Advertisement

Operating Performance Assessment of Smart Meters Based on Bayesian Networks and Convex Evidence Theory

  • Haibo Yu
  • Helong Li
  • Zehao Zheng
  • Yungang ZhuEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1084)

Abstract

Smart electricity meters have been widely installed in recent years. In order to automatically assess the operating performance of smart meters, in this paper we propose a method based on Bayesian network. Bayesian network is adopted to represent the casual relationship among attributes and operating performance of smart meter. Multiple Bayesian networks are trained from data with genetic algorithm and bagging sampling, then a subset of Bayesian networks are selected for assessment. The evidence theory is used to fuse the multiple results from Bayesian networks and generate final assessment results for smart meters. The experimental results show the effectiveness and efficiency of the proposed method.

Keywords

Smart electricity meters Bayesian network Convex evidence theory 

Notes

Acknowledgments

This paper is supported by the State Grid Corporation of Science and Technology Project “The Research and Application of Smart Meter Operation Status Evaluation Technology Based on Multi-source Data Fusion (Project No. JL71-16-006)”.

References

  1. 1.
    Ju, H., Yuan, R., Ding, H., Tian, H., Zhong, W., Pang, F., Xu, S., Li, S.: Study on the whole life cycle quality assessment method of smart meter. Electr. Meas. Instrum. 52(S1), 55–58 (2015)Google Scholar
  2. 2.
    Zhou, F., Cheng, Y., Xiao, W., Jin, Z.: Method and application of electric meter status assessment based on integrated security domain. J. Autom. Instrum. 07, 29–33 (2016)Google Scholar
  3. 3.
    Chang, Q., Yan, X., Tao, X., Fu, F.: Research on operating status analysis system for smart meters based on big data technology. Autom. Instrum. (12), 4–6 (2015)Google Scholar
  4. 4.
    He, Y., Zheng, J.-J., Zhu, L.: An entropy-based algorithm for discretization of continuous variables. Comput. Appl. 25(3), 637–639 (2005)Google Scholar
  5. 5.
    Zhu, Y., Liu, D., Jia, H., Trinugroho, D.: Incremental learning of Bayesian networks based on chaotic dual-population evolution strategies and its application to nanoelectronics. J. Nanoelectronics Optoelectron. 7(2), 113–118 (2012)CrossRefGoogle Scholar
  6. 6.
    Liu, D., Wang, F., Yinan, L., Xue, W., Wang, S.: Structure learning of Bayesian network based on genetic algorithm. Comput. Res. Dev. 38(8), 916–922 (2001)Google Scholar
  7. 7.
    Zhu, Y., Liu, D., Chen, G., Jia, H., Yu, H.: Mathematical modeling for active and dynamic diagnosis of crop diseases based on Bayesian networks and incremental learning. Math. Comput. Model. 58(3–4), 514–523 (2013)CrossRefGoogle Scholar
  8. 8.
    Liu, D., Yang, B., Zhu, Y., Sun, C.: Fundamental Theories and Methods for Processing Uncertain Knowledge. Science Press (2016)Google Scholar
  9. 9.
    Zhu, Y., Liu, D., Li, Y., Wang, X.: Selective and incremental fusion for fuzzy and uncertain data based on probabilistic graphical model. J. Intell. Fuzzy Syst. 29(6), 2397–2403 (2015)CrossRefGoogle Scholar
  10. 10.
    Liu, D., Zhu, Y., Ni, N., Liu, J.: Ordered proposition fusion based on consistency and uncertainty measurements. Sci. China Inf. Sci. 60(8), 1–19 (2017). 082103Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.China Electric Power Research InstituteBeijingChina
  2. 2.College of Computer Science and TechnologyJilin UniversityChangchunChina

Personalised recommendations