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Data Science and Big Data: An Environment of Computational Intelligence pp 209–229Cite as

Big Data for Effective Management of Smart Grids

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Part of the book series: Studies in Big Data ((SBD,volume 24))

Abstract

The Energy industry is facing a set of changes. The old grids need to be replaced, alternative energy market is increasing and consumers want more control of their consumption. On the other hand, the ever-increasing pervasiveness of technology together with the smart paradigm, are becoming the reference point of anyone involved in innovation, and energy management issues. In this context, the information that can potentially be made available by technological innovation is obvious. Nevertheless, in order to turn it into better and more efficient decisions, it is necessary to keep in mind three sets of issues: those related to the management of generated data streams , those related to the quality of the data and finally those related to their usability for human decision-maker. In smart grid , large amounts of and various types of data, such as device status data, electricity consumption data, and user interaction data are collected. Then, as described in several scientific papers, many data analysis techniques, including optimization, forecasting, classification and other, can be applied on the large amounts of smart grid big data . There are several techniques, based on Big Data analysis using computational intelligence techniques, to optimize power generation and operation in real time, to predict electricity demand and electricity consumption and to develop dynamic pricing mechanisms. The aim of the chapter is to critically analyze the way Big Data is utilized in the field of Energy Management in Smart Grid addressing problems and discussing the important trends.

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Notes

  1. 1.

    http://www.4-noks.com.

  2. 2.

    http://smartbeecontrollers.com/.

  3. 3.

    http://www.sgip.org/.

  4. 4.

    http://collaborate.nist.gov/twiki-sggrid/bin/view/SmartGrid/SGIPCoSStandardsInformationLibrary.

  5. 5.

    http://graphite.net.

  6. 6.

    https://influxdata.com/.

  7. 7.

    opentsdb.net/.

  8. 8.

    https://project-sparks.eu/.

  9. 9.

    http://www.realvalueproject.com/.

  10. 10.

    http://www.offis.de/en/offis_in_portrait/structure/structure/projekte/ies-austria.html.

  11. 11.

    http://www.sospo.dk/.

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Authors and Affiliations

  1. Department of Industrial and Information Engineering, Second University of Naples, Caserta, Italy

    Alba Amato & Salvatore Venticinque

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  1. Alba Amato
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  2. Salvatore Venticinque
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Correspondence to Alba Amato .

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Editors and Affiliations

  1. Electrical & Computer Engineering, University of Alberta Electrical & Computer Engineering, Edmonton AL, Canada

    Witold Pedrycz

  2. Dept of CS and Information Engineering, National Taiwan Univ of Science and Tech Dept of CS and Information Engineering, Taipei, Taiwan

    Shyi-Ming Chen

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Amato, A., Venticinque, S. (2017). Big Data for Effective Management of Smart Grids. In: Pedrycz, W., Chen, SM. (eds) Data Science and Big Data: An Environment of Computational Intelligence. Studies in Big Data, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-53474-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-53474-9_10

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