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Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

Author

Listed:
  • Pascal A. Schirmer

    (Communications and Intelligent Systems Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Iosif Mporas

    (Communications and Intelligent Systems Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Akbar Sheikh-Akbari

    (School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS1 3HE, UK)

Abstract

A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.

Suggested Citation

  • Pascal A. Schirmer & Iosif Mporas & Akbar Sheikh-Akbari, 2020. "Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors," Energies, MDPI, vol. 13(9), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2148-:d:352739
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    References listed on IDEAS

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    1. İsmail Hakkı ÇAVDAR & Vahid FARYAD, 2019. "New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid," Energies, MDPI, vol. 12(7), pages 1-18, March.
    2. Buchanan, Kathryn & Banks, Nick & Preston, Ian & Russo, Riccardo, 2016. "The British public’s perception of the UK smart metering initiative: Threats and opportunities," Energy Policy, Elsevier, vol. 91(C), pages 87-97.
    3. Pascal A. Schirmer & Iosif Mporas, 2019. "Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    4. 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.
    5. Lee, Dasheng & Cheng, Chin-Chi, 2016. "Energy savings by energy management systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 760-777.
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    Cited by:

    1. Wei Wang & Zilin Wang & Yanru Chen & Min Guo & Zhengyu Chen & Yi Niu & Huangeng Liu & Liangyin Chen, 2021. "Bats: An Appliance Safety Hazards Factors Detection Algorithm with an Improved Nonintrusive Load Disaggregation Method," Energies, MDPI, vol. 14(12), pages 1-18, June.
    2. Pascal A. Schirmer & Iosif Mporas & Akbar Sheikh-Akbari, 2021. "Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation," Energies, MDPI, vol. 14(9), pages 1-16, April.

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