TY - GEN
T1 - A smart appliance management system with current clustering algorithm in home network
AU - Chen, Shih Yeh
AU - Lu, Yu Sheng
AU - Lai, Chin Feng
PY - 2012/11/5
Y1 - 2012/11/5
N2 - Due to the variety of household electric devices and different power consumption habits of consumers at present, it is a challenge to identify various electric appliances without any presetting. This paper proposed the smart appliance management system for recognizing of electric appliances in home network, which can measure the power consumption of household appliances through a current sensing device. The characteristics and categories of related electric appliances are established, and this system could search the corresponding cluster data and eliminates noise for recognition functionality and error detection mechanism of electric appliances by applying the current clustering algorithm. At the same time, this system integrates household appliance control network services to control them based on users' power consumption plans, thus realizing a bidirectional monitoring service. In practical tests, the system reached a recognition rate of 95%, and could successfully control general household appliances in home network.
AB - Due to the variety of household electric devices and different power consumption habits of consumers at present, it is a challenge to identify various electric appliances without any presetting. This paper proposed the smart appliance management system for recognizing of electric appliances in home network, which can measure the power consumption of household appliances through a current sensing device. The characteristics and categories of related electric appliances are established, and this system could search the corresponding cluster data and eliminates noise for recognition functionality and error detection mechanism of electric appliances by applying the current clustering algorithm. At the same time, this system integrates household appliance control network services to control them based on users' power consumption plans, thus realizing a bidirectional monitoring service. In practical tests, the system reached a recognition rate of 95%, and could successfully control general household appliances in home network.
UR - http://www.scopus.com/inward/record.url?scp=84869594237&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84869594237&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33368-2_2
DO - 10.1007/978-3-642-33368-2_2
M3 - Conference contribution
AN - SCOPUS:84869594237
SN - 9783642333675
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
SP - 13
EP - 24
BT - Green Communications and Networking - First International Conference, GreeNets 2011, Revised Selected Papers
T2 - 1st ICST International Conference on Green Communications and Networking, GreeNets 2011
Y2 - 5 October 2011 through 7 October 2011
ER -