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New Time-Frequency Transient Features for Nonintrusive Load Monitoring

Author

Listed:
  • Mahfoud Drouaz

    (IRIMAS, Université de Haute-Alsace, 61 rue Albert Camus, 68093 Mulhouse, France
    These authors contributed equally to this work.)

  • Bruno Colicchio

    (IRIMAS, Université de Haute-Alsace, 61 rue Albert Camus, 68093 Mulhouse, France
    These authors contributed equally to this work.)

  • Ali Moukadem

    (IRIMAS, Université de Haute-Alsace, 61 rue Albert Camus, 68093 Mulhouse, France
    These authors contributed equally to this work.)

  • Alain Dieterlen

    (IRIMAS, Université de Haute-Alsace, 61 rue Albert Camus, 68093 Mulhouse, France)

  • Djafar Ould-Abdeslam

    (IRIMAS, Université de Haute-Alsace, 61 rue Albert Camus, 68093 Mulhouse, France)

Abstract

A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively.

Suggested Citation

  • Mahfoud Drouaz & Bruno Colicchio & Ali Moukadem & Alain Dieterlen & Djafar Ould-Abdeslam, 2021. "New Time-Frequency Transient Features for Nonintrusive Load Monitoring," Energies, MDPI, vol. 14(5), pages 1-11, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1437-:d:511590
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    References listed on IDEAS

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    1. Yu, Biying & Tian, Yaming & Zhang, Junyi, 2015. "A dynamic active energy demand management system for evaluating the effect of policy scheme on household energy consumption behavior," Energy, Elsevier, vol. 91(C), pages 491-506.
    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. 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:

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