A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning Article Swipe
Yuhong Zhao
,
Ruirui Liu
,
Zhansheng Liu
,
Liang Liu
,
Jingjing Wang
,
Wenxiang Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su15086876
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su15086876
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.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3390/su15086876
- https://www.mdpi.com/2071-1050/15/8/6876/pdf?version=1681901800
- OA Status
- gold
- Cited By
- 51
- References
- 76
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366598224
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4366598224Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/su15086876Digital Object Identifier
- Title
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A Review of Macroscopic Carbon Emission Prediction Model Based on Machine LearningWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-19Full publication date if available
- Authors
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Yuhong Zhao, Ruirui Liu, Zhansheng Liu, Liang Liu, Jingjing Wang, Wenxiang LiuList of authors in order
- Landing page
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https://doi.org/10.3390/su15086876Publisher landing page
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https://www.mdpi.com/2071-1050/15/8/6876/pdf?version=1681901800Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2071-1050/15/8/6876/pdf?version=1681901800Direct OA link when available
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Machine learning, Artificial intelligence, Artificial neural network, Extreme learning machine, Support vector machine, Computer science, Random forest, Carbon fibers, Algorithm, Composite numberTop concepts (fields/topics) attached by OpenAlex
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51Total citation count in OpenAlex
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2025: 18, 2024: 23, 2023: 10Per-year citation counts (last 5 years)
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76Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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