Energies 2012, 5(11), 4569-4589; doi:10.3390/en5114569
Article

Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses

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Received: 19 October 2012; in revised form: 7 November 2012 / Accepted: 7 November 2012 / Published: 14 November 2012
(This article belongs to the Special Issue Smart Grid and the Future Electrical Network)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Energy management systems strive to use energy resources efficiently, save energy, and reduce carbon output. This study proposes transient feature analyses of the transient response time and transient energy on the power signatures of non-intrusive demand monitoring and load identification to detect the power demand and load operation. This study uses the wavelet transform (WT) of the time-frequency domain to analyze and detect the transient physical behavior of loads during the load identification. The experimental results show the transient response time and transient energy are better than the steady-state features to improve the recognition accuracy and reduces computation requirements in non-intrusive load monitoring (NILM) systems. The discrete wavelet transform (DWT) is more suitable than short-time Fourier transform (STFT) for transient load analyses.
Keywords: non-intrusive load monitoring; feature analysis; wavelet transform; short-time Fourier transform; energy management systems
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MDPI and ACS Style

Chang, H.-H. Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses. Energies 2012, 5, 4569-4589.

AMA Style

Chang H-H. Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses. Energies. 2012; 5(11):4569-4589.

Chicago/Turabian Style

Chang, Hsueh-Hsien. 2012. "Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses." Energies 5, no. 11: 4569-4589.


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