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Nonintrusive Load Monitoring Based on Complementary Features of Spurious Emissions

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
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Electronics 2019, 8(9), 1002; https://doi.org/10.3390/electronics8091002
Received: 10 August 2019 / Revised: 2 September 2019 / Accepted: 4 September 2019 / Published: 7 September 2019
(This article belongs to the Section Power Electronics)
In this paper, a novel method that utilizes the fractional correlation-based algorithm and the B-spline curve fitting-based algorithm is proposed to extract the complementary features for detecting the operating states of appliances. The identification of appliance operating states is one of the key parts for nonintrusive load monitoring (NILM). Considering the individual spurious emissions generated because of nonlinear components in each electronic device, the spurious emissions from the power cord can be picked up to solve the problem of data storage. Five types of common household appliances are considered in this study. The fractional correlation-based algorithm and B-spline curve fitting-based algorithm are used to extract two groups of complementary features from the spurious emissions of those five types of appliances. The experimental results show that the feature vectors extracted using the proposed method are obviously distinguishable. In addition, the features extracted show a good long-time stability, which is verified through a five-day experiment. Finally, based on support vector machine (SVM) and Dempster–Shafer (D-S) evidence theory, the identification accuracy reaches 85.5% using a combining classifier incorporated with the features extracted from the proposed methods. View Full-Text
Keywords: feature extraction; fractional correlation; B-spline curve fitting; combining classifier; support vector machine (SVM); Dempster–Shafer (D-S) evidence theory; nonintrusive load monitoring (NILM) feature extraction; fractional correlation; B-spline curve fitting; combining classifier; support vector machine (SVM); Dempster–Shafer (D-S) evidence theory; nonintrusive load monitoring (NILM)
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Su, D.; Shi, Q.; Xu, H.; Wang, W. Nonintrusive Load Monitoring Based on Complementary Features of Spurious Emissions. Electronics 2019, 8, 1002.

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