Big data, better energy management and control decisions for distribution systems in smart grid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Big Data is an essential element for energy management and control decision toward improved energy security, efficiency, and decreasing costs of energy use. Power distribution network is required to deliver electric energy reliability with reduced complexity and to be part of future smart grid. Therefore, in this paper Big Data related to the distribution generation systems will be discussed and illustrated within the context of smart grid principle. The paper work is to study the impact of adopting big data on energy management systems and to show the importance of the big data in strategic decision-making. The paper will highlight the Big Data issues and challenges associated with it in the energy management and control decisions in power distribution networks.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3115-3120
Number of pages6
ISBN (Electronic)9781467390040
DOIs
Publication statusPublished - 2 Feb 2017
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: 5 Dec 20168 Dec 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
CountryUnited States
CityWashington
Period5/12/168/12/16

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Keywords

  • Big Data
  • Big Data analytics
  • Distribution systems
  • Energy Mangement
  • Smart grid

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Hardware and Architecture

Cite this

Khalil, S., Abu-Rub, H., & Mohamed, A. (2017). Big data, better energy management and control decisions for distribution systems in smart grid. In Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 (pp. 3115-3120). [7840966] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2016.7840966