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A novel grey prediction model with a feedforward neural network based on a carbon emission dynamic evolution system and its application.

A novel grey prediction model with a feedforward neural network based on a carbon emission dynamic evolution system and its application.

Authors :
Nie W
Ao O
Duan H
Source :
Environmental science and pollution research international [Environ Sci Pollut Res Int] 2023 Feb; Vol. 30 (8), pp. 20704-20720. Date of Electronic Publication: 2022 Oct 18.
Publication Year :
2023

Abstract

The objective and accurate prediction of carbon dioxide emissions holds great significance for improving governmental energy policies and plans. Therefore, starting from an evolutionary system of carbon emissions, this paper studies the evolution of the system, establishes a grey model of the system, and expands the modeling structure of this model. The modeling mechanism of the classical feedforward neural network model is organically combined with the function of the external influencing factors of carbon emissions, and the grey model of the carbon emission dynamic system is established with a neural network. Then, the properties of the model are studied, the parameters of the model are optimized, and the modeling steps are obtained. Finally, the validity of the model is analyzed by using the carbon emissions of Beijing from 2009 to 2018. The results of the four cases show that the simulation and prediction errors of the new model are all less than 10%, and case 1 shows the best results of 1.56% and 2.07%, respectively, which are used to predict the carbon dioxide emissions in the next 5 years in Beijing. The prediction results are in accordance with the actual trend, which indicates the effectiveness and feasibility of the model.<br /> (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Details

Language :
English
ISSN :
1614-7499
Volume :
30
Issue :
8
Database :
MEDLINE
Journal :
Environmental science and pollution research international
Publication Type :
Academic Journal
Accession number :
36253576
Full Text :
https://doi.org/10.1007/s11356-022-23541-4