Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree

Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali and Abbes Amira

Applied Energy, 2020, vol. 267, issue C, No S0306261920303895

Abstract: Providing 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.

Keywords: Energy efficiency; Non-intrusive load monitoring; Appliance recognition; Event detection; Ensemble bagging tree; Multi-scale wavelet packet tree (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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DOI: 10.1016/j.apenergy.2020.114877

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