Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm
Abstract It is essential to predict carbon prices precisely in order to reduce CO2 emissions and mitigate global warming. As a solution to the limitations of a single machine learning model that has insufficient forecasting capability in the carbon price prediction problem, a carbon price prediction...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-45524-2 |
| _version_ | 1797647267851665408 |
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| author | Mengdan Feng Yonghui Duan Xiang Wang Jingyi Zhang Lanlan Ma |
| author_facet | Mengdan Feng Yonghui Duan Xiang Wang Jingyi Zhang Lanlan Ma |
| author_sort | Mengdan Feng |
| collection | DOAJ |
| description | Abstract It is essential to predict carbon prices precisely in order to reduce CO2 emissions and mitigate global warming. As a solution to the limitations of a single machine learning model that has insufficient forecasting capability in the carbon price prediction problem, a carbon price prediction model (GWO–XGBOOST–CEEMDAN) based on the combination of grey wolf optimizer (GWO), extreme gradient boosting (XGBOOST), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is put forward in this paper. First, a random forest (RF) method is employed to screen the primary carbon price indicators and determine the main influencing factors. Second, the GWO–XGBOOST model is established, and the GWO algorithm is utilized to optimize the XGBOOST model parameters. Finally, the residual series of the GWO–XGBOOST model are decomposed and corrected using the CEEMDAN method to produce the GWO–XGBOOST–CEEMDAN model. Three carbon emission trading markets, Guangdong, Hubei, and Fujian, were experimentally predicted to verify the model’s validity. Based on the experimental results, it has been demonstrated that the proposed hybrid model has enhanced prediction precision compared to the comparison model, providing an effective experimental method for the prediction of future carbon prices. |
| first_indexed | 2024-03-11T15:14:50Z |
| format | Article |
| id | doaj.art-7cffd4c8eb6a4ae8b0769771021e9a98 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-11T15:14:50Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7cffd4c8eb6a4ae8b0769771021e9a982023-10-29T12:24:27ZengNature PortfolioScientific Reports2045-23222023-10-0113112310.1038/s41598-023-45524-2Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithmMengdan Feng0Yonghui Duan1Xiang Wang2Jingyi Zhang3Lanlan Ma4Department of Civil Engineering, Henan University of TechnologyDepartment of Civil Engineering, Henan University of TechnologyDepartment of Civil Engineering, Zhengzhou University of AeronauticsDepartment of Civil Engineering, Henan University of TechnologyDepartment of Civil Engineering, Henan University of TechnologyAbstract It is essential to predict carbon prices precisely in order to reduce CO2 emissions and mitigate global warming. As a solution to the limitations of a single machine learning model that has insufficient forecasting capability in the carbon price prediction problem, a carbon price prediction model (GWO–XGBOOST–CEEMDAN) based on the combination of grey wolf optimizer (GWO), extreme gradient boosting (XGBOOST), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is put forward in this paper. First, a random forest (RF) method is employed to screen the primary carbon price indicators and determine the main influencing factors. Second, the GWO–XGBOOST model is established, and the GWO algorithm is utilized to optimize the XGBOOST model parameters. Finally, the residual series of the GWO–XGBOOST model are decomposed and corrected using the CEEMDAN method to produce the GWO–XGBOOST–CEEMDAN model. Three carbon emission trading markets, Guangdong, Hubei, and Fujian, were experimentally predicted to verify the model’s validity. Based on the experimental results, it has been demonstrated that the proposed hybrid model has enhanced prediction precision compared to the comparison model, providing an effective experimental method for the prediction of future carbon prices.https://doi.org/10.1038/s41598-023-45524-2 |
| spellingShingle | Mengdan Feng Yonghui Duan Xiang Wang Jingyi Zhang Lanlan Ma Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm Scientific Reports |
| title | Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm |
| title_full | Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm |
| title_fullStr | Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm |
| title_full_unstemmed | Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm |
| title_short | Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm |
| title_sort | carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm |
| url | https://doi.org/10.1038/s41598-023-45524-2 |
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