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Time-Lapse Image Method for Classifying Appliances in Nonintrusive Load Monitoring

Author

Listed:
  • Joonho Seon

    (Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea)

  • Youngghyu Sun

    (Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea)

  • Soohyun Kim

    (Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea)

  • Jinyoung Kim

    (Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea)

Abstract

In this paper, a time-lapse image method is proposed to improve the classification accuracy for multistate appliances with complex patterns based on nonintrusive load monitoring (NILM). A log-likelihood ratio detector with a maxima algorithm was applied to construct a real-time event detection of home appliances. Moreover, a novel image-combining method was employed to extract information from the data based on the Gramian angular field (GAF) and recurrence plot (RP) transformations. From the simulation results, it was confirmed that the classification accuracy can be increased by up to 30% with the proposed method compared with the conventional approaches in classifying multistate appliances.

Suggested Citation

  • Joonho Seon & Youngghyu Sun & Soohyun Kim & Jinyoung Kim, 2021. "Time-Lapse Image Method for Classifying Appliances in Nonintrusive Load Monitoring," Energies, MDPI, vol. 14(22), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7630-:d:679568
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    2. Veronica Piccialli & Antonio M. Sudoso, 2021. "Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network," Energies, MDPI, vol. 14(4), pages 1-16, February.
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