Real-Time Electricity Pricing Trend Forecasting Based on Multi-density Clustering and Sequence Pattern Mining

  • Tie Hua Zhou
  • Cong Hui Sun
  • Ling Wang
  • Gong Liang Hu
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 109)


The implementation of real-time electricity price has become an essential point in the electricity market reform. It reflects the balance between the real-time market price and the electricity price. However, due to the non-linear, non-stationary, time variant and other uncertainties factors in power market, prediction accuracy is difficult to guarantee. Therefore, we proposed a Multi-density Clustering (MD Clustering) algorithm use different radius to classify the electricity price data, and automatically generated multi-levels clusters by different price ranges. Then, we forecast the trend of electricity price based on the association analysis and pattern recognition of different level catagories. The experimental results show that our MD clustering algorithm has fast performance and high accuracy in dealing with the data of density attributes nonuniformity condition, and ensure the accuracy of real-time electricity price forecasting.


Real-time electricity price Trend forecasting Multi-density clustering Sequence pattern mining 



This work was supported by the National Natural Science Foundation of China (No. 61701104), and by the Science Research of Education Department of Jilin Province (No. JJKH20170108KJ).


  1. 1.
    Ding, Y., Pineda, S., Nyeng, P., Stergaard, J., Larsen, E.M., Wu, Q.: Real-time market concept architecture for EcoGrid EU—a prototype for European smart grids. IEEE Trans. Smart Grid 4(4), 2006–2016 (2013)Google Scholar
  2. 2.
    Khani, H., Zadeh, M.R.D.: Online adaptive real-time optimal dispatch of privately owned energy storage systems using public-domain electricity market prices. IEEE Trans. Power Syst. 30(2), 930–938 (2013)Google Scholar
  3. 3.
    Weron, R.: Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecast. 30(4), 1030–1081 (2014)Google Scholar
  4. 4.
    Ji, Y., Thomas, R.J., Tong, L.: Probabilistic forecasting of real-time LMP and network congestion. IEEE Trans. Power Syst. 32(2), 831–841 (2017)Google Scholar
  5. 5.
    Philippe, F.V., Jerry, C.W.L., Rage, U.K., Yun, S.K., Rincy, T.: A survey of sequential pattern mining. Data Sci. Pattern Recognit. 1(1), 54–77 (2017)Google Scholar
  6. 6.
    Ding, R., Wang, Q., Dang, Y., Fu, Q., Zhang, H., Zhang, D.: Yading: fast clustering of large-scale time series data. In: VLDB Endowment, pp. 473–484 (2015)Google Scholar
  7. 7.
    Lavin, A., Klabjan, D.: Clustering time-series energy data from smart meters. Energ. Eff. 8(4), 681–689 (2015)Google Scholar
  8. 8.
    Hossain, M.A., Jia, X., Pickering, M.: Subspace detection using a mutual information measure for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 11(2), 424–428 (2014)Google Scholar
  9. 9.
    Yoon, J.H., Baldick, R., Novoselac, A.: Dynamic demand response controller based on real-time retail price for residential buildings. IEEE Trans. Smart Grid 5(1), 121–129 (2104)Google Scholar
  10. 10.
    Wang, H., Cai, Y., Yang, Y., Zhang, S., Mamoulis, N.: Durable queries over historical time series. IEEE Trans. Knowl. Data Eng. 26(3), 595–607 (2014)Google Scholar
  11. 11.
    Feijoo, F., Silva, W., Das, T.K.: A computationally efficient electricity price forecasting model for real time energy markets. Energy Convers. Manag. 113, 27–35 (2014)Google Scholar
  12. 12.
    Sotiriadis, M.S., Tsotsos, R., Kosmidou, K.: Price and volatility interrelationships in the wholesale spot electricity markets of the Central-Western European and Nordic region: a multivariate GARCH approach. Energy Syst. 7(1), 5–32 (2016)Google Scholar
  13. 13.
    Wang, F., Liao, G.P., Li, J.H., Li, X.C., Zhou, T.J.: Multifractal detrended fluctuation analysis for clustering structures of electricity price periods. Phys. A Stat. Mech. Appl. 392(22), 5723–5734 (2016)Google Scholar
  14. 14.
    Rana, S., Jasola, S., Kumar, R.: A boundary restricted adaptive particle swarm optimization for data clustering. Int. J. Mach. Learn. Cybern. 4(4), 391–400 (2013)Google Scholar
  15. 15.
    Lines, J., Bagnall, A.: Time series classification with ensembles of elastic distance measures. Data Min. Knowl. Disc. 29(3), 565–592 (2015)MathSciNetGoogle Scholar
  16. 16.
    Krishnasamy, G., Kulkarni, A.J., Paramesran, R.: A hybrid approach for data clustering based on modified cohort intelligence and K-means. Expert Syst. Appl. 41(13), 6009–6016 (2015)Google Scholar
  17. 17.
    Zhou, D.P., Roozbehani, M., Dahleh, M.A., Tomlin, C.J.: Stability analysis of wholesale electricity markets under dynamic consumption models and real-time pricing. In: American Control Conference (ACC), pp. 2048–2053 (2017)Google Scholar
  18. 18.
    Petitjean, F., Ketterlin, A., Gançarski, P.: A global averaging method for dynamic time warping, with applications to clustering. Pattern Recogn. 44(3), 678–693 (2011)zbMATHGoogle Scholar
  19. 19.
    Jin, C.H., Pok, G., Park, H.W., Ryu, K.H.: Improved pattern sequence-based forecasting method for electricity load. IEEE Trans. Electr. Electron. Eng. 9(6), 670–674 (2014)Google Scholar
  20. 20.
    Hong, T.P., Wu, J.M.T., Li, Y.K., Chen, C.H.: Generalizing concept-drift patterns for fuzzy association rules. J. Netw. Intell. 3(2), 126–137 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tie Hua Zhou
    • 1
  • Cong Hui Sun
    • 1
  • Ling Wang
    • 1
  • Gong Liang Hu
    • 1
  1. 1.Department of Computer Science and Technology, School of Computer ScienceNortheast Electric Power UniversityJilinChina

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