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• W4205980158 abstract "Carbon price is one of the core factors in the carbon trading process, so it is of great significance to establish a carbon price prediction model for guiding the carbon trading market to make scientific and reasonable decision. In order to improve the accuracy of carbon price prediction, a carbon price combination prediction model with variational mode decomposition based on seagull optimization algorithm (SVMD), fluctuation-based dispersion entropy (FDE), extreme learning machine (ELM) and kernel extreme learning machine (KELM) optimized by JAYA algorithm (JAYA-KELM), named SVMD-FDE-ELM-JAYA-KELM, is proposed. To solve the selection of mode number K and the penalty factor α of VMD, SVMD is proposed, which realizes adaptive determination of the parameter K and α. The idea of the SVMD-FDE-ELM-JAYA-KELM is roughly as follows. Firstly, SVMD decomposes carbon price into a series of intrinsic mode functions (IMFs). According to the FDE value, these IMFs are divided into high frequency IMFs or low frequency IMFs. Secondly, JAYA-KELM is established for high frequency IMFs and ELM is established for low frequency IMFs. Finally, the predicted value of carbon price is reconstructed to complete the prediction. The experimental results show that the proposed model has best prediction accuracy, and provides a new method for carbon price prediction." @default.
• W4205980158 created "2022-01-26" @default.
• W4205980158 creator A5019033238 @default.
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• W4205980158 date "2022-11-01" @default.
• W4205980158 modified "2023-10-05" @default.
• W4205980158 title "Carbon price combination prediction model based on improved variational mode decomposition" @default.
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• W4205980158 doi "https://doi.org/10.1016/j.egyr.2021.11.270" @default.
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