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Detecting users' behaviors based on nonintrusive load monitoring technologies
Conventional user behavior detection relies on a large amount of sensors and expensive monitoring devices. Moreover, the systems are usually intrusive and may suffer from deployment problems. In this paper, we design and implement an energy management system (EMS) consisting of a non-intrusive load...
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creator | Yung-Chi Chen Chun-Mei Chu Shiao-Li Tsao Tzung-Cheng Tsai |
description | Conventional user behavior detection relies on a large amount of sensors and expensive monitoring devices. Moreover, the systems are usually intrusive and may suffer from deployment problems. In this paper, we design and implement an energy management system (EMS) consisting of a non-intrusive load monitoring (NILM) meter, gateway, server and mobile device. The NILM meter provides a non-intrusive and low-cost solution to recognize the states of appliances and to disaggregate the energy consumption of appliances in a house/building. Based on the proposed EMS, we further implement a data mining scheme to detect users' behaviors based on the usage patterns of appliances. A prototype system verifies our design concept and the simulation results show that the detection accuracy of users' behaviors is more than 80% for most of the activities. |
doi_str_mv | 10.1109/ICNSC.2013.6548841 |
format | conference_proceeding |
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A prototype system verifies our design concept and the simulation results show that the detection accuracy of users' behaviors is more than 80% for most of the activities.</description><subject>Actuators</subject><subject>data mining</subject><subject>energy management system</subject><subject>Home appliances</subject><subject>Logic gates</subject><subject>Mobile communication</subject><subject>non-intrusive load monitoring</subject><subject>Sensor phenomena and characterization</subject><subject>Servers</subject><subject>user behavior detection</subject><isbn>9781467351980</isbn><isbn>1467351989</isbn><isbn>1467351997</isbn><isbn>9781467351997</isbn><isbn>9781467352000</isbn><isbn>1467352004</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1kMtOwzAURI0QElDyA7DxjlWKrx-xvUThVanAAlhXcXLTGqU2stNK_D1BlNVoRjqj0RByCWwOwOzNon55q-ecgZhXShoj4Yicg6y0UGCtPiaF1ebfG3ZKipw_GWMTXAkrz8jzHY7Yjj6s6S5jytfU4abZ-5gydU3GjsZAQww-jGmX_R7pEJuObqdkjOkXm_BNiENce8wX5KRvhozFQWfk4-H-vX4ql6-Pi_p2Wbacy7G0pmmddaznvTTAuGu1ZhKdUE4Jg7xzsmq1AulQVsz1UME0FrgSALo1UszI1V-vR8TVV_LbJn2vDg-IHxaBT4g</recordid><startdate>201304</startdate><enddate>201304</enddate><creator>Yung-Chi Chen</creator><creator>Chun-Mei Chu</creator><creator>Shiao-Li Tsao</creator><creator>Tzung-Cheng Tsai</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201304</creationdate><title>Detecting users' behaviors based on nonintrusive load monitoring technologies</title><author>Yung-Chi Chen ; Chun-Mei Chu ; Shiao-Li Tsao ; Tzung-Cheng Tsai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c224t-98acb9b0f2f48102bc7704eb35b538e2db46c7514be460bf1613941253117c843</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Actuators</topic><topic>data mining</topic><topic>energy management system</topic><topic>Home appliances</topic><topic>Logic gates</topic><topic>Mobile communication</topic><topic>non-intrusive load monitoring</topic><topic>Sensor phenomena and characterization</topic><topic>Servers</topic><topic>user behavior detection</topic><toplevel>online_resources</toplevel><creatorcontrib>Yung-Chi Chen</creatorcontrib><creatorcontrib>Chun-Mei Chu</creatorcontrib><creatorcontrib>Shiao-Li Tsao</creatorcontrib><creatorcontrib>Tzung-Cheng Tsai</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Explore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yung-Chi Chen</au><au>Chun-Mei Chu</au><au>Shiao-Li Tsao</au><au>Tzung-Cheng Tsai</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Detecting users' behaviors based on nonintrusive load monitoring technologies</atitle><btitle>2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC)</btitle><stitle>ICNSC</stitle><date>2013-04</date><risdate>2013</risdate><spage>804</spage><epage>809</epage><pages>804-809</pages><isbn>9781467351980</isbn><isbn>1467351989</isbn><eisbn>1467351997</eisbn><eisbn>9781467351997</eisbn><eisbn>9781467352000</eisbn><eisbn>1467352004</eisbn><abstract>Conventional user behavior detection relies on a large amount of sensors and expensive monitoring devices. Moreover, the systems are usually intrusive and may suffer from deployment problems. In this paper, we design and implement an energy management system (EMS) consisting of a non-intrusive load monitoring (NILM) meter, gateway, server and mobile device. The NILM meter provides a non-intrusive and low-cost solution to recognize the states of appliances and to disaggregate the energy consumption of appliances in a house/building. Based on the proposed EMS, we further implement a data mining scheme to detect users' behaviors based on the usage patterns of appliances. A prototype system verifies our design concept and the simulation results show that the detection accuracy of users' behaviors is more than 80% for most of the activities.</abstract><pub>IEEE</pub><doi>10.1109/ICNSC.2013.6548841</doi><tpages>6</tpages></addata></record> |
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identifier | ISBN: 9781467351980 |
ispartof | 2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC), 2013, p.804-809 |
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language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Actuators data mining energy management system Home appliances Logic gates Mobile communication non-intrusive load monitoring Sensor phenomena and characterization Servers user behavior detection |
title | Detecting users' behaviors based on nonintrusive load monitoring technologies |
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