open access articleProviding 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
