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Energy Disaggregation of Type I and II Loads by Means of Birch Clustering and Watchdog Timers

Author

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  • Amitay Kligman

    (School of Electrical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel)

  • Arbel Yaniv

    (School of Electrical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel)

  • Yuval Beck

    (School of Electrical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel)

Abstract

A non-intrusive load monitoring (NILM) process is intended to allow for the separation of individual appliances from an aggregated energy reading in order to estimate the operation of individual loads. In the past, electricity meters specified only active power readings, for billing purposes, thus limiting NILM capabilities. Recent progress in smart metering technology has introduced cost-effective, household-consumer-grade metering products, which can produce multiple features with high accuracy. In this paper, a new method is proposed for applying a BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm as part of a multi-dimensional load disaggregation solution based on the extraction of multiple features from a smart meter. The method uses low-frequency meter reading and constructs a multi-dimensional feature space with adaption to smart meter parameters and is useful for type I as well as type II loads with the addition of timers. This new method is described as energy disaggregation in NILM by means of multi-dimensional BIRCH clustering (DNB). It is simple, fast, uses raw meter sampling, and does not require preliminary training or powerful hardware. The algorithm is tested using a private dataset and a public dataset.

Suggested Citation

  • Amitay Kligman & Arbel Yaniv & Yuval Beck, 2023. "Energy Disaggregation of Type I and II Loads by Means of Birch Clustering and Watchdog Timers," Energies, MDPI, vol. 16(7), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3027-:d:1107738
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    References listed on IDEAS

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    1. Anand Nayyar & Vikram Puri, 2017. "Comprehensive Analysis & Performance Comparison of Clustering Algorithms for Big Data," Review of Computer Engineering Research, Conscientia Beam, vol. 4(2), pages 54-80.
    2. Hwan Kim & Sungsu Lim, 2021. "Temporal Patternization of Power Signatures for Appliance Classification in NILM," Energies, MDPI, vol. 14(10), pages 1-17, May.
    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.
    4. Qian Wu & Fei Wang, 2019. "Concatenate Convolutional Neural Networks for Non-Intrusive Load Monitoring across Complex Background," Energies, MDPI, vol. 12(8), pages 1-17, April.
    5. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
    6. Anand Nayyar & Vikram Puri, 2017. "Comprehensive Analysis & Performance Comparison of Clustering Algorithms for Big Data," Review of Computer Engineering Research, Conscientia Beam, vol. 4(2), pages 54-80.
    7. Jin-Gyeom Kim & Bowon Lee, 2019. "Appliance Classification by Power Signal Analysis Based on Multi-Feature Combination Multi-Layer LSTM," Energies, MDPI, vol. 12(14), pages 1-24, July.
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    Cited by:

    1. Men-Shen Tsai & Yen-Kuang Lin, 2023. "Applying the Geometric Features of Cumulative Sums to the Development of Event Detection," Energies, MDPI, vol. 16(20), pages 1-25, October.

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