Comparing Electricity Consumer Categories Based on Load Pattern Clustering with Their Natural Types

  • Zigui Jiang
  • Rongheng Lin
  • Fangchun Yang
  • Zhihan Liu
  • Qiqi Zhang
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10393)

Abstract

As one aspect of smart city, smart gird has similar situation such as big data issue. Data analysis of daily load data generated by smart meters can benefit both electricity suppliers and end consumers. Electricity consumer categorization based on load pattern clustering is one of research subjects. This paper aims to achieve a better understanding of electricity consumer categorization by detecting the relationships among consumer categories and their natural types. A two-stage clustering based on multi-level 1D discrete wavelet transform and K-means algorithm is applied to perform daily load curve clustering and load pattern clustering. Additionally, to obtain distinct consumer categories, method of category identification based on association rule mining and characteristic similarity is also proposed in this paper. Experiment is conducted on data set of 24-value daily load data with labels of consumer types. Based on the comparison of experimental results, both relationships and differences exist among consumer categories and consumer types but consumer types cannot determine consumer categories.

Keywords

Smart Grid Consumer category Consumer type Load pattern Clustering 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This work is supported by the National High Technology Research and Development Program (863 Program) of China (2015AA050203) and Beijing Natural Science Foundation (4174099).

References

  1. 1.
    Kitchin, R.: The real-time city? big data and smart urbanism. GeoJournal 79(1), 1–14 (2014)CrossRefGoogle Scholar
  2. 2.
    Mets, K., Depuydt, F., Develder, C.: Two-stage load pattern clustering using fast wavelet transformation. IEEE Trans. Smart Grid 7(5), 2250–2259 (2016)CrossRefGoogle Scholar
  3. 3.
    Kwac, J., Flora, J., Rajagopal, R.: Household energy consumption segmentation using hourly data. IEEE Trans. Smart Grid 5(1), 420–430 (2014)CrossRefGoogle Scholar
  4. 4.
    Figueiredo, V., Rodrigues, F., Vale, Z., Gouveia, J.B.: An electric energy consumer characterization framework based on data mining techniques. IEEE Trans. Power Syst. 20(2), 596–602 (2005)CrossRefGoogle Scholar
  5. 5.
    Albert, A., Rajagopal, R.: Smart meter driven segmentation: what your consumption says about you. IEEE Trans. Power Syst. 28(4), 4019–4030 (2013)CrossRefGoogle Scholar
  6. 6.
    Alahakoon, D., Yu, X.: Smart electricity meter data intelligence for future energy systems: a survey. IEEE Trans. Ind. Inform. 12(1), 425–436 (2016)CrossRefGoogle Scholar
  7. 7.
    Haben, S., Singleton, C., Grindrod, P.: Analysis and clustering of residential customers energy behavioral demand using smart meter data. IEEE Trans. Smart Grid 7(1), 136–144 (2016)CrossRefGoogle Scholar
  8. 8.
    Chicco, G.: Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy 42(1), 68–80 (2012)CrossRefGoogle Scholar
  9. 9.
    Panapakidis, I.P., Alexiadis, M.C., Papagiannis, G.K.: Electricity customer characterization based on different representative load curves. In: 2012 9th International Conference on the European Energy Market, pp. 1–8. IEEE (2012)Google Scholar
  10. 10.
    Khumchoo, K.Y., Kongprawechnon, W.: Cluster analysis for primary feeder identification using metering data. In: 2015 6th International Conference of Information and Communication Technology for Embedded Systems, pp. 1–6. IEEE (2015)Google Scholar
  11. 11.
    Wang, Y., Chen, Q., Kang, C., Zhang, M., Wang, K., Zhao, Y.: Load profiling and its application to demand response: a review. Tsinghua Sci. Technol. 20(2), 117–129 (2015)CrossRefGoogle Scholar
  12. 12.
    Vendramin, L., Campello, R.J., Hruschka, E.R.: On the comparison of relative clustering validity criteria. In: SDM, pp. 73–744. SIAM (2009)Google Scholar
  13. 13.
    Rohit, S.: Association rule mining algorithms: survey. Int. Res. J. Eng. Technol. 3(10), 500–505 (2016)Google Scholar
  14. 14.
    Rathod, R.R., Garg, R.D.: Regional electricity consumption analysis for consumers using data mining techniques and consumer meter reading data. Int. J. Electr. Power Energy Syst. 78, 368–374 (2016)CrossRefGoogle Scholar
  15. 15.
    Addi, A.M., Tarik, A., Fatima, G.: Comparative survey of association rule mining algorithms based on multiple-criteria decision analysis approach. In: 2015 3rd International Conference on Control, Engineering & Information Technology, pp. 1–6. IEEE (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zigui Jiang
    • 1
  • Rongheng Lin
    • 1
  • Fangchun Yang
    • 1
  • Zhihan Liu
    • 1
  • Qiqi Zhang
    • 2
  1. 1.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.State Grid Shanghai Municipal Electric Power CompanyShanghaiChina

Personalised recommendations