IADIS International Journal on Computer Science and Information Systems

Published by IADIS (International Association for Development of the Information Society) • ISSN (Online): 1646-3692 • ISSN (Print): 1646-3692
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Big Data Processing for Smart Grids

Driss Benhaddou *
Mohamed Riduan Abid# *
Ouidad Achahbar# *
Nacer Khalil *
Tajjeeddine Rachidi# *
Maen Al Assaf *
* *University of Houston, Houston, TX, USA #Alakhawayn University in Ifrane, Ifrane, Morocco **The University of Jordan, Amman, Jordan (Portugal)
* *University of Houston, Houston, TX, USA #Alakhawayn University in Ifrane, Ifrane, Morocco **The University of Jordan, Amman, Jordan (Portugal)
* *University of Houston, Houston, TX, USA #Alakhawayn University in Ifrane, Ifrane, Morocco **The University of Jordan, Amman, Jordan (Portugal)
* *University of Houston, Houston, TX, USA #Alakhawayn University in Ifrane, Ifrane, Morocco **The University of Jordan, Amman, Jordan (Portugal)
* *University of Houston, Houston, TX, USA #Alakhawayn University in Ifrane, Ifrane, Morocco **The University of Jordan, Amman, Jordan (Portugal)
* *University of Houston, Houston, TX, USA #Alakhawayn University in Ifrane, Ifrane, Morocco **The University of Jordan, Amman, Jordan (Portugal)

Abstract

Smart Grids (SGs) are emerging as a promising technology meant to cope with the energy efficiency issue, currently witnessed in legacy electrical grids, by disseminating relevant information in a real-time mode among the different SG components. The SG Advanced Metering Infrastructure (AMI) form s a central SG component, and consists basically of meters/sensors that are regularly communicating data towards the Control Plane. Much of these communicated data emanates from wireless sensors, and falls in the realm of Big Data. The latter needs substantial high-performance compute (HPC) power for processing and mining. In this paper, we shed further light into a syne rgetic interface between SGs and the Cloud. We propose the use of Cloud computing to provide HPCaaS fo r SG Big Data processing, and delineate a suitable architecture. We present the blue print for deployi ng a real world private cloud testbed using OpenStack, Hadoop, and the MapReduce progra mming model. To assess the test bed functionalities, we run extensive experiments using benchmarked Big Data sets.

Keywords

Smart grids wireless sensors cloud computing high-performance compute
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Declarations & Ethics

Funding: This research received academic dissemination support through ESCAP / JournalsHub publishing programs.
Conflicts of Interest: The authors declare no competing financial or institutional interests.
Peer Review: Double-blind peer reviewed by international subject specialists.
License: Creative Commons Attribution 4.0 International (CC BY 4.0).
How to Cite This Article
APA / MLA / BibTeX
Benhaddou, et al. (2015). Big Data Processing for Smart Grids. IADIS International Journal on Computer Science and Information Systems, 10(1). https://doi.org/10.33965/ijcsis_2015_v10i1_04
Benhaddou, et al. "Big Data Processing for Smart Grids." IADIS International Journal on Computer Science and Information Systems, vol. 10, no. 1, 2015. https://doi.org/10.33965/ijcsis_2015_v10i1_04
Benhaddou, et al. "Big Data Processing for Smart Grids." IADIS International Journal on Computer Science and Information Systems 10, no. 1 (2015). https://doi.org/10.33965/ijcsis_2015_v10i1_04