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A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images

Author

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  • Petros Papageorgiou

    (Department of Electrical & Computer Engineering, University of Western Macedonia, ZEP Campus, 50150 Kozani, Greece)

  • Dimitra Mylona

    (Department of Electrical & Computer Engineering, University of Western Macedonia, ZEP Campus, 50150 Kozani, Greece)

  • Konstantinos Stergiou

    (Department of Electrical & Computer Engineering, University of Western Macedonia, ZEP Campus, 50150 Kozani, Greece)

  • Aggelos S. Bouhouras

    (Department of Electrical & Computer Engineering, University of Western Macedonia, ZEP Campus, 50150 Kozani, Greece)

Abstract

Non-intrusive load monitoring (NILM) has been on the rise for more than three decades. Its main objective is non-intrusive load disaggregation into individual operating appliances. Recent studies have shown that a higher sampling rate in the aggregated measurements allows better performance regarding load disaggregation. In addition, recent developments in deep learning and, in particular, convolutional neural networks (CNNs) have facilitated load disaggregation using CNN models. Several methods have been described in the literature that combine both a higher sampling rate and a CNN-based NILM framework. However, these methods use only a small number of cycles of the aggregated signal, which complicates the practical application of real-time NILM. In this work, a high sampling rate time-driven CNN-based NILM framework is also proposed. However, a novel current harmonic distortion image extracted from 60 cycles of the aggregated signal is proposed, resulting in 1 s appliance classification with low computational complexity. Appliance classification performance is evaluated using the PLAID3 dataset for both single and combined appliance operation. In addition, a comparison is made with a method from the literature. The results highlight the robustness of the novel feature and confirm the real-time applicability of the proposed NILM framework.

Suggested Citation

  • Petros Papageorgiou & Dimitra Mylona & Konstantinos Stergiou & Aggelos S. Bouhouras, 2023. "A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images," Sustainability, MDPI, vol. 15(17), pages 1-14, August.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12957-:d:1227001
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    References listed on IDEAS

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    2. Christos Athanasiadis & Dimitrios Doukas & Theofilos Papadopoulos & Antonios Chrysopoulos, 2021. "A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption," Energies, MDPI, vol. 14(3), pages 1-23, February.
    3. José Antonio Hoyo-Montaño & Guillermo Valencia-Palomo & Rafael Armando Galaz-Bustamante & Abel García-Barrientos & Daniel Fernando Espejel-Blanco, 2019. "Environmental Impacts of Energy Saving Actions in an Academic Building," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
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    5. Miltiadis D. Lytras & Kwok Tai Chui, 2019. "The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications," Energies, MDPI, vol. 12(16), pages 1-7, August.
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    Cited by:

    1. Jiachuan Shi & Dingrui Zhi & Rao Fu, 2023. "Research on a Non-Intrusive Load Recognition Algorithm Based on High-Frequency Signal Decomposition with Improved VI Trajectory and Background Color Coding," Mathematics, MDPI, vol. 12(1), pages 1-20, December.

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