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Design and Life Cycle Data Analysis for Smart Metering

  • Josef Horalek
  • Vladimir SobeslavEmail author
Conference paper
  • 218 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1178)

Abstract

The presented article introduces an issue of data processing in the Smart Metering from the life cycle analysis persepctive. The life cycle of data has been identified and formalized; based on the results, the methodology for control and usage of the data in the Smart Metering area has been proposed. The methodology has been verified in three chosen pilot projects realized by a company owning licence for electric power distribution in the Czech Republic. On the grounds of verification, the fundamental areas that seem to be critical for effective usage of Smart Metering and AMM in distribution system are formulated.

Keywords

AMM Smart grid Smart metering Methodology Data processing 

References

  1. 1.
    Horalek, J., Sobeslav, V., Krejcar, O., Balik, L.: Communications and security aspects of smart grid networks design. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2014. CCIS, vol. 465, pp. 35–46. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11958-8_4CrossRefGoogle Scholar
  2. 2.
    Kabalci, Y.: A survey on smart metering and smart grid communication. Renew. Sustain. Energy Rev. 57, 302–318 (2016).  https://doi.org/10.1016/j.rser.2015.12.114. ISSN 1364-0321CrossRefGoogle Scholar
  3. 3.
    Sobeslav, V., Horalek, J.: Communications and quality aspects of smart grid network design. In: Wong, W.E. (ed.) Proceedings of the 4th International Conference on Computer Engineering and Networks, pp. 1255–1262. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-11104-9_143CrossRefGoogle Scholar
  4. 4.
    Komarek, A., Pavlik, J., Mercl, L., Sobeslav, V.: Hardware layer of ambient intelligence environment implementation. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10449, pp. 325–334. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67077-5_31CrossRefGoogle Scholar
  5. 5.
    Zhou, Z., Gong, J., He, Y., Zhang, Y.: Software defined machine-to-machine communication for smart energy management. IEEE Commun. Mag. 55(10), 52–60 (2017).  https://doi.org/10.1109/MCOM.2017.1700169CrossRefGoogle Scholar
  6. 6.
    Xu, S., Qian, Y., Qingyang Hu, R.: Reliable and resilient access network design for advanced metering infrastructures in smart grid. IET Smart Grid 1(1), 24–30 (2018).  https://doi.org/10.1049/iet-stg.2018.0008CrossRefGoogle Scholar
  7. 7.
    Rawat, D.B., Bajracharya, C.: Detection of false data injection attacks in smart grid communication systems. IEEE Signal Process. Lett. 22(10), 1652–1656 (2015).  https://doi.org/10.1109/LSP.2015.2421935CrossRefGoogle Scholar
  8. 8.
    Horalek, J., Sobeslav, V.: Analysis of the error rate in electrometers for smart grid metering. In: Nguyen, N.T., Gaol, F.L., Hong, T.-P., Trawiński, B. (eds.) ACIIDS 2019. LNCS (LNAI), vol. 11432, pp. 533–542. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-14802-7_46. SCIACCA, Samuel C. IEEE 2030 ® Smart Grid Interoperability Standards What is Smart Grid ? [online]. 2012, 1–20 [vid. 2019-04-03]CrossRefGoogle Scholar
  9. 9.
    Zhang, Y., Huang, T., Bompard, E.F.: Energy Inform 1, 8 (2018).  https://doi.org/10.1186/s42162-018-0007-5CrossRefGoogle Scholar
  10. 10.
    Murray, D.M., Stankovic, L., Stankovic, V., Espinoza-Orias, N.D.: Appliance electrical consumption modelling at scale using smart meter data. J. Cleaner Prod. 187, 237–249 (2018).  https://doi.org/10.1016/j.jclepro.2018.03.163. ISSN 0959-6526CrossRefGoogle Scholar
  11. 11.
    Yao, H.-W., Wang, X.-W., Wu, L.-S., Jiang, D., Luo, T., Liang, D.: Prediction method for smart meter life based on big data. Procedia Eng. 211, 1111–1114 (2018).  https://doi.org/10.1016/j.proeng.2017.12.116. ISSN 1877-7058CrossRefGoogle Scholar
  12. 12.
    IEC: IEC - Smart Grid Standards Map [vid. 2018-04-01]. http://smartgridstandardsmap.com/
  13. 13.
    ITU: SG15: Smart Grid [vid. 2019-04-03]. https://www.itu.int/en/ITU-T/studygroups/Pages/sg15-sg.aspx
  14. 14.
    Wang, J., Zhou, P., Huang, G., Wang, W.: A data mining approach to discover critical events for event-driven optimization in building air conditioning systems. Energy Procedia 143, 251–257 (2017).  https://doi.org/10.1016/j.egypro.2017.12.680. ISSN 1876-6102CrossRefGoogle Scholar
  15. 15.
    Tao, F., Qi, Q., Liu, A., Kusiak, A.: Data-driven smart manufacturing. J. Manufact. Syst. (2018).  https://doi.org/10.1016/j.jmsy.2018.01.006CrossRefGoogle Scholar
  16. 16.
    Takahashi, Y., Fujimoto, Y., Hayashi, Y.: Forecast of infrequent wind power ramps based on data sampling strategy. Energy Procedia 135, 496–503 (2017).  https://doi.org/10.1016/j.egypro.2017.09.494. ISSN 1876-6102CrossRefGoogle Scholar
  17. 17.
    Intezari, A., Pauleen, D.J., Taskin, N.: The DIKW hierarchy and management decision-making. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, pp. 4193–4201 (2016).  https://doi.org/10.1109/hicss.2016.520
  18. 18.
    Jennex, M.E.: Big data, the internet of things, and the revised knowledge pyramid. SIGMIS Database 48(4), 69–79 (2017).  https://doi.org/10.1145/3158421.3158427CrossRefGoogle Scholar
  19. 19.
    Burnay, C., Jureta, I.J., Linden, I., et al.: Softw. Syst. Model. 15, 531 (2016).  https://doi.org/10.1007/s10270-014-0417-1CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Faculty of Informatics and ManagementUniversity of Hradec KraloveHradec KraloveCzech Republic

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