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

Author

Listed:
  • 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
    Pilot National Laboratory for Marine Science and Technology, 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)

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.

Suggested Citation

  • Ying Zhang & Bo Yin & Yanping Cong & Zehua Du, 2020. "Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering," Energies, MDPI, vol. 13(4), pages 1-12, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:792-:d:319384
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    References listed on IDEAS

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    1. Dinesh, Chinthaka & Welikala, Shirantha & Liyanage, Yasitha & Ekanayake, Mervyn Parakrama B. & Godaliyadda, Roshan Indika & Ekanayake, Janaka, 2017. "Non-intrusive load monitoring under residential solar power influx," Applied Energy, Elsevier, vol. 205(C), pages 1068-1080.
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    3. Salvina Gagliano & Fabiana Cairone & Angelo Amenta & Maide Bucolo, 2019. "A Real Time Feed Forward Control of Slug Flow in Microchannels †," Energies, MDPI, vol. 12(13), pages 1-11, July.
    4. Marco Fagiani & Roberto Bonfigli & Emanuele Principi & Stefano Squartini & Luigi Mandolini, 2019. "A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks," Energies, MDPI, vol. 12(7), pages 1-26, April.
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    Cited by:

    1. Hasan Rafiq & Xiaohan Shi & Hengxu Zhang & Huimin Li & Manesh Kumar Ochani, 2020. "A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing," Energies, MDPI, vol. 13(9), pages 1-26, May.
    2. Patrick Huber & Alberto Calatroni & Andreas Rumsch & Andrew Paice, 2021. "Review on Deep Neural Networks Applied to Low-Frequency NILM," Energies, MDPI, vol. 14(9), pages 1-34, April.

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