Application of Neural Networks on Carbon Emission Prediction: A Systematic Review and Comparison

Wentao Feng, Tailong Chen, Longsheng Li, Le Zhang, Bingyan Deng, Wei Liu, Jian Li and Dongsheng Cai ()
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Wentao Feng: State Grid Sichuan Information & Telecommunication Company, Chengdu 610095, China
Tailong Chen: State Grid Sichuan Information & Telecommunication Company, Chengdu 610095, China
Longsheng Li: State Grid Sichuan Information & Telecommunication Company, Chengdu 610095, China
Le Zhang: State Grid Sichuan Information & Telecommunication Company, Chengdu 610095, China
Bingyan Deng: State Grid Sichuan Information & Telecommunication Company, Chengdu 610095, China
Wei Liu: Sichuan Provincial Key Laboratory of Power System Wide-Area Measurement and Control, University of Electronic Science and Technology of China, Chengdu 611731, China
Jian Li: Sichuan Provincial Key Laboratory of Power System Wide-Area Measurement and Control, University of Electronic Science and Technology of China, Chengdu 611731, China
Dongsheng Cai: Sichuan Provincial Key Laboratory of Power System Wide-Area Measurement and Control, University of Electronic Science and Technology of China, Chengdu 611731, China

Energies, 2024, vol. 17, issue 7, 1-15

Abstract: The greenhouse effect formed by the massive emission of carbon dioxide has caused serious harm to the Earth’s environment, in which the power sector constitutes one of the primary contributors to global greenhouse gas emissions. Reducing carbon emissions from electricity plays a pivotal role in minimizing greenhouse gas emissions and mitigating the ecological, economic, and social impacts of climate change, while carbon emission prediction provides a valuable point of reference for the formulation of policies to reduce carbon emissions from electricity. The article provides a detailed review of research results on deep learning-based carbon emission prediction. Firstly, the main neural networks applied in the domain of carbon emission forecasting at home and abroad, as well as the models combining other methods and neural networks, are introduced, and the main roles of different methods, when combined with neural networks, are discussed. Secondly, neural networks were used to predict electricity carbon emissions, and the performance of different models on carbon emissions was compared. Finally, the application of neural networks in the realm of the prediction of carbon emissions is summarized, and future research directions are discussed. The article provides a reference for researchers to understand the research dynamics and development trend of deep learning in the realm of electricity carbon emission forecasting.

Keywords: carbon emission prediction; BP neural network; recurrent neural network; deep learning; hybrid models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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