A new carbon price prediction model

Guohui Li, Zhiyuan Ning, Hong Yang and Lipeng Gao

Energy, 2022, vol. 239, issue PD

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.

Keywords: Carbon price prediction; Optimized variational mode decomposition; Complete ensemble empirical mode decomposition with adaptive noise; Spatial-dependence recurrence sample entropy; Particle swarm optimized extreme learning machine (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:239:y:2022:i:pd:s036054422102572x

DOI: 10.1016/j.energy.2021.122324

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