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作者:

Zhao, Yuhong (Zhao, Yuhong.) | Liu, Ruirui (Liu, Ruirui.) | Liu, Zhansheng (Liu, Zhansheng.) | Liu, Liang (Liu, Liang.) | Wang, Jingjing (Wang, Jingjing.) | Liu, Wenxiang (Liu, Wenxiang.)

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SSCI Scopus SCIE

摘要:

Under the background of global warming and the energy crisis, the Chinese government has set the goal of carbon peaking and carbon neutralization. With the rapid development of machine learning, some advanced machine learning algorithms have also been applied to the control and prediction of carbon emissions due to their high efficiency and accuracy. In this paper, the current situation of machine learning applied to carbon emission prediction is studied in detail by means of paper retrieval. It was found that machine learning has become a hot topic in the field of carbon emission prediction models, and the main carbon emission prediction models are mainly based on back propagation neural networks, support vector machines, long short-term memory neural networks, random forests and extreme learning machines. By describing the characteristics of these five types of carbon emission prediction models and conducting a comparative analysis, we determined the applicable characteristics of each model, and based on this, future research ideas for carbon emission prediction models based on machine learning are proposed.

关键词:

macroscopic carbon emission machine learning prediction model

作者机构:

  • [ 1 ] [Zhao, Yuhong]Beijing Univ Technol, Fac Architecture Civil & Transportat Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Liu, Ruirui]Beijing Univ Technol, Fac Architecture Civil & Transportat Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Liu, Zhansheng]Beijing Univ Technol, Fac Architecture Civil & Transportat Engn, Beijing 100124, Peoples R China
  • [ 4 ] [Liu, Liang]Beijing Univ Technol, Fac Architecture Civil & Transportat Engn, Beijing 100124, Peoples R China
  • [ 5 ] [Wang, Jingjing]Beijing Univ Technol, Fac Architecture Civil & Transportat Engn, Beijing 100124, Peoples R China
  • [ 6 ] [Liu, Wenxiang]Beijing Univ Technol, Fac Architecture Civil & Transportat Engn, Beijing 100124, Peoples R China
  • [ 7 ] [Zhao, Yuhong]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing 100124, Peoples R China
  • [ 8 ] [Liu, Ruirui]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing 100124, Peoples R China
  • [ 9 ] [Liu, Zhansheng]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing 100124, Peoples R China
  • [ 10 ] [Liu, Liang]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing 100124, Peoples R China
  • [ 11 ] [Wang, Jingjing]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing 100124, Peoples R China
  • [ 12 ] [Liu, Wenxiang]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing 100124, Peoples R China

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来源 :

SUSTAINABILITY

年份: 2023

期: 8

卷: 15

3 . 9 0 0

JCR@2022

ESI学科: ENVIRONMENT/ECOLOGY;

ESI高被引阀值:17

被引次数:

WoS核心集被引频次: 26

SCOPUS被引频次: 37

ESI高被引论文在榜: 0 展开所有

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中文被引频次:

近30日浏览量: 1

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