Skip to main content

The Effect of Data Granularity on Load Data Compression

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


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.


  • 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.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-25876-8_7
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   44.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-25876-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   59.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.


  1. Coded Character Sets - 7-Bit American National Standard Code for Information Interchange (7-Bit ASCII) (1986)

    Google Scholar 

  2. Distribution Automation Using Distribution Line Carrier Systems - Part 6: A-XDR Encoding Rule (2000)

    Google Scholar 

  3. Electricity Metering - Data Exchange for Meter Reading, Tariff and Load Control - Part 21: Direct Local Data Exchange (2002)

    Google Scholar 

  4. Efthymiou, C., Kalogridis, G.: Smart grid privacy via anonymization of smart metering data. In: Proceedings of First IEEE International Conference on Smart Grid Communications, Gaithersburg, Maryland, USA, pp. 238–243 (2010)

    Google Scholar 

  5. Eibl, G., Engel, D.: Influence of data granularity on smart meter privacy. IEEE Trans. Smart Grid 6(2), 930–939 (2015)

    CrossRef  Google Scholar 

  6. Engel, D.: Wavelet-based load profile representation for smart meter privacy. In: Proceedings of IEEE PES Innovative Smart Grid Technologies (ISGT 2013), Washington, D.C., USA, pp. 1–6 (2013).

  7. European Commission: Cost-benefit analyses & state of play of smart metering deployment in the EU-27. Technical report, European Commission Report (2014).

  8. Khan, J., Bhuiyan, S., Murphy, G., Arline, M.: Embedded zerotree wavelet based data compression for smart grid. In: 2013 IEEE Industry Applications Society Annual Meeting, pp. 1–8 (2013)

    Google Scholar 

  9. Kolter, J., Johnson, M.J.: Redd: a public data set for energy disaggregation research. In: Workshop on Data Mining Applications in Sustainability (SIGKDD), pp. 1–6, August 2011

    Google Scholar 

  10. Ning, J., Wang, J., Gao, W., Liu, C.: A wavelet-based data compression technique for smart grid. IEEE Trans. Smart Grid 2(1), 212–218 (2011)

    CrossRef  Google Scholar 

  11. Ringwelski, M., Renner, C., Reinhardt, A., Weigel, A., Turau, V.: The Hitchhiker’s guide to choosing the compression algorithm for your smart meter data. In: 2012 IEEE International Energy Conference and Exhibition (ENERGYCON), pp. 935–940, September 2012

    Google Scholar 

  12. Sankar, L., Rajagopalan, S.R., Mohajer, S., Poor, H.V.: Smart meter privacy: a theoretical framework. IEEE Trans. Smart Grid 4(2), 837–846 (2013)

    CrossRef  Google Scholar 

  13. Unterweger, A., Engel, D.: Resumable load data compression in smart grids. IEEE Trans. Smart Grid 6(2), 919–929 (2015).

    CrossRef  Google Scholar 

Download references


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


Corresponding author

Correspondence to Andreas Unterweger .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

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.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25875-1

  • Online ISBN: 978-3-319-25876-8

  • eBook Packages: Computer ScienceComputer Science (R0)