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Proceedings Paper

A time-frequency approach for event detection in non-intrusive load monitoring
Author(s): Yuanwei Jin; Eniye Tebekaemi; Mario Berges; Lucio Soibelman
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Paper Abstract

Non-intrusive load monitoring is an emerging signal processing and analysis technology that aims to identify individual appliance in residential or commercial buildings or to diagnose shipboard electro-mechanical systems through continuous monitoring of the change of On and Off status of various loads. In this paper, we develop a joint time-frequency approach for appliance event detection based on the time varying power signals obtained from the measured aggregated current and voltage waveforms. The short-time Fourier transform is performed to obtain the spectral components of the non-stationary aggregated power signals of appliances. The proposed event detector utilizes a goodness-of-fit Chi-squared test for detecting load activities using the calculated average power followed by a change point detector for estimating the change point of the transient signals using the first harmonic component of the power signals. Unlike the conventional detectors such as the generalized likelihood ratio test, the proposed event detector allows a closed form calculation of the decision threshold and provides a guideline for choosing the size of the detection data window, thus eliminating the need for extensive training for determining the detection threshold while providing robust detection performance against dynamic load activities. Using the real-world power data collected in two residential building testbeds, we demonstrate the superior performance of the proposed algorithm compared to the conventional generalized likelihood ratio detector.

Paper Details

Date Published: 5 May 2011
PDF: 13 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80501U (5 May 2011); doi: 10.1117/12.884385
Show Author Affiliations
Yuanwei Jin, Univ. of Maryland Eastern Shore (United States)
Eniye Tebekaemi, Univ. of Maryland Eastern Shore (United States)
Mario Berges, Carnegie Mellon Univ. (United States)
Lucio Soibelman, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)

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