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Electricity price forecasts using a Curvelet denoising based approach

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  • He, Kaijian
  • Xu, Yang
  • Zou, Yingchao
  • Tang, Ling

Abstract

Price movement in the electricity market can be viewed as a nonlinear and dynamic system, exhibiting significant chaotic and multiscale characteristics. To conduct more accurate analysis and forecasting, this paper proposes a new Curvelet denoising based algorithm to analyze these characteristics and predict its future movement. We project the original electricity price into its time delay embedding domain to reveal its chaotic characteristics. The Curvelet denoising method is introduced to separate and suppress the noise disruptions in the transformed phase space. Empirical studies using the typical Australian electricity market prices data show that the proposed algorithm demonstrates more robust and superior performance than the traditional benchmark models.

Suggested Citation

  • He, Kaijian & Xu, Yang & Zou, Yingchao & Tang, Ling, 2015. "Electricity price forecasts using a Curvelet denoising based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 425(C), pages 1-9.
    • Handle: RePEc:eee:phsmap:v:425:y:2015:i:c:p:1-9
    DOI: 10.1016/j.physa.2015.01.012
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    Cited by:

    1. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2018. "Forecasting exchange rate using Variational Mode Decomposition and entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 15-25.
    2. Ping Jiang & Feng Liu & Yiliao Song, 2016. "A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection," Energies, MDPI, vol. 9(8), pages 1-27, August.
    3. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
    4. Bartoš, Erik & Pinčák, Richard, 2017. "Identification of market trends with string and D2-brane maps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 57-70.
    5. Zou, Yingchao & Yu, Lean & Tso, Geoffrey K.F. & He, Kaijian, 2020. "Risk forecasting in the crude oil market: A multiscale Convolutional Neural Network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).

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