor-Driven Energy Use Analysis System\",\"authors\":\"Deokwoo Jung, V. Krishna, Ngo Quang Minh Khiem, H. Nguyen, David K. Y. Yau\",\"doi\":\"10.1145/2528282.2528289\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Demand side management (DSM) has emerged as a promising way to balance the electrical grid's demand and supply in an economical and environmentally friendly manner. For successful DSM, it is crucial to automate the analysis of building energy usage with respect to important factors that drive it, such as occupancy. In this paper, we present a sensor-driven energy use analysis system, EnergyTrack, that continuously analyzes, evaluates, and interprets building energy use in real-time. We develop an energy usage model in EnergyTrack that simultaneously considers device-specific energy consumption, occupancy changes, and occupant utility. We also design a low-cost occupancy estimation algorithm with a lightweight training requirement. The EnergyTrack testbed is implemented in a commercial building office space. Through this testbed, we demonstrate the performance of our occupancy estimation algorithm and the application of EnergyTrack in energy use analysis.\",\"PeriodicalId\":184274,\"journal\":{\"name\":\"Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings\",\"volume\":\"284 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2528282.2528289\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2528282.2528289","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
EnergyTrack: Sensor-Driven Energy Use Analysis System
Demand side management (DSM) has emerged as a promising way to balance the electrical grid's demand and supply in an economical and environmentally friendly manner. For successful DSM, it is crucial to automate the analysis of building energy usage with respect to important factors that drive it, such as occupancy. In this paper, we present a sensor-driven energy use analysis system, EnergyTrack, that continuously analyzes, evaluates, and interprets building energy use in real-time. We develop an energy usage model in EnergyTrack that simultaneously considers device-specific energy consumption, occupancy changes, and occupant utility. We also design a low-cost occupancy estimation algorithm with a lightweight training requirement. The EnergyTrack testbed is implemented in a commercial building office space. Through this testbed, we demonstrate the performance of our occupancy estimation algorithm and the application of EnergyTrack in energy use analysis.