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Event Matching Classification Method for Non-Intrusive Load Monitoring

Author

Listed:
  • Elnaz Azizi

    (Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran 14155-6343, Iran)

  • Mohammad T. H. Beheshti

    (Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran 14155-6343, Iran)

  • Sadegh Bolouki

    (Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran 14155-6343, Iran)

Abstract

Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.

Suggested Citation

  • Elnaz Azizi & Mohammad T. H. Beheshti & Sadegh Bolouki, 2021. "Event Matching Classification Method for Non-Intrusive Load Monitoring," Sustainability, MDPI, vol. 13(2), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:693-:d:479269
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    References listed on IDEAS

    as
    1. Zhao, Bochao & Ye, Minxiang & Stankovic, Lina & Stankovic, Vladimir, 2020. "Non-intrusive load disaggregation solutions for very low-rate smart meter data," Applied Energy, Elsevier, vol. 268(C).
    2. 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.
    3. Arash Moradzadeh & Omid Sadeghian & Kazem Pourhossein & Behnam Mohammadi-Ivatloo & Amjad Anvari-Moghaddam, 2020. "Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis," Sustainability, MDPI, vol. 12(8), pages 1-14, April.
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    Cited by:

    1. Muhammad Asif Ali Rehmani & Saad Aslam & Shafiqur Rahman Tito & Snjezana Soltic & Pieter Nieuwoudt & Neel Pandey & Mollah Daud Ahmed, 2021. "Power Profile and Thresholding Assisted Multi-Label NILM Classification," Energies, MDPI, vol. 14(22), pages 1-18, November.

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