Detecting users' behaviors based on nonintrusive load monitoring technologies

Yung Chi Chen, Chun Mei Chu, Shiao-Li Tsao, Tzung Cheng Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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.

Original languageEnglish
Title of host publication2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013
Pages804-809
Number of pages6
DOIs
StatePublished - 14 Aug 2013
Event2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013 - Evry, France
Duration: 10 Apr 201312 Apr 2013

Publication series

Name2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013

Conference

Conference2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013
CountryFrance
CityEvry
Period10/04/1312/04/13

Keywords

  • data mining
  • energy management system
  • non-intrusive load monitoring
  • user behavior detection

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    Chen, Y. C., Chu, C. M., Tsao, S-L., & Tsai, T. C. (2013). Detecting users' behaviors based on nonintrusive load monitoring technologies. In 2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013 (pp. 804-809). [6548841] (2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013). https://doi.org/10.1109/ICNSC.2013.6548841