Advanced Analytics for Harnessing the Power of Smart Meter Big Data

被引:0
|
作者
Alahakoon, Damminda [1 ]
Yu, Xinghuo [2 ]
机构
[1] Deakin Univ, Sch Informat & Business Analyt, Geelong, Vic 3217, Australia
[2] RMIT Univ, Platform Technol Res Inst, Melbourne, Vic, Australia
关键词
Advanced Metering Infrastructure (AMI); Smart Meters; Data Mining; Analytics; Big Data; Stream Analytics; CUSTOMER; IDENTIFICATION; NETWORKS;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Smart meters or advanced metering infrastructure (AMI) are being deployed in many countries around the world. Smart meters are the basic building block of the smart grid and governments have invested vast amounts in smart meter deployment targeting wide economic, social and environmental benefits. The key functionality of the smart meter is the capture and transfer of data relating to the consumption (electricity, gas) and events such as power quality and meter status. Such capability has also resulted in the generation of an unprecedented data volume, speed of collection and complexity, which has resulted in the so called big data challenge. To realize the hidden value and power in such data, it is important to use the appropriate tools and technology which are currently being called advanced analytics. In this paper we define a smart metering landscape and discuss different technologies available for harnessing the smart meter captured data. Main limitations and challenges with existing techniques with big data are also highlighted and several future directions in smart metering are presented.
引用
收藏
页码:40 / 45
页数:6
相关论文
共 50 条
  • [21] A residential labeled dataset for smart meter data analytics
    Pereira, Lucas
    Costa, Donovan
    Ribeiro, Miguel
    [J]. SCIENTIFIC DATA, 2022, 9 (01)
  • [22] 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
  • [23] Harnessing the Power of Big Data in Healthcare
    Nash, David B.
    [J]. AMERICAN HEALTH AND DRUG BENEFITS, 2014, 7 (02): : 69 - 70
  • [24] Smart Meter Data Analytics: Systems, Algorithms, and Benchmarking
    Liu, Xiufeng
    Golab, Lukasz
    Golab, Wojciech
    Ilyas, Ihab F.
    Jin, Shichao
    [J]. ACM TRANSACTIONS ON DATABASE SYSTEMS, 2017, 42 (01):
  • [25] Harnessing the Power of Big Data in Science
    Bhatnagar, Nitu
    [J]. INTERNATIONAL CONFERENCE ON ADVANCED MACHINE LEARNING TECHNOLOGIES AND APPLICATIONS (AMLTA2018), 2018, 723 : 479 - 485
  • [26] Smart Meter Data Analytics using R and Hadoop
    Mathiyalagan, P.
    Shanmugapriya, A.
    Geethu, A., V
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON ELECTRO INFORMATION TECHNOLOGY (EIT), 2017, : 623 - 629
  • [27] Big data analytics in smart grids: a review
    Zhang Y.
    Huang T.
    Bompard E.F.
    [J]. Energy Informatics, 1 (1)
  • [28] A Survey of Big Data Analytics for Smart Forestry
    Zou, Weitao
    Jing, Weipeng
    Chen, Guangsheng
    Lu, Yang
    Song, Houbing
    [J]. IEEE ACCESS, 2019, 7 : 46621 - 46636
  • [29] Big data analytics for smart factories of the future
    Gao, Robert X.
    Wang, Lihui
    Helu, Moneer
    Teti, Roberto
    [J]. CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2020, 69 (02) : 668 - 692
  • [30] Harnessing the Power of Machine Learning Algorithms and Big Data Analytics: Enhancing NSQIP Risk Predictions
    Janjua, Haroon M.
    Rogers, Michael P.
    Grimsley, Emily A.
    Read, Meagan
    Kuo, Paul C.
    [J]. JOURNAL OF THE AMERICAN COLLEGE OF SURGEONS, 2023, 237 (02) : 382 - 382