Household appliance recognition through a Bayes classification model. (April 2019)

• Record Type: Journal Article
• Authors: Yan, Da; Jin, Yuan; Sun, Hongsan; Dong, Bing; Ye, Zi; Li, Zhaoxuan; Yuan, Yanping
• Is Part Of: Sustainable cities and society. Volume 46(2019)
• Publication Date: 2019-04
• Subjects: Household appliances -- Recognition algorithm -- Bayes classification -- Occupant behavior -- Smart grid; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2018.12.021
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: This study focused on household appliance recognition through Bayes classification algorithm. The feature set of the appliance recognition contained load curve feature and occupant behavior. Seven types of household appliances were analyzed and recognized in the case study. The recognition algorithm was verified through cross-validation. Abstract: 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.