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Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction
Applied Energy ( IF 11.2 ) Pub Date : 2020-09-17 , DOI: 10.1016/j.apenergy.2020.115872
Yassine Himeur , Abdullah Alsalemi , Faycal Bensaali , Abbes Amira

Recently, a growing interest has been dedicated towards developing and implementing low-cost energy efficiency solutions in buildings. Accordingly, non-intrusive load monitoring has been investigated in various academic and industrial projects for capturing device-specific consumption footprints without any additional hardware installation. However, its performance should be improved further to enable an accurate appliance identification from the aggregated load. This paper presents an efficient non-intrusive load monitoring framework that consists of the following main components: (i) a novel fusion of multiple time-domain features is proposed to extract appliance fingerprints; (ii) a dimensionality reduction scheme is introduced to be applied to the fused time-domain features, which relies on fuzzy-neighbors preserving analysis based QR-decomposition. The latter can not only reduce feature dimensionality, but it can also effectively decrease the intra-class distances and increase the extra-class distances of appliance features; and (iii) a powerful decision bagging tree classifier is implemented to accurately classify electrical devices using the reduced features. Empirical evaluations performed on three real datasets, namely ACS-F2, REDD and WHITED collected at different sampling rates have shown a promising performance, according to the accuracy and F1 score achieved using the proposed non-intrusive load monitoring system. Reported accuracy and F1 score have reached both 100% for the WHITED dataset, 99.79% and 99.76% for the REDD dataset, and up to 99.41% and 98.93% for the ACS-f2 dataset, respectively. The outstanding performance achieved using the proposed solution determines its effectiveness in collecting individual-appliance consumption data and in promoting energy saving behaviors.



中文翻译:

基于新型降维多指标融合的建筑物非侵入式有效载荷监测

最近,人们越来越关注在建筑物中开发和实施低成本的能源效率解决方案。因此,已经在各种学术和工业项目中研究了非侵入式负载监视,以捕获特定于设备的消耗足迹,而无需任何额外的硬件安装。但是,应进一步提高其性能,以根据汇总的负载进行准确的设备识别。本文提出了一种有效的非侵入式负载监控框架,该框架包括以下主要组件:(i)提出了一种新颖的融合多个时域特征的方法来提取设备指纹;(ii)引入降维方案以应用于融合的时域特征,它依赖于基于QR分解的模糊邻居保存分析。后者不仅可以减小特征维数,还可以有效地减少类内距离,增加家电特征的类外距离。(iii)实施功能强大的决策袋树分类器,以使用简化后的功能对电子设备进行准确分类。根据使用拟议的非侵入式负荷监测系统获得的准确性和F1分数,对以不同采样率收集的三个真实数据集ACS-F2,REDD和WHITED进行的实证评估显示出令人鼓舞的性能。WHITED数据集的报告准确性和F1分数均达到100%,REDD数据集的报告准确性和F1分数分别达到99.79%和99.76%,而ACS-f2数据集分别达到99.41%和98.93%。

更新日期:2020-09-18
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