Carbon price combination prediction model based on improved variational mode decomposition
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 p...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484721014177 |
| _version_ | 1797902065820762112 |
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| author | Guohui Li Caifeng Zheng Hong Yang |
| author_facet | Guohui Li Caifeng Zheng Hong Yang |
| author_sort | Guohui Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T09:11:54Z |
| format | Article |
| id | doaj.art-35724873abcf44ef95699af5210051a9 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T09:11:54Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-35724873abcf44ef95699af5210051a92023-02-21T05:09:38ZengElsevierEnergy Reports2352-48472022-11-01816441664Carbon price combination prediction model based on improved variational mode decompositionGuohui Li0Caifeng Zheng1Hong Yang2Corresponding authors.; School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, ChinaSchool of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, ChinaCorresponding authors.; School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, ChinaCarbon 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.http://www.sciencedirect.com/science/article/pii/S2352484721014177Carbon price predictionMode decompositionFluctuation-based dispersion entropyExtreme learning machineKernel extreme learning machine |
| spellingShingle | Guohui Li Caifeng Zheng Hong Yang Carbon price combination prediction model based on improved variational mode decomposition Energy Reports Carbon price prediction Mode decomposition Fluctuation-based dispersion entropy Extreme learning machine Kernel extreme learning machine |
| title | Carbon price combination prediction model based on improved variational mode decomposition |
| title_full | Carbon price combination prediction model based on improved variational mode decomposition |
| title_fullStr | Carbon price combination prediction model based on improved variational mode decomposition |
| title_full_unstemmed | Carbon price combination prediction model based on improved variational mode decomposition |
| title_short | Carbon price combination prediction model based on improved variational mode decomposition |
| title_sort | carbon price combination prediction model based on improved variational mode decomposition |
| topic | Carbon price prediction Mode decomposition Fluctuation-based dispersion entropy Extreme learning machine Kernel extreme learning machine |
| url | http://www.sciencedirect.com/science/article/pii/S2352484721014177 |
| work_keys_str_mv | AT guohuili carbonpricecombinationpredictionmodelbasedonimprovedvariationalmodedecomposition AT caifengzheng carbonpricecombinationpredictionmodelbasedonimprovedvariationalmodedecomposition AT hongyang carbonpricecombinationpredictionmodelbasedonimprovedvariationalmodedecomposition |