Academic Journal of Environment & Earth Science, 2022, 4(2); doi: 10.25236/AJEE.2022.040202.
Haidong Zhu1, Chi Wen2, Weilin Xu3, Rong Luo4
1School of Hydraulic and Electric Power, Heilongjiang University, Harbin, 150000, China
2Mathematics and Inofrmation Technology Department, The Education University of Hong Kong, Hong Kong, 999077, China
3College of Business and Economics, Chung-Ang University, Seoul, 06974, South Korea
4Rocket Force University of Engineering, Xi’an, 710025, China
To address the problem that the traditional BP(Back-Propagation) neural network is prone to fall into the local optimal situation for carbon emission prediction, and thus the prediction results have large errors, this paper proposes to improve the BP neural network by using particle swarm optimization algorithm, and conducts simulation experiments with national carbon emission data. The results show that the model has a 12.1% reduction in error compared with the traditional BP neural network model, and has a better prediction effect on carbon emissions.
Carbon Emissions, Particle Swarm Optimization Algorithm, BP Neural Network
Haidong Zhu, Chi Wen, Weilin Xu, Rong Luo. A Study on Carbon Emission Forecasting in China Based on PSO-BP Neural Network. Academic Journal of Environment & Earth Science (2022) Vol. 4 Issue 2: 5-9. https://doi.org/10.25236/AJEE.2022.040202.
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