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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1443700

Non-intrusive Residential Load Identification Based on Load Feature Matrix and CBAM-BiLSTM algorithm

Provisionally accepted
Shunfu Lin Shunfu Lin Bing Zhao Bing Zhao *Yinfeng Zhan Yinfeng Zhan Xiaoyan Bian Xiaoyan Bian Dongdong Li Dongdong Li
  • School of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China

The final, formatted version of the article will be published soon.

    With the increasing demand for the refined management of residential loads, the study of the noninvasive load monitoring (NILM) technologies has attracted much attention in recent years. This paper proposes a novel method of residential load identification based on load feature matrix and improved neural networks. Firstly, it constructs a unified scale bitmap format gray image consisted of multiple load feature matrix including: V-I characteristic curve, 1-16 harmonic currents, 1-cycle steady-state current waveform, maximum and minimum current values, active and reactive power. Secondly, it adopts a convolutional layer to extract image features and performs further feature extraction through a convolutional block attention module (CBAM). Thirdly, the feature matrix is converted and input to a bidirectional long short-term memory (BiLSTM) for training and identification. Furthermore, the identification results are optimized with dynamic time warping (DTW). The effectiveness of the proposed method is verified by the commonly used PLAID database.

    Keywords: Non-intrusive Load Monitoring, load feature, Convolutional block attention module, Bi-directional long short-term memory, Dynamic Time Warping

    Received: 04 Jun 2024; Accepted: 31 Jul 2024.

    Copyright: © 2024 Lin, Zhao, Zhan, Bian and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Bing Zhao, School of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.