Load Signature Study-Part I: Basic Concept, Structure, and Methodology

被引:337
作者
Liang, Jian [1 ]
Ng, Simon K. K. [1 ]
Kendall, Gail [1 ]
Cheng, John W. M. [1 ]
机构
[1] CLP Res Inst Ltd, Hong Kong, Hong Kong, Peoples R China
关键词
Artificial neural network; committee decision mechanism; electric-load intelligence; load disaggregation; load signature; smart metering; RECOGNITION;
D O I
10.1109/TPWRD.2009.2033799
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Load signature is the unique consumption pattern intrinsic to each individual electrical appliance/piece of equipment. This paper focus on building a universal platform to better understand and explore the nature of electricity consumption patterns using load signatures and advanced technology, such as feature extraction and intelligent computing. Through this knowledge, we can explore and develop innovative applications to achieve better utilization of resources and develop more intelligent ways of operation. This paper depicts the basic concept, features of load signatures, structure and methodology of applying mathematical programming techniques, pattern recognition tools, and committee decision mechanism to perform load disaggregation. New indices are also introduced to aid our understanding of the nature of load signatures and different disaggregation algorithms.
引用
收藏
页码:551 / 560
页数:10
相关论文
共 18 条
[1]  
Akbar M., 2007, Multitopic Conference, P1, DOI [DOI 10.1109/INMIC.2007.4557691, 10.1109/INMIC.2007.4557691]
[2]  
Arrillaga J., 2000, Power System Quality Assessment
[3]  
BIAN Zhao-qi, 2000, Pattern recognition
[4]   Nonintrusive monitoring of electric loads [J].
Drenker, S ;
Kader, A .
IEEE COMPUTER APPLICATIONS IN POWER, 1999, 12 (04) :47-51
[5]  
Duan J, 2004, 2004 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL 5, PROCEEDINGS, P988
[6]   Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses [J].
Farinaccio, L ;
Zmeureanu, R .
ENERGY AND BUILDINGS, 1999, 30 (03) :245-259
[7]   NONINTRUSIVE APPLIANCE LOAD MONITORING [J].
HART, GW .
PROCEEDINGS OF THE IEEE, 1992, 80 (12) :1870-1891
[8]  
Haykin S., 1999, Neural Networks: A Comprehensive Foundation, DOI DOI 10.1017/S0269888998214044
[9]  
Jie Chen, 2006, 2006 First Bio-Inspired Models of Network, Information and Computing Systems (IEEE Cat No. 06EX1490), P1
[10]   A novel method to construct taxonomy of electrical appliances based on load signatures [J].
Lam, H. Y. ;
Fung, G. S. K. ;
Lee, W. K. .
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2007, 53 (02) :653-660