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A Novel Nonintrusive Load Monitoring Approach based on Linear-Chain Conditional Random Fields

Author

Listed:
  • Hui He

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
    These authors contributed equally to this work.)

  • Zixuan Liu

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
    These authors contributed equally to this work.)

  • Runhai Jiao

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Guangwei Yan

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

In a real interactive service system, a smart meter can only read the total amount of energy consumption rather than analyze the internal load components for users. Nonintrusive load monitoring (NILM), as a vital part of smart power utilization techniques, can provide load disaggregation information, which can be further used for optimal energy use. In our paper, we introduce a new method called linear-chain conditional random fields (CRFs) for NILM and combine two promising features: current signals and real power measurements. The proposed method relaxes the independent assumption and avoids the label bias problem. Case studies on two open datasets showed that the proposed method can efficiently identify multistate appliances and detect appliances that are not easily identified by other models.

Suggested Citation

  • Hui He & Zixuan Liu & Runhai Jiao & Guangwei Yan, 2019. "A Novel Nonintrusive Load Monitoring Approach based on Linear-Chain Conditional Random Fields," Energies, MDPI, vol. 12(9), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1797-:d:230344
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    References listed on IDEAS

    as
    1. Kofi Afrifa Agyeman & Sekyung Han & Soohee Han, 2015. "Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System," Energies, MDPI, vol. 8(9), pages 1-20, August.
    2. Kwok Tai Chui & Miltiadis D. Lytras & Anna Visvizi, 2018. "Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption," Energies, MDPI, vol. 11(11), pages 1-20, October.
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    Cited by:

    1. Wesley Angelino de Souza & Fernando Deluno Garcia & Fernando Pinhabel Marafão & Luiz Carlos Pereira da Silva & Marcelo Godoy Simões, 2019. "Load Disaggregation Using Microscopic Power Features and Pattern Recognition," Energies, MDPI, vol. 12(14), pages 1-18, July.
    2. 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.
    3. 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.

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