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A new graph learning-based signal processing approach for non-intrusive load disaggregation with active power measurements

  • ATCI 2019
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Abstract

Recently, there is a potential technology called graph-based signal processing (GSP) that is being used in many applications. GSP has been used successfully in the domains such as signal and image filtering and processing. In the paper, GSP is used as an applicable method to non-intrusive appliance load monitoring (NILM). In NILM, all of power consumption is disaggregated down to every appliance’s consumption without hardware. Although there is over 30 years after NILM was proposed, there are still some problems faced by applications of NILM in real scenario if there is no training data. By combination of NILM with GSP concept, such a challenge is tackled with better performance over existing methods. As the first step, we propose a new graph learning algorithm to get a graph suitable for appliance load representation and for the disaggregation algorithm. In the following steps, graph-based signal processing method is used three times, from representation of the data sets of power measurements. Public datasets are used to demonstrate the proposed method’s performance and feasibility.

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  1. School of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming, China

    Ming-Yue Zhai

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Zhai, MY. A new graph learning-based signal processing approach for non-intrusive load disaggregation with active power measurements. Neural Comput & Applic 32, 5495–5504 (2020). https://doi.org/10.1007/s00521-019-04623-w

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