TY - JOUR
T1 - Household appliance recognition through a Bayes classification model
AU - Yan, Da
AU - Jin, Yuan
AU - Sun, Hongsan
AU - Dong, Bing
AU - Ye, Zi
AU - Li, Zhaoxuan
AU - Yuan, Yanping
N1 - Funding Information:
This study was supported by National Natural Science Foundation of China (grant number 51778321 ): Research on the quantitative description and simulation methodology of occupant behavior in buildings. It was supported in part by Innovative Research Groups of the National Natural Science Foundation of China (grant number 51521005 ).
PY - 2019/4
Y1 - 2019/4
N2 - With the acceleration of global warming and energy shortages, the smart grid has become the goal of power grid development, which makes intelligent household appliance control systems essential. To promote the integration of traditional household appliances into a new electrical system, this study focused on an appliance recognition algorithm applicable to traditional and typical household appliances. Using sequential appliance power consumption data from intelligent power sockets, this study generalized and extracted the characteristics of occupant behavior and power consumption of typical household appliances. A new recognition algorithm for household appliances, based on a Bayes classification model, is presented in this paper. Seven types of household appliances (refrigerator, electric cooker, air conditioner, television, laptop computer, washing machine, and water dispenser) were analyzed in 15 Beijing households. The proposed algorithm was proven to be applicable for appliance recognition.
AB - With the acceleration of global warming and energy shortages, the smart grid has become the goal of power grid development, which makes intelligent household appliance control systems essential. To promote the integration of traditional household appliances into a new electrical system, this study focused on an appliance recognition algorithm applicable to traditional and typical household appliances. Using sequential appliance power consumption data from intelligent power sockets, this study generalized and extracted the characteristics of occupant behavior and power consumption of typical household appliances. A new recognition algorithm for household appliances, based on a Bayes classification model, is presented in this paper. Seven types of household appliances (refrigerator, electric cooker, air conditioner, television, laptop computer, washing machine, and water dispenser) were analyzed in 15 Beijing households. The proposed algorithm was proven to be applicable for appliance recognition.
KW - Bayes classification
KW - Household appliances
KW - Occupant behavior
KW - Recognition algorithm
KW - Smart grid
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U2 - 10.1016/j.scs.2018.12.021
DO - 10.1016/j.scs.2018.12.021
M3 - Article
AN - SCOPUS:85060653378
SN - 2210-6707
VL - 46
JO - Sustainable Cities and Society
JF - Sustainable Cities and Society
M1 - 101393
ER -