CO2 emission prediction based on carbon verification data of 17 thermal power enterprises in Gansu Province

Shi, Wei; Yang, Jiapeng; Qiao, Fuwei; Wang, Chengyuan; Dong, Bowen; Zhang, Xiaolong; Zhao, Sixue; Wang, Weijuan

doi:10.1007/s11356-023-31391-x

Cited by 1 publication

(1 citation statement)

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“…After processing the data, the training rate and prediction precision of the model are improved. There are also some combinations of algorithms, such as Nonlinear PSO [27], IPSO [28], Genetic Algorithm [29], and BP neural network. The principal objective of these algorithms is to optimize the parameters of the neural network model to improve its performance.…”

Section: Prediction Methods Based On Bp Neural Networkmentioning

confidence: 99%

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

Feng,

Chen,

et al. 2024

Energies

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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.

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Section: Prediction Methods Based On Bp Neural Networkmentioning

confidence: 99%