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Open AccessArticle

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

by Ying Zhang 1, Bo Yin 1,2,*, Yanping Cong 1 and Zehua Du 1
1
College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China
2
Pilot National Laboratory for Marine Science and Technology, Qingdao 266000, China
*
Author to whom correspondence should be addressed.
Energies 2020, 13(4), 792; https://doi.org/10.3390/en13040792
Received: 4 November 2019 / Revised: 7 February 2020 / Accepted: 8 February 2020 / Published: 11 February 2020
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. View Full-Text
Keywords: non-intrusive load monitoring; the identification of appliances types; the identification of appliances states non-intrusive load monitoring; the identification of appliances types; the identification of appliances states
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Zhang, Y.; Yin, B.; Cong, Y.; Du, Z. Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering. Energies 2020, 13, 792.

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