Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey

被引：336
作者：
Alahakoon, Damminda [1 ]
Yu, Xinghuo [2 ]
机构：
[1] La Trobe Univ, Melbourne, Vic 3086, Australia
[2] RMIT Univ, Platform Technol Res Inst, Melbourne, Vic 3000, Australia
来源：
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2016年 / 12卷 / 01期
关键词：
Artificial intelligence; automated meter infrastructure; big data; cloud computing; data analytics; Internet of Things (IoT); machine learning; privacy; smart grids (SGs); smart meters; DATA ANALYTICS; CUSTOMER; CLASSIFICATION; IDENTIFICATION; NETWORKS; DISAGGREGATION;
D O I：
10.1109/TII.2015.2414355
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social, and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics, which deals with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interests. Furthermore, the paper highlights challenges as well as opportunities arising due to the advent of big data and the increasing popularity of cloud environments.
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收藏
页码：425 / 436
页数：12
相关论文
共 94 条
[1]  
Abdel-Majeed Ahmad., 2012, Universities Power Engineering Conference (UPEC), 2012 47th International, P1, DOI DOI 10.1109/UPEC.2012.6398598
[2]   Principal component analysis [J].
Abdi, Herve ;
Williams, Lynne J. .
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2010, 2 (04) :433-459
[3]  
Abreu J., 2011, P EUR COUNC EN EFF E, P1739
[4]   Using pattern recognition to identify habitual behavior in residential electricity consumption [J].
Abreu, Joana M. ;
Pereira, Francisco Camara ;
Ferrao, Paulo .
ENERGY AND BUILDINGS, 2012, 49 :479-487
[5]  
Ahmad S., 2012, INT J SMART GRID CLE, V1, P15, DOI DOI 10.12720/SGCE.1.1.15-21
[6]  
Alahakoon D, 2013, 2013 IEEE INTERNATIONAL WORKSHOP ON INTELLIGENT ENERGY SYSTEMS (IWIES), P40, DOI 10.1109/IWIES.2013.6698559
[7]  
Anas M., 2012, 2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2012), P176, DOI 10.1109/3PGCIC.2012.42
[8]  
[Anonymous], 2013, P 26 IEEE CAN C EL C
[9]  
[Anonymous], 2014, TECHNICAL REPORT
[10]  
[Anonymous], 2012, P 2012 6 IEEE INT C, DOI DOI 10.1109/DEST.2012.6227925
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