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Non-Intrusive Load Identification Method Based on KPCA-IGWO-RF

Author

Listed:
  • Sheng Hu

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

  • Gongjin Yuan

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

  • Kaifeng Hu

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

  • Cong Liu

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

  • Minghu Wu

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

Abstract

Non-invasive load monitoring (NILM) represents a crucial technology in enabling smart electricity consumption. In response to the challenges posed by high feature redundancy, low identification accuracy, and the high computational costs associated with current load identification models, a novel load identification model based on kernel principal component analysis (KPCA) and random forest (RF) optimized by improved Grey Wolf Optimizer (IGWO) is proposed. Initially, 17 steady-state load characteristics were selected as discrimination indexes. KPCA was subsequently employed to reduce the dimension of the original data and diminish the correlation between the feature indicators. Then, the dimension reduction in load data was classified by RF. In order to improve the performance of the classifier, IGWO was used to optimize the parameters of the RF classifier. Finally, the proposed model was implemented to identify 25 load states consisting of seven devices. The experimental results demonstrate that the identification accuracy of this method is up to 96.8% and the Kappa coefficient is 0.9667.

Suggested Citation

  • Sheng Hu & Gongjin Yuan & Kaifeng Hu & Cong Liu & Minghu Wu, 2023. "Non-Intrusive Load Identification Method Based on KPCA-IGWO-RF," Energies, MDPI, vol. 16(12), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4805-:d:1174661
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    References listed on IDEAS

    as
    1. Tekler, Zeynep Duygu & Low, Raymond & Zhou, Yuren & Yuen, Chau & Blessing, Lucienne & Spanos, Costas, 2020. "Near-real-time plug load identification using low-frequency power data in office spaces: Experiments and applications," Applied Energy, Elsevier, vol. 275(C).
    2. Zhuang, Dian & Gan, Vincent J.L. & Duygu Tekler, Zeynep & Chong, Adrian & Tian, Shuai & Shi, Xing, 2023. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning," Applied Energy, Elsevier, vol. 338(C).
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