论文标题
有效的多描述符融合，用于非侵入式设备识别
Efficient multi-descriptor fusion for non-intrusive appliance recognition
论文作者
论文摘要
关于设备级别功耗的意识可以帮助用户促进家庭的能源效率。在本文中，提出了一种可以提供每个设备的特定消费足迹的卓越的非侵入式设备识别方法。通过以下步骤将不同的描述符组合组合来很好地认识到电气设备：（a）研究适用性以及几个时域（TD）特征提取方案的性能可比性； （b）探索他们的互补特征； （c）利用整体装袋树（EBT）分类器的新设计。因此，提出了一种基于TD功能融合的功能提取技术，即FTDF，旨在提高功能歧视能力并优化识别任务。在称为Greend and Withed的两个不同数据集上进行了广泛的实验性能评估，分别以1 Hz和44000 Hz采样频率收集功耗特征。获得的结果揭示了与其他TD描述符和机器学习分类器相比，提出的基于FTDF的EBT系统的主要效率。
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.
