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D-A-CH Conference on Energy Informatics

EI 2015: Energy Informatics pp 69–80Cite as

The Effect of Data Granularity on Load Data Compression

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9424)

Abstract

A vast volume of data is generated through smart metering. Suitable compression mechanisms for this kind of data are highly desirable to better utilize low-bandwidth links and to save costs and energy. To date, the important factor of data resolution has been neglected in the compression of smart meter data. In this paper, we review and evaluate compression methods for smart metering in the context of different resolutions. We show that state-of-the-art compression methods are well suited for high resolution, but not for low resolution data. Furthermore, we elaborate on the compression performance differences between appliance-level and household-level load data. We conclude that the latter are practically incompressible at most resolutions.

Keywords

  • Execution Time
  • Compression Ratio
  • Smart Grid
  • Compression Algorithm
  • Code Word

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgements

The authors would like to thank Günther Eibl for his help in visualizing the compression ratio results. They would also like to thank their partner Salzburg AG for providing additional real-world load data.

The financial support by the Austrian Federal Ministry of Economy, Family and Youth and the Austrian National Foundation for Research, Technology and Development is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Josef Ressel Center for User-Centric Smart Grid Privacy, Security and Control, Salzburg University of Applied Sciences, Urstein Süd 1, 5412, Puch/Salzburg, Austria

    Andreas Unterweger & Dominik Engel

  2. Institut für Telematik, Technische Universität Hamburg-Harburg, Am Schwarzenberg-Campus 1, 21073, Hamburg, Germany

    Martin Ringwelski

Authors
  1. Andreas Unterweger
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  2. Dominik Engel
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  3. Martin Ringwelski
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Corresponding author

Correspondence to Andreas Unterweger .

Editor information

Editors and Affiliations

  1. FZI Forschungszentrum Informatik, Karlsruhe, Germany

    Sebastian Gottwalt

  2. Institut AIFB, KIT, Karlsruhe, Germany

    Lukas König

  3. Institut AIFB, KIT, Karlsruhe, Germany

    Hartmut Schmeck

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Unterweger, A., Engel, D., Ringwelski, M. (2015). The Effect of Data Granularity on Load Data Compression. In: Gottwalt, S., König, L., Schmeck, H. (eds) Energy Informatics. EI 2015. Lecture Notes in Computer Science(), vol 9424. Springer, Cham. https://doi.org/10.1007/978-3-319-25876-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-25876-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25875-1

  • Online ISBN: 978-3-319-25876-8

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