Forecasting Chinese CO2 emissions from fuel combustion using a novel grey multivariable model

Ding, Song; Dang, Yaoguo; Li, Xuemei; Wang, Junjie; Zhao, Kai

doi:10.1016/j.jclepro.2017.06.167

Cited by 139 publications

(65 citation statements)

References 34 publications

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“…Therefore, the GM(1,N) model and its optimization models are increasing in researches and applications. Ding et al established a novel GM(1,N) model combined with the changing trend of the driving term, and this model was applied to predict CO 2 emissions from fuel combustion in China [32]. Zeng et al proposed the optimal background-value GM(1,N) model through optimizing the background-value coefficient with the particle swarm optimization algorithm [33].…”

Section: Study Of Grey Prediction Modelmentioning

confidence: 99%

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

et al. 2019

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Haze is the greatest challenge facing China's sustainable development, and it seriously affects China's economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM 10 , SO 2 and NO 2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM 10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.

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Section: Study Of Grey Prediction Modelmentioning

confidence: 99%

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

et al. 2019

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“…Carbon emissions could be regarded as a grey system, as it they are affected by many uncertainty factors. Song Ding et al [20] predicted Chinese carbon emissions using grey models and compared them with non-grey models. The result suggested that grey models were more suitable for carbon prediction.…”

Section: Grey Prediction Modelsmentioning

confidence: 99%

An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

Xie

Zhang

et al. 2018

Energies

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Abstract:In this paper, an improved grey model and scenario analysis, GA-GM(1,N) is proposed to forecast the carbon intensity in the Pearl River Delta (PRD) region, one of the most developed regions in China. Moreover, to show the advantage and feasibility of the proposed model, the forecasting results of the GA-GM(1,N) model are compared with that of a single-variable grey model (GM (1,1)) and a multivariable form (GM(1,N)). Data from one sample period (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) are used to develop the models, and data from another sample period (2013)(2014)(2015) are used to test them. The mean absolute percentage error (MAPE) is applied to measure the accuracy of prediction. The results show that, of the three models, GA-GM(1,N) produces the best carbon intensity forecasts, with MAPEs of 0.4-1.4% and 0.04-0.4% in the development and testing periods respectively. This indicates that the optimization of the genetic algorithm is effective. The realization of carbon reduction targets in different cities is also explored by combining grey models with scenario analysis. Only Guangzhou could achieve its reduction target under all scenarios, and it can serve as a reference for other cities. Policy recommendations are provided based on these results.

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“…The DGMT has been applied to build several effective grey models, such as the NDGM model [18]. And recently it has also been extended to build the multivariate grey models, such as the DGM(1, N) [19], RDGM (1, n) [20], TDVGM(1, N) [21], etc. Thirdly, some other methods, such as the intelligent optimizers [22], kernel machine learning [23], data grouping [24], mega-trend-diffusion [25], have also been introduced to build the grey models.…”

Section: Introductionmentioning

confidence: 99%

The conformable fractional grey system model

et al. 2020

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The fractional order grey models (FGM) have appealed considerable interest of research in recent years due to its higher effectiveness and flexibility than the conventional grey models and other prediction models. However, the definitions of the fractional order accumulation (FOA) and difference (FOD) is computationally complex, which leads to difficulties for the theoretical analysis and applications. In this paper, the new definition of the FOA are proposed based on the definitions of Conformable Fractional Derivative, which is called the Conformable Fractional Accumulation (CFA), along with its inverse operation, the Conformable Fractional Difference (CFD). Then the new Conformable Fractional Grey Model (CFGM) based on CFA and CFD is introduced with detailed modelling procedures. The feasibility and simplicity and the CFGM are shown in the numerical example. And the at last the comprehensive real-world case studies of natural gas production forecasting in 11 countries are presented, and results show that the CFGM is much more effective than the existing FGM model in the 165 subcases.

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Forecasting Chinese CO2 emissions from fuel combustion using a novel grey multivariable model

Cited by 139 publications

References 34 publications

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

The conformable fractional grey system model

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