Skip to main content

Data Quality Management Framework for Smart Grid Systems

  • Conference paper
  • First Online:
Book cover Business Information Systems (BIS 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 354))

Included in the following conference series:

Abstract

New devices in smart grid such as smart meters and sensors have emerged to become a massive and complex network, where a large volume of data is flowing to the smart grid systems. Those data can be real-time, fast-moving, and originated from a vast variety of terminal devices. However, the big smart grid data also bring various data quality problems, which may cause the delayed, inaccurate analysis of results, even fatal errors in the smart grid system. This paper, therefore, identifies a comprehensive taxonomy of typical data quality problems in the smart grid. Based on the adaptation of established data quality research and frameworks, this paper proposes a new data quality management framework that classifies the typical data quality problems into related data quality dimensions, contexts, as well as countermeasures. Based on this framework, this paper not only provides a systematic overview of data quality in the smart grid domain, but also offers practical guidance to improve data quality in smart grids such as which data quality dimensions are critical and which data quality problems can be addressed in which context.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abo, R., Even, A.: Managing the quality of smart grid data research in progress. In: IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies, pp. 5–8 (2016)

    Google Scholar 

  2. Al-Omar, B., Al-Ali, A., Ahmed, R., Landolsi, T.: Role of information and communication technologies in the smart grid. J. Emerg. Trends Comput. Inf. Sci. 3(5), 707–716 (2012)

    Google Scholar 

  3. Alahakoon, D., Yu, X.: Smart electricity meter data intelligence for future energy systems: a survey. IEEE Trans. Industr. Inf. 12(1), 425–436 (2016)

    Article  Google Scholar 

  4. Buchmann, E., Böhm, K., Burghardt, T., Kessler, S.: Re-identification of smart meter data. Pers. Ubiquit. Comput. 17(4), 653–662 (2013)

    Article  Google Scholar 

  5. Burnett, R.O., Butts, M.M., Sterlina, P.S.: Power system applications for phasor measurement units. IEEE Comput. Appl. Power 7(1), 8–13 (1994)

    Article  Google Scholar 

  6. Chen, J., Li, W., Lau, A., Cao, J., Wang, K.: Automated load curve data cleansing in power systems. IEEE Trans. Smart Grid 1(2), 213–221 (2010)

    Article  Google Scholar 

  7. Chen, P.Y., Cheng, S.M., Chen, K.C.: Smart attacks in smart grid communication networks. IEEE Commun. Mag. 50(8), 24–29 (2012)

    Article  Google Scholar 

  8. Chen, W., Zhou, K., Yang, S., Wu, C.: Data quality of electricity consumption data in a smart grid environment. Renew. Sustain. Energy Rev. 75, 98–105 (2017)

    Article  Google Scholar 

  9. Chren, S., Rossi, B., Buhnova, B., Pitner, T.: Reliability data for smart grids: where the real data can be found. In: 2018 Smart City Symposium Prague, pp. 1–6 (2018)

    Google Scholar 

  10. Chren, S., Rossi, B., Pitner, T.: Smart grids deployments within EU projects: the role of smart meters. In: 2016 Smart Cities Symposium Prague, pp. 1–5. IEEE (2016)

    Google Scholar 

  11. Daki, H., El Hannani, A., Aqqal, A., Haidine, A., Dahbi, A.: Big data management in smart grid: concepts, requirements and implementation. J. Big Data 4(1), 13 (2017)

    Article  Google Scholar 

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

    Google Scholar 

  13. Eichinger, F., Pathmaperuma, D., Vogt, H., Müller, E.: Data analysis challenges in the future energy domain. In: Computational Intelligent Data Analysis for Sustainable Development, pp. 181–242 (2013)

    Google Scholar 

  14. Gao, J., Xiao, Y., Liu, J., Liang, W., Chen, C.P.: A survey of communication/networking in smart grids. Future Gener. Comput. Syst. 28(2), 391–404 (2012)

    Article  Google Scholar 

  15. Ge, M., Helfert, M.: A framework to assess decision quality using information quality dimensions. In: Proceedings of the 11th International Conference on Information Quality, pp. 455–466. MIT, USA (2006)

    Google Scholar 

  16. Ge, M., Helfert, M.: Effects of information quality on inventory management. Int. J. Inf. Qual. 2(2), 177–191 (2008)

    Google Scholar 

  17. Ge, M., Helfert, M., Jannach, D.: Information quality assessment: validating measurement dimensions and processes. In: 19th European Conference on Information Systems, Helsinki, Finland, p. 75 (2011)

    Google Scholar 

  18. Ge, M., O’Brien, T., Helfert, M.: Predicting data quality success - the Bullwhip effect in data quality. In: Johansson, B., Møller, C., Chaudhuri, A., Sudzina, F. (eds.) BIR 2017. LNBIP, vol. 295, pp. 157–165. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64930-6_12

    Chapter  Google Scholar 

  19. Gellings, C.W., Samotyj, M., Howe, B.: The future’s smart delivery system. IEEE Power Energ. Mag. 2(5), 40–48 (2004)

    Article  Google Scholar 

  20. Jakkula, V., Cook, D.: Outlier detection in smart environment structured power datasets. In: Sixth International Conference on Intelligent Environments, pp. 29–33. IEEE (2010)

    Google Scholar 

  21. Jawurek, M., Johns, M., Rieck, K.: Smart metering de-pseudonymization. In: 27th Annual Computer Security Applications Conference, pp. 227–236 (2011)

    Google Scholar 

  22. Kim, M., Park, S., Lee, J., Joo, Y., Choi, J.K.: Learning-based adaptive imputation methodwith kNN algorithm for missing power data. Energies 10(10), 1668 (2017)

    Article  Google Scholar 

  23. Kosut, O., Jia, L., Thomas, R.J., Tong, L.: Malicious data attacks on the smart grid. IEEE Trans. Smart Grid 2(4), 645–658 (2011)

    Article  Google Scholar 

  24. Leonardi, A., Ziekow, H., Strohbach, M., Kikiras, P.: Dealing with data quality in smart home environments lessons learned from a smart grid pilot. J. Sens. Actuator Netw. 5(1), 5 (2016)

    Article  Google Scholar 

  25. Li, F., et al.: Smart transmission grid: vision and framework. IEEE Trans. Smart Grid 1(2), 168–177 (2010)

    Article  Google Scholar 

  26. Li, F., Luo, B.: Preserving data integrity for smart grid data aggregation. In: 2012 IEEE Third International Conference on Smart Grid Communications, pp. 366–371. IEEE (2012)

    Google Scholar 

  27. Liu, Y., Ning, P., Reiter, M.K.: False data injection attacks against state estimation in electric power grids. ACM Trans. Inf. Syst. Secur. 14(1), 13 (2011)

    Article  Google Scholar 

  28. Matta, N., Rahim-Amoud, R., Merghem-Boulahia, L., Jrad, A.: Putting sensor data to the service of the smart grid: from the substation to the AMI. J. Netw. Syst. Manage. 26(1), 108–126 (2018)

    Article  Google Scholar 

  29. Peppanen, J., Zhang, X., Grijalva, S., Reno, M.J.: Handling bad or missing smart meter data through advanced data imputation. In: IEEE Innovative Smart Grid Technologies Conference, pp. 1–5. IEEE (2016)

    Google Scholar 

  30. Rao, R., Akella, S., Guley, G.: Power line carrier (PLC) signal analysis of smart meters for outlier detection. In: IEEE International Conference on Smart Grid Communications, pp. 291–296. IEEE (2011)

    Google Scholar 

  31. Rossi, B., Chren, S., Buhnova, B., Pitner, T.: Anomaly detection in smart grid data: an experience report. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 2313–2318. IEEE (2016)

    Google Scholar 

  32. Shishido, J., Solutions, E.U.: Smart meter data quality insights. In: ACEEE Summer Study on Energy Efficiency in Buildings (2012)

    Google Scholar 

  33. Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manag. Inf. Syst. 12(4), 5–33 (1996)

    Article  Google Scholar 

  34. Zhang, Q., Luo, X., Bertagnolli, D., Maslennikov, S., Nubile, B.: PMU data validation at iso new england. In: 2013 IEEE Power and Energy Society General Meeting (PES), pp. 1–5. IEEE (2013)

    Google Scholar 

  35. Zhang, Y., Huang, T., Bompard, E.F.: Big data analytics in smart grids: a review. Energy Inform. 1(1), 8 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

The research was supported from ERDF/ESF “CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/16_019/0000822).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mouzhi Ge .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ge, M., Chren, S., Rossi, B., Pitner, T. (2019). Data Quality Management Framework for Smart Grid Systems. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 354. Springer, Cham. https://doi.org/10.1007/978-3-030-20482-2_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20482-2_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20481-5

  • Online ISBN: 978-3-030-20482-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics