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A new carbon price prediction model

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  • Li, Guohui
  • Ning, Zhiyuan
  • Yang, Hong
  • Gao, Lipeng

Abstract

The excessive emission of carbon is one of the important factors causing environmental pollution, and the prediction of carbon trading market price is an important mean of emission reduction. In order to accurately predict the carbon price, a new carbon price prediction model is proposed in this paper. Firstly, the data is decomposed into multiple intrinsic mode functions (IMFs) by optimized variational mode decomposition (OVMD). Secondly, the complexity of IMFs is analyzed by spatial-dependence recurrence sample entropy (SdrSampEn). Thirdly, the IMFs with higher complexity are integrated and decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to get high complexity IMFs. Then, particle swarm optimized extreme learning machine (PSOELM) is used to predict the high complexity IMFs, and extreme learning machine (ELM) is used to predict other. Finally, the predicted value is reconstructed to complete the prediction. In this paper, OVMD is proposed to solve the selection of decomposition layers K by variational mode decomposition (VMD) from the perspective of variance contribution rate. Through the experimental results, the effectiveness of the proposed model is verified, and it can be used to predict the supply and demand of carbon market and evaluate the effectiveness of current carbon trading policies.

Suggested Citation

  • Li, Guohui & Ning, Zhiyuan & Yang, Hong & Gao, Lipeng, 2022. "A new carbon price prediction model," Energy, Elsevier, vol. 239(PD).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pd:s036054422102572x
    DOI: 10.1016/j.energy.2021.122324
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    7. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).
    8. Qin, Chaoyong & Qin, Dongling & Jiang, Qiuxian & Zhu, Bangzhu, 2024. "Forecasting carbon price with attention mechanism and bidirectional long short-term memory network," Energy, Elsevier, vol. 299(C).
    9. Xing, Zhuoqun & Pan, Yiqun & Yang, Yiting & Yuan, Xiaolei & Liang, Yumin & Huang, Zhizhong, 2024. "Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction," Applied Energy, Elsevier, vol. 365(C).
    10. Dan Wang & Juheng Yang, 2022. "Carbon Neutrality Strategies for Chinese International Oil Company Based on the Rapid Development of Global Carbon Market," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
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