An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

<p dir="ltr">Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Yassine Himeur (14147787) (author)
مؤلفون آخرون: Abdullah Alsalemi (6951986) (author), Faycal Bensaali (12427401) (author), Abbes Amira (6952001) (author)
منشور في: 2020
الموضوعات:
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الملخص:<p dir="ltr">Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time-domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two-dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D-PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D-PEP descriptor achieves outperformance for appliance identification in comparison with other recent techniques. Accordingly, high identification accuracies are attained on the GREEND, UK-DALE, and WHITED data sets, where 99.54%, 98.78%, and 100% rates have been achieved, respectively, using the proposed 2D-PEP descriptor.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Intelligent Systems<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1002/int.22292" target="_blank">http://dx.doi.org/10.1002/int.22292</a></p>