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AuthorHimeur, Yassine
AuthorAlsalemi, Abdullah
AuthorBensaali, Faycal
AuthorAmira, Abbes
AuthorSardianos, Christos
AuthorVarlamis, Iraklis
AuthorDimitrakopoulos, George
Available date2022-12-29T07:34:45Z
Publication Date2021
Publication NameAdvances in Intelligent Systems and Computing
ResourceScopus
URIhttp://dx.doi.org/10.1007/978-3-030-55190-2_15
URIhttp://hdl.handle.net/10576/37839
AbstractIn recent years, the automatic identification of electrical devices through their power consumption signals finds a variety of applications in smart home monitoring and non-intrusive load monitoring (NILM). This work proposes a novel appliance identification scheme and introduces a new feature extraction method that represents power signals in a 2D space, similar to images and then extracts their properties. In this context, the local binary pattern (LBP) and other variants are investigated on their ability to extract histograms of 2D binary patterns of power signals. Specifically, by moving to a 2D representation space, each power sample is surrounded by eight neighbors at least. This can help extracting pertinent characteristics and providing more possibilities to encode power signals robustly. Moreover, the proposed identification technique has the main advantage of accurately recognizing the electrical devices independently of their states and on/off events, unlike existing models. Three public databases including real household power consumption measurements at the appliance-level are employed to assess the performance of the proposed system while considering various machine learning classifiers. The promising performance obtained in terms of accuracy and F-score proves the successful application of the 2D LBP in recognizing electrical devices and creates new possibilities for energy efficiency based on NILM models. Springer Nature Switzerland AG 2021.
SponsorThis 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.
Languageen
PublisherSpringer
SubjectAppliance identification
Classifiers
Feature extraction
Local binary pattern
Non-intrusive load monitoring
Power consumption signals
TitleOn the applicability of 2D local binary patterns for identifying electrical appliances in non-intrusive load monitoring
TypeConference Paper
Pagination188-205
Volume Number1252 AISC
dc.accessType Abstract Only


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