Volume 31 Issue 4
Jul.  2022
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CHEN Junfeng and WANG Xue, “Non-intrusive Load Monitoring Using Gramian Angular Field Color Encoding in Edge Computing,” Chinese Journal of Electronics, vol. 31, no. 4, pp. 595-603, 2022, doi: 10.1049/cje.2020.00.268
Citation: CHEN Junfeng and WANG Xue, “Non-intrusive Load Monitoring Using Gramian Angular Field Color Encoding in Edge Computing,” Chinese Journal of Electronics, vol. 31, no. 4, pp. 595-603, 2022, doi: 10.1049/cje.2020.00.268

Non-intrusive Load Monitoring Using Gramian Angular Field Color Encoding in Edge Computing

doi: 10.1049/cje.2020.00.268
Funds:  This work was supported by National Key R&D Program of China (2018YFB2003500, 2018YFB2003201)
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  • Author Bio:

    received the B.S. degree in instrumentation science and technology from Tsinghua University in 2017. He is a Ph.D. candidate in Department of Precision Instrument, Tsinghua University. His current research interests include smart grid and edge computing.(Email: chenjf17@mails.tsinghua.edu.cn)

    (corresponding author) received the Ph.D. degree from Huazhong University of Science and Technology in 1994. He is now a Professor and Supervisor for Ph.D. student in Department of Precision Instrument, Tsinghua University. His main research interests includes precision measurement and sensor technology and wireless sensor network measurement. (Email: wangxue@mail.tsinghua.edu.cn)

  • Received Date: 2020-08-28
  • Accepted Date: 2022-01-17
  • Available Online: 2022-03-07
  • Publish Date: 2022-07-05
  • Non-intrusive load monitoring (NILM) can infer the status of the appliances in operation and their energy consumption by analyzing the energy data collected from monitoring devices. With the rapid increase of electric loads in amount and type, the traditional way of uploading all energy data to cloud is facing enormous challenges. It becomes increasingly significant to construct distinguishable load signatures and build robust classification models for NILM. In this paper, we propose a load signature construction method for load recognition task in home scenarios. The load signature is based on the Gramian angular field encoding theory, which is convenient to construct and significantly reduces the data transmission volume of the network. Moreover, edge computing architecture can reasonably utilize computing resources and relieve the pressure of the server. The experimental results on NILM datasets demonstrate that the proposed method obtains superior performance in the recognition of household appliances under insufficient resources.
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