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.

中文翻译：

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用于非侵入式家电识别的高效多描述符融合

对电器级功耗的意识可以帮助用户提高家庭能源效率。在本文中，提出了一种优越的非侵入式家电识别方法，可以提供每个家电的特定消费足迹。通过以下步骤，不同描述符的组合可以很好地识别电气设备： (a) 调查几种时域 (TD) 特征提取方案的适用性和性能可比性；(b) 探索它们的互补特征；(c) 利用集成装袋树 (EBT) 分类器的新设计。因此，提出了一种基于TD特征融合的强大特征提取技术，即fTDF，旨在提高特征辨别能力并优化识别任务。对称为 GREEND 和 WITHED 的两个不同数据集进行了广泛的实验性能评估，其中功耗特征分别在 1 Hz 和 44000 Hz 采样频率下收集。获得的结果表明，与其他 TD 描述符和机器学习分类器相比，所提出的基于 fTDF 的 EBT 系统的主要效率。