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This paper proposes a new appliance identification scheme by introducing anovel approach for extracting highly discriminative characteristic sets thatcan considerably distinguish between various appliance footprints. In thiscontext, a precise and powerful characteristic projection technique dependingon fuzzy-neighbors-preserving analysis based QR-decomposition (FNPA-QR) isapplied on the extracted energy consumption time-domain features. The FNPA-QRaims to diminish the distance among the between class features and increase thegap among features of dissimilar categories. Following, a novel baggingdecision tree (BDT) classifier is also designed to further improve theclassification accuracy. The proposed technique is then validated on threeappliance energy consumption datasets, which are collected at both low and highfrequency. The practical results obtained point out the outstandingclassification rate of the time-domain based FNPA-QR and BDT.

Improving in-home appliance identification using fuzzy-neighbors-preserving analysis based QR-decomposition