Review on key techniques of non-intrusive load monitoring

被引：0
|
作者：
Guo H. [1 ,2 ]
Lu J. [1 ,2 ]
Yang P. [1 ,2 ]
Liu Z. [1 ,2 ]
机构：
[1] School of Electric Power, South China University of Technology, Guangzhou
[2] Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2021年 / 41卷 / 01期
关键词：
Machine learning; Non-intrusive load monitoring; Smart meter; Transparent power grid; Ubiquitous Power Internet of Things;
D O I：
10.16081/j.epae.202011001
中图分类号：
学科分类号：
摘要：
NILM(Non-Intrusive Load Monitoring) technology can identify the users' internal load by using the data measured at a single point on the bus,and it is one of the basic technologies for the construction of Ubiquitous Power Internet of Things and transparent power grid. Based on the analysis of the basic implementation framework and technical system of NILM,three key technical problems that need to be solved in NILM application are summarized,including data source selection,algorithm accuracy and scalability. In terms of data source selection,the application of low frequency and high frequency data source in NILM is analyzed and summarized,especially the application of smart meter in NILM. In the aspect of algorithm accuracy,the existing NILM algorithm model and algorithm evaluation scheme are reviewed and analyzed. In view of the problem that there are few researches related to scalability,the denoising recognition and the labeling and training of new load are analyzed and discussed through combining NILM with speech recognition and machine learning. Finally,the future development trend and application of NILM are prospected. © 2021, Electric Power Automation Equipment Press. All right reserved.
引用
收藏
页码：135 / 144
页数：9
相关论文
共 55 条
  • [1] LI Qinhao, ZHANG Yongjun, CHEN Jiaqi, Et al., Development patterns and challenges of Ubiquitous Power Internet of Things, Automation of Electric Power Systems, 44, 1, pp. 13-22, (2020)
  • [2] EHRHARDT K, DONNELLY K, LAIT J A., Advanced metering initiatives and residential feedback programs:a meta-review for household electricity-saving opportunities, (2010)
  • [3] HART G W., Nonintrusive appliance load monitoring[J], Pro-ceedings of the IEEE, 80, 12, pp. 1870-1891, (1992)
  • [4] HOSSEINI S S, AGBOSSOU K, KELOUWANI S, Et al., Non-intrusive load monitoring through home energy management systems:a comprehensive review[J], Renewable and Sustai-nable Energy Reviews, 79, pp. 1266-1274, (2017)
  • [5] HE D W, LIN W X, LIU N, Et al., Incorporating non-intrusive load monitoring into building level demand response[J], IEEE Transactions on Smart Grid, 4, 4, pp. 1870-1877, (2013)
  • [6] TABATABAEI S M, DICK S, XU W., Toward non-intrusive load monitoring via multi-label classification[J], IEEE Transactions on Smart Grid, 8, 1, pp. 26-40, (2017)
  • [7] LIANG J, NG S K K, KENDALL G, Et al., Load signature study:part Ⅰ:basic concept,structure,and methodology[J], IEEE Transactions on Power Delivery, 25, 2, pp. 551-560, (2010)
  • [8] ADABI A, MANTEY P, HOLMEGAARD E, Et al., Status and challenges of residential and industrial non-intrusive load monitoring, 2015 IEEE Conference on Technologies for Sustainability(SusTech), pp. 181-188, (2015)
  • [9] WANG Sitong, ZHOU Hui, YUAN Ruiming, Et al., Concept and application of smart meter, Power System Technology, 34, 4, pp. 17-23, (2010)
  • [10] ESA N F, ABDULLAH M P, HASSAN M Y., A review disag-gregation method in non-intrusive appliance load monitoring[J], Renewable and Sustainable Energy Reviews, 66, pp. 163-173, (2016)
123456