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MC-NILM: A Multi-Chain Disaggregation Method for NILM

Author

Listed:
  • Hao Ma

    (School of Computer Science and Technology, Soochow University, Suzhou 215006, China)

  • Juncheng Jia

    (School of Computer Science and Technology, Soochow University, Suzhou 215006, China
    Jiangsu Province Software New Technology and Industrialization Collaborative Innovation Center, Nanjing 210023, China)

  • Xinhao Yang

    (School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215137, China)

  • Weipeng Zhu

    (School of Computer Science and Technology, Soochow University, Suzhou 215006, China)

  • Hong Zhang

    (School of Computer Science and Technology, Soochow University, Suzhou 215006, China)

Abstract

Non-intrusive load monitoring (NILM) is an approach that helps residents obtain detailed information about household electricity consumption and has gradually become a research focus in recent years. Most of the existing algorithms on NILM build energy disaggregation models independently for an individual appliance while neglecting the relation among them. For this situation, this article proposes a multi-chain disaggregation method for NILM (MC-NILM). MC-NILM integrates the models generated by existing algorithms and considers the relation among these models to improve the performance of energy disaggregation. Given the high time complexity of searching for the optimal MC-NILM structure, this article proposes two methods to reduce the time complexity, the k -length chain method and the graph-based chain generation method. Finally, we use the Dataport and UK-DALE datasets to evaluate the feasibility, effectiveness, and generality of the MC-NILM.

Suggested Citation

  • Hao Ma & Juncheng Jia & Xinhao Yang & Weipeng Zhu & Hong Zhang, 2021. "MC-NILM: A Multi-Chain Disaggregation Method for NILM," Energies, MDPI, vol. 14(14), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4331-:d:596615
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    References listed on IDEAS

    as
    1. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
    2. 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.
    3. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    4. 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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