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A Big Data Platform for Smart Meter Data Analytics

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)250-259
Number of pages10
JournalComputers in Industry
Volume105
Early online date22 Jan 2019
DOIs
DateAccepted/In press - 13 Dec 2018
DateE-pub ahead of print - 22 Jan 2019
DatePublished (current) - 1 Feb 2019

Abstract

Smart grids have started generating an ever increasingly large volume of data. Extensive research has been done in meter data analytics for small data sets of electrical grid and electricity consumption. However limited research has investigated the methods, systems and tools to support data storage and data analytics for big data generated by smart grids. This work has proposed a new core-broker-client system architecture for big data analytics. Its implemented platform is named Smart Meter Analytics Scaled by Hadoop (SMASH). Our work has demonstrated that SMASH is able to perform data storage, query, analysis and visualization tasks on large data sets at 20 TB scale. The performance of SMASH in storing and querying large quantities of data are compared with the published results provided by Google Cloud, IBM, MongoDB, and AMPLab. The experimental results suggest that SMASH provides industry a competitive and easily operable platform to manage big energy data and visualize knowledge, with potential to support data-intensive decision making.

    Research areas

  • Big data, smart grid, meter data anaytics

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at https://www.sciencedirect.com/science/article/pii/S0166361518303749. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 995 KB, PDF-document

    Embargo ends: 22/01/21

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    Licence: CC BY-NC-ND

  • Supplementary information PDF

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at https://www.sciencedirect.com/science/article/pii/S0166361518303749. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 253 KB, PDF-document

    Embargo ends: 22/01/21

    Request copy

    Licence: CC BY-NC-ND

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