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A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

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  • Jianguo Zhou

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

  • Shiguo Wang

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

Abstract

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

Suggested Citation

  • Jianguo Zhou & Shiguo Wang, 2021. "A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors," Energies, MDPI, vol. 14(5), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1328-:d:508176
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    References listed on IDEAS

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    Cited by:

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    10. Beibei Hu & Yunhe Cheng, 2023. "Prediction of Regional Carbon Price in China Based on Secondary Decomposition and Nonlinear Error Correction," Energies, MDPI, vol. 16(11), pages 1-22, May.

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