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AuthorHimeur, Yassine
AuthorAlsalemi, Abdullah
AuthorBensaali, Faycal
AuthorAmira, Abbes
Available date2022-12-29T07:34:41Z
Publication Date2020
Publication NameApplied Energy
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.apenergy.2020.114877
URIhttp://hdl.handle.net/10576/37795
AbstractProviding the user with appliance-level consumption data is the core of each energy efficiency system. To that end, non-intrusive load monitoring is employed for extracting appliance specific consumption data at a low cost without the need of installing separate submeters for each electrical device. In this context, we propose in this paper a novel non-intrusive appliance recognition system based on (i) detecting events in the aggregated power signal using a novel and powerful scheme, (ii) applying multiscale wavelet packet tree to collect comprehensive energy consumption features, and (iii) adopting an ensemble bagging tree classifier along with comparing its performance with various machine learning schemes. Moreover, to validate the proposed model, an empirical investigation is conducted on two real and public energy consumption datasets, namely, the GREEND and REDD, in which consumption readings are collected at low-frequencies. In addition, a comprehensive review of recent non-intrusive load monitoring approaches has been conducted and presented, in which their characteristics, performances and limitations are described. The proposed non-intrusive load monitoring system shows a high appliance recognition performance in terms of the accuracy, F1 score and low time complexity when it has been applied to different households from the GREEND and REDD repositories, in which every house includes various domestic appliances. Obtained results have described, e.g., that average accuracies of 97.01% and 96.36% have been reached on the GREEND and REDD datasets, respectively, which outperformed almost existing solutions considered in this framework. 2020 Elsevier Ltd
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
PublisherElsevier
SubjectAppliance recognition
Energy efficiency
Ensemble bagging tree
Event detection
Multi-scale wavelet packet tree
Non-intrusive load monitoring
TitleRobust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree
TypeArticle
Volume Number267
dc.accessType Open Access


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