Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering

Ying Zhang, Bo Yin, Yanping Cong and Zehua Du
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Ying Zhang: College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China
Bo Yin: College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China
Yanping Cong: College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China
Zehua Du: College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China

Energies, 2020, vol. 13, issue 4, 1-12

Abstract: Non-intrusive load monitoring, a convenient way to discern the energy consumption of a house, has been studied extensively. However, most research works have been carried out based on a hypothetical condition that each electric appliance has only one running state. This leads to low identification accuracy for multi-state electric appliances. To deal with this problem, a method for identifying the type and state of electric appliances based on a power time series is proposed in this paper. First, to identify the type of appliance, a convolutional neural network model was constructed that incorporated residual modules. Then, a k-means clustering algorithm was applied to calculate the number of states of the appliance. Finally, in order to identify the states of the appliances, different k-means clustering models were established for different multi-state electric appliances. Experimental results show effectiveness of the proposed method in identifying both the type and the running state of electric appliances.

Keywords: non-intrusive load monitoring; the identification of appliances types; the identification of appliances states (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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