Volume 9, Number 8, June 2019

Appliance Recognition Using A Density- Based Clustering Approach with Multiple Granularities

  Authors

Chun-Wei Yen, Yu-Lin Ke,Sheng-Ta Chen, Yi-Chieh Pai, Hung-Chieh Wei, and Wei-Guang Teng, National Cheng Kung University, Taiwan

  Abstract

Electricity may not be economically stored as other forms of energy such that it would be in short supply during the peak time. In view of this difficulty, most power suppliers encourage their customers to adopt time-of-use rate plans. Consequently, it is essential for a user to be able to perceive the real-time information of power consumption. With the advancement of Internet of Things technologies, smart sockets are becoming a commodity to manage power consumption in a household. However, current smart sockets merely present the total electricity consumption rather than the individual consumption of household appliances. In this work, we thus design the capability of appliance recognition and implement this feature into a smart socket so that we can identify the power consumption of each appliance respectively. The proposed recursive DBSCAN approach realizes the recognition task without prior knowledge of new appliances and shows a feasible result in our experimental studies.

  Keywords

Appliance Recognition, Data Clustering, Smart Socket

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