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المؤلفHimeur, Yassine
المؤلفAlsalemi, Abdullah
المؤلفBensaali, Faycal
المؤلفAmira, Abbes
تاريخ الإتاحة2022-12-29T07:34:41Z
تاريخ النشر2021
اسم المنشورSustainable Cities and Society
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1016/j.scs.2021.102764
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37796
الملخصNon-intrusive load monitoring (NILM) is a key cost-effective technology for monitoring power consumption and contributing to several challenges encountered when transiting to an efficient, sustainable, and competitive energy efficiency environment. This paper proposes a smart NILM system based on a novel local power histogramming (LPH) descriptor, in which appliance power signals are transformed into 2D space and short histograms are extracted to represent each device. Specifically, short local histograms are drawn to represent individual appliance consumption signatures and robustly extract appliance-level data from the aggregated power signal. Furthermore, an improved k-nearest neighbors (IKNN) algorithm is presented to reduce the learning computation time and improve the classification performance. This results in highly improving the discrimination ability between appliances belonging to distinct categories. A deep evaluation of the proposed LPH-IKNN based solution is investigated under different data sets, in which the proposed scheme leads to promising performance. An accuracy of up to 99.65% and 98.51% has been achieved on GREEND and UK-DALE data sets, respectively. While an accuracy of more than 96% has been attained on both WHITED and PLAID data sets. This proves the validity of using 2D descriptors to accurately identify appliances and create new perspectives for the NILM problem. 2021 The Authors
راعي المشروعThis paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library
اللغةen
الناشرElsevier
الموضوع2D representation
Appliance identification
Feature extraction
Improved k-nearest neighbors
Local power histograms
Non-intrusive load monitoring
العنوانSmart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier
النوعArticle
رقم المجلد67
dc.accessType Open Access


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