Forecasting carbon emissions using a multi-variable GM (1,N) model based on linear time-varying parameters

Xiong, Pingping; Xiao, Lushuang; Liu, Yuchun; Yang, Zhuo; Zhou, Yifan; Cao, Shuren

doi:10.3233/jifs-202711

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…Scholars have carried out the improvement GS 14,1 work of the grey forecasting model from various angles. The current improvements for GM(1,N) model are mainly background value optimization (Hsu, 2009;Guo et al, 2013;Huang, 2009), consideration of time-lag features (Zhang et al, 2015;Xiong et al, 2021;Ofosu-Adarkwa and Xie, 2023), and nonlinear features (Zhou and Fang, 2010;Ding et al, 2018a;Wang and Ye, 2017). Among them, parameter improvement of background value is more straightforward than other methods, and the improvement effect is ideal.…”

Section: Chinese Carbon Emission Intensity Forecastingmentioning

confidence: 99%

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Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model

Wang,

2023

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PurposeThis study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.Design/methodology/approachIn this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.FindingsThe results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.Originality/valueThe paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.

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Section: Chinese Carbon Emission Intensity Forecastingmentioning

confidence: 99%

“…, 2013; Huang, 2009), consideration of time-lag features (Zhang et al. , 2015; Xiong et al. , 2021; Ofosu-Adarkwa and Xie, 2023), and nonlinear features (Zhou and Fang, 2010; Ding et al ., 2018a; Wang and Ye, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model

Wang,

2023

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“…For long-term forecast, the uncertainty of future perturbation factors will make the forecast accuracy lower. Xiong et al 23 developed a new multivariate grey model based on linear time-varying discrete parameters. In this model, a linear time-varying function was introduced into the traditional model to dynamically optimize the fixed parameters that can only be used for static analysis.…”

Section: Introductionmentioning

confidence: 99%

Research on the community electric carbon emission prediction considering the dynamic emission coefficient of power system

Yang

et al. 2023

Sci Rep

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Based on the counted power system emission factors of North China Power Grid, a community carbon emissions sample database is constructed. The support vector regression (SVR) model is trained to forecast the power carbon emissions, which is optimized by genetic algorithm (GA). A community carbon emission warning system is designed according the results. The dynamic emission coefficient curve of the power system is obtained by fitting the annual carbon emission coefficients. The time series SVR carbon emission prediction model is constructed, while the GA is improved to optimize its parameters. Taking Beijing Caochang Community as an example, a carbon emission sample database is generated based on the electricity consumption and emission coefficient curve to train and test the SVR model. The results show that the GA–SVR model fits well with the training set and the testing set, and the prediction accuracy of the testing set reaches 86%. In view of the training model in this paper, the carbon emission trend of community electricity consumption in the next month is predicted. The carbon emission warning system of the community is designed, and the specific strategy of community carbon emission reduction is proposed.

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“…They establish a multi-provincial carbon emissions time series planning prediction model. To solve the problem of many factors affecting carbon emissions, Xiong et al [10] establish a new multi-variable grey model based on linear time-varying discrete parameters. In this model, the linear time-varying function is introduced into the traditional model to dynamically optimize the fixed parameters that can only be used for static analysis.…”

Section: Introductionmentioning

confidence: 99%

Research on the community electric carbon emission prediction considering the dynamic emission coefficient of power system

Yang

et al. 2022

Preprint

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Based on the counted power system emission factors of North China Power Grid(NCPG), a community carbon emissions sample database is constructed. The support vector regression (SVR) model is trained to forecast the power carbon emissions, which is optimized by genetic algorithm(GA). A community carbon emission warning system is designed according the results. The dynamic emission coefficient curve of the power system is obtained by fitting the annual carbon emission coefficients of NCPG. The time series SVR carbon emission prediction model is constructed and the GA are improved to optimize its parameters. Taking Beijing Caochang Community as an example, a carbon emission sample database is generated based on the electricity consumption and emission coefficient curve to training and testing the SVR model. The results show that the GA-SVR model fits well with the training set and the testing set, and the prediction accuracy of the testing set reaches 86%. In view of the training model in this paper, the carbon emission trend of community electricity consumption in the next month is predicted. The carbon emission warning system of the community is designed, and the specific strategy of community carbon emission reduction is proposed.

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Forecasting carbon emissions using a multi-variable GM (1,N) model based on linear time-varying parameters

Cited by 13 publications

References 18 publications

Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model

Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model

Research on the community electric carbon emission prediction considering the dynamic emission coefficient of power system

Research on the community electric carbon emission prediction considering the dynamic emission coefficient of power system

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