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A Novel Denoising Auto-Encoder-Based Approach for Non-Intrusive Residential Load Monitoring

Author

Listed:
  • Xi He

    (Department of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang 421002, China)

  • Heng Dong

    (Department of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang 421002, China)

  • Wanli Yang

    (Department of Electrical and Information Engineering, Hunan University, Changsha 410006, China)

  • Jun Hong

    (Department of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang 421002, China)

Abstract

Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising Auto-Encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation Dataset (REDD) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance.

Suggested Citation

  • Xi He & Heng Dong & Wanli Yang & Jun Hong, 2022. "A Novel Denoising Auto-Encoder-Based Approach for Non-Intrusive Residential Load Monitoring," Energies, MDPI, vol. 15(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2290-:d:776027
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    References listed on IDEAS

    as
    1. Antonio Ruano & Alvaro Hernandez & Jesus Ureña & Maria Ruano & Juan Garcia, 2019. "NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review," Energies, MDPI, vol. 12(11), pages 1-29, June.
    2. Tu, Chunming & He, Xi & Shuai, Zhikang & Jiang, Fei, 2017. "Big data issues in smart grid – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1099-1107.
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    Cited by:

    1. Leijiao Ge & Jun Yan & Yonghui Sun & Zhongguan Wang, 2022. "Situational Awareness for Smart Distribution Systems," Energies, MDPI, vol. 15(11), pages 1-3, June.

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