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

被引:314
|
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
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.
引用
收藏
页码:425 / 436
页数:12
相关论文
共 50 条
  • [1] A bibliometric survey on incremental clustering algorithm for electricity smart meter data analysis
    Archana Chaudhari
    Preeti Mulay
    [J]. Iran Journal of Computer Science, 2019, 2 (4) : 197 - 206
  • [2] Interactive search algorithm of artificial intelligence for household classification on smart electricity meter data
    Suresh, M.
    Anbarasi, M. S.
    [J]. INTERNATIONAL JOURNAL OF ENGINEERING SYSTEMS MODELLING AND SIMULATION, 2022, 13 (03) : 183 - 193
  • [3] A survey on behind the meter energy management systems in smart grid
    Bayram, Islam Safak
    Ustun, Taha Selim
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 72 : 1208 - 1232
  • [4] Analysis of Smart Meter Data for Electricity Consumers
    Dudek, Grzegorz
    Gawlak, Anna
    Kornatka, Miroslaw
    Szkutnik, Jerzy
    [J]. 2018 15TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET (EEM), 2018,
  • [5] Subgroup Discovery in Smart Electricity Meter Data
    Jin, Nanlin
    Flach, Peter
    Wilcox, Tom
    Sellman, Royston
    Thumim, Joshua
    Knobbe, Arno
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2014, 10 (02) : 1327 - 1336
  • [6] Smart Meter Data Privacy: A Survey
    Asghar, Muhammad Rizwan
    Dan, Gyorgy
    Miorandi, Daniele
    Chlamtac, Imrich
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2017, 19 (04): : 2820 - 2835
  • [7] Electricity Theft Detection Using Smart Meter Data
    Sahoo, Sanujit
    Nikovski, Daniel
    Muso, Toru
    Tsuru, Kaoru
    [J]. 2015 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT), 2015,
  • [8] Electricity Consumption Clustering Using Smart Meter Data
    Tureczek, Alexander
    Nielsen, Per Sieverts
    Madsen, Henrik
    [J]. ENERGIES, 2018, 11 (04)
  • [9] Smart Electricity Meter Data Analytics: A Brief Review
    Pawar, Savita
    Momin, B. F.
    [J]. 2017 IEEE REGION 10 INTERNATIONAL SYMPOSIUM ON TECHNOLOGIES FOR SMART CITIES (IEEE TENSYMP 2017), 2017,
  • [10] Compression of smart meter big data: A survey
    Wen, Lulu
    Zhou, Kaile
    Yang, Shanlin
    Li, Lanlan
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 91 : 59 - 69