TY - GEN
T1 - Detecting users' behaviors based on nonintrusive load monitoring technologies
AU - Chen, Yung Chi
AU - Chu, Chun Mei
AU - Tsao, Shiao-Li
AU - Tsai, Tzung Cheng
PY - 2013/8/14
Y1 - 2013/8/14
N2 - 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.
AB - 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.
KW - data mining
KW - energy management system
KW - non-intrusive load monitoring
KW - user behavior detection
UR - http://www.scopus.com/inward/record.url?scp=84881291701&partnerID=8YFLogxK
U2 - 10.1109/ICNSC.2013.6548841
DO - 10.1109/ICNSC.2013.6548841
M3 - Conference contribution
AN - SCOPUS:84881291701
SN - 9781467351980
T3 - 2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013
SP - 804
EP - 809
BT - 2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013
T2 - 2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013
Y2 - 10 April 2013 through 12 April 2013
ER -
