Building a Graph Signal Processing Model Using Dynamic Time Warping for Load Disaggregation
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
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Sensors 2020, 20(22), 6628; https://doi.org/10.3390/s20226628
Received: 21 October 2020
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Revised: 9 November 2020
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Accepted: 13 November 2020
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Published: 19 November 2020
(This article belongs to the Special Issue Graph Signal Processing for Sensing Applications)
Building on recent unsupervised Non-intrusive load monitoring (NILM) algorithms that use graph Laplacian regularization (GLR) and achieve state-of-the-art performance, in this paper, we propose a novel unsupervised approach to design an underlying graph to model the correlation within time-series smart meter measurements. We propose a variable-length data segmentation approach to extract potential events, assign all measurements associated with an identified event to each graph node, employ dynamic time warping to define the adjacency matrix of the graph, and propose a robust cluster labeling approach. Our simulation results on four different datasets show up to 10% improvement in classification performance over competing approaches.
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Keywords:
NILM; graph signal processing; energy efficiency; load disaggregation
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MDPI and ACS Style
He, K.; Stankovic, V.; Stankovic, L. Building a Graph Signal Processing Model Using Dynamic Time Warping for Load Disaggregation. Sensors 2020, 20, 6628. https://doi.org/10.3390/s20226628
AMA Style
He K, Stankovic V, Stankovic L. Building a Graph Signal Processing Model Using Dynamic Time Warping for Load Disaggregation. Sensors. 2020; 20(22):6628. https://doi.org/10.3390/s20226628
Chicago/Turabian StyleHe, Kanghang, Vladimir Stankovic, and Lina Stankovic. 2020. "Building a Graph Signal Processing Model Using Dynamic Time Warping for Load Disaggregation" Sensors 20, no. 22: 6628. https://doi.org/10.3390/s20226628
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