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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)

Abstract

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.

Keywords

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

Notes

Acknowledgments

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).

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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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