  Consciousness about power consumption at the appliance level can assist user
in promoting energy efficiency in households. In this paper, a superior
non-intrusive appliance recognition method that can provide particular
consumption footprints of each appliance is proposed. Electrical devices are
well recognized by the combination of different descriptors via the following
steps: (a) investigating the applicability along with performance comparability
of several time-domain (TD) feature extraction schemes; (b) exploring their
complementary features; and (c) making use of a new design of the ensemble
bagging tree (EBT) classifier. Consequently, a powerful feature extraction
technique based on the fusion of TD features is proposed, namely fTDF, aimed at
improving the feature discrimination ability and optimizing the recognition
task. An extensive experimental performance assessment is performed on two
different datasets called the GREEND and WITHED, where power consumption
signatures were gathered at 1 Hz and 44000 Hz sampling frequencies,
respectively. The obtained results revealed prime efficiency of the proposed
fTDF based EBT system in comparison with other TD descriptors and machine
learning classifiers.
