DOI: 10.1016/j.scitotenv.2020.137194
论文题名: Forecasting CO2 emissions in Chinas commercial department, through BP neural network based on random forest and PSO
作者: Wen L. ; Yuan X.
刊名: Science of the Total Environment
ISSN: 489697
出版年: 2020
卷: 718 语种: 英语
英文关键词: BP neural network
; China's commercial department
; CO2 emissions prediction
; Particle swarm optimization
; Random forest
Scopus关键词: Carbon dioxide
; Climate change
; Decision trees
; Neural networks
; Particle swarm optimization (PSO)
; Random forests
; BP neural networks
; China's commercial department
; CO2 emissions
; Hybrid forecasting
; Hybrid prediction models
; Performance optimizations
; Prediction accuracy
; Prediction indicators
; Forecasting
; coal
; coke
; diesel fuel
; fuel oil
; gasoline
; kerosene
; natural gas
; petroleum
; accuracy assessment
; artificial neural network
; carbon emission
; commercialization
; forecasting method
; optimization
; panel data
; trend analysis
; Article
; back propagation neural network
; carbon footprint
; China
; commercial phenomena
; controlled study
; forecasting
; intermethod comparison
; mathematical model
; measurement accuracy
; particle swarm optimization
; prediction
; priority journal
; quantitative analysis
; random forest
; validity
; China
英文摘要: In recent years, with the worsening of the global climate problem, the issue of CO2 emissions has gradually attracted people's attention. Accurately predicting CO2 emissions and analyzing its change trends are important elements in addressing climate issues at this stage. Although the predecessors have done a lot of research on CO2 emissions and also established some prediction models, few people have adopted quantifiable methods to select prediction indicators and studied the CO2 emissions of commercial department. So this paper establishes a novel BP neural network prediction model based on the index quantization ability of random forest and the performance optimization ability of PSO. For further strengthening the prediction accuracy, several improvements have been made to PSO. Finally, the validity of the model is tested using panel data from 1997 to 2017 of the Chinese commercial sector. The results as follows: (1) Compared with other parallel models, the newly established hybrid forecasting model can more accurately predict the CO2 emissions of China's commercial department. (2) The prediction indexes selected after quantification based on the random forest can improve the prediction accuracy. (3) These improvements of PSO in this paper can greatly enhance the prediction effect of the hybrid prediction model. © 2020 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158188
Appears in Collections: 气候变化与战略
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作者单位: Department of Economics and Management, North China Electric Power University, Baoding, Hebei, China
Recommended Citation:
Wen L.,Yuan X.. Forecasting CO2 emissions in Chinas commercial department, through BP neural network based on random forest and PSO[J]. Science of the Total Environment,2020-01-01,718