Big data application in power systems

Big data application in power systems

by Arghandeh, Reza Zhou, Yuxun

Type Book
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Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurem…

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Title Statements

  • Full title :Big data application in power systems / edited by Reza Arghandeh, Yuxun Zhou.

Summary

Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data. The book chapters discuss challenges, opportunities, success stories and pathways for utilizing big data value in smart grids.Provides expert analysis of the latest developments by global authoritiesContains detailed references for further reading and extended researchProvides additional cross-disciplinary lessons learned from broad disciplines such as statistics, computer science and bioinformaticsFocuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data.

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Edition Statements

  • Publication Date:copyright 2018

Publication Statements

  • Publisher(s):Elsevier
  • Place of Publication:Amsterdam, Netherlands
  • Language:eng
  • Publication Date:copyright 2018

The Physical Object

  • Description:1 ressource en ligne (1 ressource électronique.)

Edition Identifiers

  • ISBN:9780128119693, 0128119691
  • oclc:on1084321102
  • LC Classification:TK1001
  • Dewey Decimal:621.31

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