This paper proposes a new appliance identification scheme by introducing a novel approach for extracting highly discriminative characteristic sets that can considerably distinguish between various appliance footprints. In this context, a precise and powerful characteristic projection technique depending on fuzzy-neighbors-preserving analysis based QR-decomposition (FNPA-QR) is applied on the extracted energy consumption time-domain features. The FNPA-QR aims to diminish the distance among the between class features and increase the gap among features of dissimilar categories. Following, a novel bagging decision tree (BDT) classifier is also designed to further improve the classification accuracy. The proposed technique is then validated on three appliance energy consumption datasets, which are collected at both low and high frequency. The practical results obtained point out the outstanding classification rate of the time-domain based FNPA-QR and BDT.

中文翻译：

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使用基于 QR 分解的模糊邻居保留分析改进家用电器识别

本文提出了一种新的家电识别方案，通过引入一种新方法来提取具有高度辨别力的特征集，这些特征集可以显着区分各种家电足迹。在这种情况下，基于模糊邻域保留分析的 QR 分解 (FNPA-QR) 的精确而强大的特征投影技术应用于提取的能耗时域特征。FNPA-QR 旨在减少类特征之间的距离并增加不同类别特征之间的差距。接下来，还设计了一种新颖的装袋决策树（BDT）分类器以进一步提高分类精度。然后在三个设备能耗数据集上验证所提出的技术，这些数据集以低频和高频收集。