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A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data

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  • Quanbo Yuan

    (School of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Langfang 065000, China
    The School of Computer Software, Tianjin University, Tianjin 300354, China)

  • Huijuan Wang

    (School of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Langfang 065000, China
    State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China)

  • Botao Wu

    (Department of Automotive Engineering, Hebei Institute of Machinery and Electricity, Xintai 054000, China)

  • Yaodong Song

    (School of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Langfang 065000, China)

  • Hejia Wang

    (The School of Computer Software, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

In order to achieve more efficient energy consumption, it is crucial that accurate detailed information is given on how power is consumed. Electricity details benefit both market utilities and also power consumers. Non-intrusive load monitoring (NILM), a novel and economic technology, obtains single-appliance power consumption through a single total power meter. This paper, focusing on load disaggregation with low hardware costs, proposed a load disaggregation method for low sampling data from smart meters based on a clustering algorithm and support vector regression optimization. This approach combines the k-median algorithm and dynamic time warping to identify the operating appliance and retrieves single energy consumption from an aggregate smart meter signal via optimized support vector regression (OSVR). Experiments showed that the technique can recognize multiple devices switching on at the same time using low-frequency data and achieve a high load disaggregation performance. The proposed method employs low sampling data acquired by smart meters without installing extra measurement equipment, which lowers hardware cost and is suitable for applications in smart grid environments.

Suggested Citation

  • Quanbo Yuan & Huijuan Wang & Botao Wu & Yaodong Song & Hejia Wang, 2019. "A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data," Future Internet, MDPI, vol. 11(2), pages 1-13, February.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:2:p:51-:d:207067
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    References listed on IDEAS

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    1. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
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    Cited by:

    1. Brudermueller, Tobias & Kreft, Markus & Fleisch, Elgar & Staake, Thorsten, 2023. "Large-scale monitoring of residential heat pump cycling using smart meter data," Applied Energy, Elsevier, vol. 350(C).

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