A combined prediction model based on secondary decomposition and intelligence optimization for carbon emission
•An improved singular spectrum decomposition is proposed.•High complexity components are secondarily decomposed to reduce its complexity.•An improved prediction model of optimization algorithm is proposed.•Intelligent weighting strategy is introduced to overcome traditional weighting problem.•A comb...
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| Vydáno v: | Applied mathematical modelling Ročník 121; s. 484 - 505 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Inc
01.09.2023
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| ISSN: | 0307-904X |
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| Abstract | •An improved singular spectrum decomposition is proposed.•High complexity components are secondarily decomposed to reduce its complexity.•An improved prediction model of optimization algorithm is proposed.•Intelligent weighting strategy is introduced to overcome traditional weighting problem.•A combined prediction model for carbon emission is proposed.
Accurate prediction of carbon emission is critical for the development of low-carbon economy. However, most carbon emission prediction studies use a single model with low prediction accuracy, and do not consider the instability of carbon emission. Therefore, this paper proposes a combined prediction model of carbon emission. Firstly, the original data is decomposed by singular spectrum decomposition to obtain a limited amount of singular spectrum components. Secondly, high complexity components are secondarily decomposed by variational mode decomposition. Then, chameleon swarm algorithm and carnivorous plant algorithm are used to train the regularization coefficients and kernel parameters of kernel extreme learning machine and least squares support vector machine respectively, and the trained model is used to predict the decomposition components. Finally, induced ordered weighted averaging operator is used to calculate the weight of single model, and error correction is introduced to further promote the prediction accuracy. The carbon emission data of China and the United States is used to make a prediction experiment. The results indicate that the proposed model is superior to other comparative models in different indexes, which provides a new idea for carbon emission prediction. |
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| AbstractList | •An improved singular spectrum decomposition is proposed.•High complexity components are secondarily decomposed to reduce its complexity.•An improved prediction model of optimization algorithm is proposed.•Intelligent weighting strategy is introduced to overcome traditional weighting problem.•A combined prediction model for carbon emission is proposed.
Accurate prediction of carbon emission is critical for the development of low-carbon economy. However, most carbon emission prediction studies use a single model with low prediction accuracy, and do not consider the instability of carbon emission. Therefore, this paper proposes a combined prediction model of carbon emission. Firstly, the original data is decomposed by singular spectrum decomposition to obtain a limited amount of singular spectrum components. Secondly, high complexity components are secondarily decomposed by variational mode decomposition. Then, chameleon swarm algorithm and carnivorous plant algorithm are used to train the regularization coefficients and kernel parameters of kernel extreme learning machine and least squares support vector machine respectively, and the trained model is used to predict the decomposition components. Finally, induced ordered weighted averaging operator is used to calculate the weight of single model, and error correction is introduced to further promote the prediction accuracy. The carbon emission data of China and the United States is used to make a prediction experiment. The results indicate that the proposed model is superior to other comparative models in different indexes, which provides a new idea for carbon emission prediction. |
| Author | Yang, Hong Wang, Maozhu Li, Guohui |
| Author_xml | – sequence: 1 givenname: Hong surname: Yang fullname: Yang, Hong email: uestcyhong@163.com – sequence: 2 givenname: Maozhu surname: Wang fullname: Wang, Maozhu – sequence: 3 givenname: Guohui surname: Li fullname: Li, Guohui |
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