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Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model

Author

Listed:
  • Ce Peng

    (Marketing Department of Guangdong Power Grid Company, Guangzhou 510000, China)

  • Guoying Lin

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310000, China)

  • Shaopeng Zhai

    (The Ministry of Education Key Laboratory of Control of Power Transmission and Conversion, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200200, China)

  • Yi Ding

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310000, China)

  • Guangyu He

    (The Ministry of Education Key Laboratory of Control of Power Transmission and Conversion, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200200, China)

Abstract

Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods.

Suggested Citation

  • Ce Peng & Guoying Lin & Shaopeng Zhai & Yi Ding & Guangyu He, 2020. "Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model," Energies, MDPI, vol. 13(21), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5629-:d:435847
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    References listed on IDEAS

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    1. Hosseini, Sayed Saeed & Agbossou, Kodjo & Kelouwani, Sousso & Cardenas, Alben, 2017. "Non-intrusive load monitoring through home energy management systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1266-1274.
    2. Cominola, A. & Giuliani, M. & Piga, D. & Castelletti, A. & Rizzoli, A.E., 2017. "A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring," Applied Energy, Elsevier, vol. 185(P1), pages 331-344.
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    Cited by:

    1. Sarra Houidi & Dominique Fourer & François Auger & Houda Ben Attia Sethom & Laurence Miègeville, 2021. "Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning," Energies, MDPI, vol. 14(9), pages 1-28, May.

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