TY - GEN
T1 - A parallel multi-appliance recognition for smart meter
AU - Wang, Lien Chun
AU - Cho, Wei Ting
AU - Chiu, Yu Sheng
AU - Lai, Chin Feng
PY - 2013/1/1
Y1 - 2013/1/1
N2 - This study proposes a non-invasive smart meter system that considers the power use habits of users unfamiliar with electric appliances, and can be used by inserting the smart meter into an electrical circuit. This study also creates a database mechanism, appliance recognition classification, and a waveform recognition method, in order to solve the large data volume problem in current appliance recognition systems. In comparison to other appliance recognition systems, the low-end embedded system chip used in this study has low power consumption, as well as high expandability and ease of use. This experiment is different from the research environments of other appliance recognition systems by considering parallel multi-appliance recognition and general users' habit of using power. This study will not make any assumption of power utilization in the experiment. The total system recognition rate is 84.42%, and the total recognition rate of a single electric appliance is 93.82%, proving the high feasibility of this study.
AB - This study proposes a non-invasive smart meter system that considers the power use habits of users unfamiliar with electric appliances, and can be used by inserting the smart meter into an electrical circuit. This study also creates a database mechanism, appliance recognition classification, and a waveform recognition method, in order to solve the large data volume problem in current appliance recognition systems. In comparison to other appliance recognition systems, the low-end embedded system chip used in this study has low power consumption, as well as high expandability and ease of use. This experiment is different from the research environments of other appliance recognition systems by considering parallel multi-appliance recognition and general users' habit of using power. This study will not make any assumption of power utilization in the experiment. The total system recognition rate is 84.42%, and the total recognition rate of a single electric appliance is 93.82%, proving the high feasibility of this study.
UR - http://www.scopus.com/inward/record.url?scp=84904469712&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904469712&partnerID=8YFLogxK
U2 - 10.1109/DASC.2013.110
DO - 10.1109/DASC.2013.110
M3 - Conference contribution
AN - SCOPUS:84904469712
SN - 9781479933815
T3 - Proceedings - 2013 IEEE 11th International Conference on Dependable, Autonomic and Secure Computing, DASC 2013
SP - 475
EP - 480
BT - Proceedings - 2013 IEEE 11th International Conference on Dependable, Autonomic and Secure Computing, DASC 2013
PB - IEEE Computer Society
T2 - 11th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2013
Y2 - 21 December 2013 through 22 December 2013
ER -
