Research on prediction model of carbon trading price based on BAS-BP neural network: take Shenzhen, Guangdong Province, China as an example

Zihan Deng; Yeying Wang; Ling Nian

doi:10.1117/12.2656169

20 October 2022 Research on prediction model of carbon trading price based on BAS-BP neural network: take Shenzhen, Guangdong Province, China as an example

Zihan Deng, Yeying Wang, Ling Nian

Author Affiliations +

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124510U (2022) https://doi.org/10.1117/12.2656169
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

To the relevant policies of the carbon trading market in China, this paper adds the optimization algorithm that longicorn must use based on the traditional BP neural network, which increases the convergence speed and global optimization ability of the original BP neural network. The empirical study takes the carbon trading price of Shenzhen city, Guangdong Province, China, from 2015-to 2022 as an example. The results show that the base-BP neural network can significantly reduce the probability of local convergence in the iterative process and reduce the mean square error of prediction by 3.08% compared with the traditional BP neural network.

Citation Download Citation

Zihan Deng, Yeying Wang, and Ling Nian "Research on prediction model of carbon trading price based on BAS-BP neural network: take Shenzhen, Guangdong Province, China as an example", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124510U (20 October 2022); https://doi.org/10.1117/12.2656169

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