Estimating Chinese energy-related CO2 emissions by employing a novel discrete grey prediction model

Ding, Song; Xu, Ning; Ye, Jing; Zhou, Weijie; Zhang, Xiaoxiong

doi:10.1016/j.jclepro.2020.120793

Cited by 119 publications

(39 citation statements)

References 40 publications

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“…Additionally, prediction performance between FANGBM(1,1) and OFANGBM(1,1) is compared by using MAPE. The formulation of the APE and MAPE is given by the following equation ( Ding et al., 2020 ):

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Section: Methodsmentioning

confidence: 99%

Future of renewable energy consumption in France, Germany, Italy, Spain, Turkey and UK by 2030 using optimized fractional nonlinear grey Bernoulli model

Şahin

2021

Sustainable Production and Consumption

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In this study, gross final energy consumption (GFEC), energy consumption of renewable energy sources (RES) and its share in France, Germany, Italy, Spain, Turkey and the United Kingdom (UK) are forecasted by 2030. A novel model is proposed in this study which is called optimized fractional nonlinear grey Bernoulli model, briefly as OFANGBM(1,1). In this model, three parameters, which are background value λ , power index value γ and fractional order value r , are optimized by genetic algorithm (GA) method. Results of OFANGBM(1,1) show that GFEC in France, Germany, Italy, Spain, Turkey and UK will reach to 151.7 Mtoe, 227.6 Mtoe, 110.8 Mtoe, 84.5 Mtoe, 173.4 Mtoe and 132.2 Mtoe, respectively, in 2030. Additionally, energy consumption from RES in France, Germany, Italy, Spain, Turkey and UK is forecasted as 28.5 Mtoe, 53.8 Mtoe, 22.2 Mtoe, 23.2 Mtoe, 26.1 Mtoe and 39.3 Mtoe, respectively, for the year 2030. Results of this study are compared with the national target of these countries on the share of RES in GFEC. Moreover, it is estimated that RES can satisfy 18.8%, 23.6%, 20.0%, 27.5%, 15.1% and 29.7% of GFEC in France, Germany, Italy, Spain, Turkey and UK in 2030, respectively.

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…”

Section: Methodsmentioning

confidence: 99%

Future of renewable energy consumption in France, Germany, Italy, Spain, Turkey and UK by 2030 using optimized fractional nonlinear grey Bernoulli model

Şahin

2021

Sustainable Production and Consumption

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“…As a modeling method, grey system theory has been widely employed in research fields such as energy [ 26 ], transportation [ 27 ], economy [ 28 ], electricity [ 29 ], population [ 30 ], etc. Grey prediction is a basic and the most important part of grey system theory.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction on transaction amounts of China’s CBEC with improved GM (1, 1) models based on the principle of new information priority

Wang

Xiao

2020

Electron Commer Res

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Benefited by e-commerce activities and information technology development, cross-border e-commerce (CBEC) has experienced rapid growth and attracted much research attention. This study takes China’s CBEC as a typical research object and intends to forecast its future development trend based on an exploration of its dynamic changing rules as a whole. The data set of transaction amounts of China’s CBEC from 2008 to 2018 was used in the modeling processes of improved grey models (GM) (1,1) proposed in this study, after which forecast results on the development of China’s CBEC from 2019 to 2020 were achieved. The experimental results reveal that, introducing the principle of new information priority to the improvement of grey models indeed works when forecasting a newly-emerging and vulnerable system like CBEC. Finally, it is predicted that China’s CBEC promises to continue to grow in the near future.

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“…Wang and Ye (2017) presented a forecasting method based on nonlinear grey multivariable method for predicting Chinese carbon emission from fossil energy use. Furthermore, a discrete grey forecasting model for energy-related CO 2 emissions prediction in China from 2011 to 2015 implemented by (Ding et al 2020). A hybrid intelligent prediction model based on semi-experimental regression approach as well as ANFIS for air pollution forecasting has been applied by (Zeinalnezhad et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Air pollution forecasting application based on deep learning model and optimization algorithm

Heydari

Nezhad

Garcia

et al. 2021

Clean Techn Environ Policy

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Air pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO2 and SO2 by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO2, and SO2 for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO2 and SO2); type (2) includes just lagged values of the output variables (NO2 and SO2). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables. Graphic abstract

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Estimating Chinese energy-related CO2 emissions by employing a novel discrete grey prediction model

Cited by 119 publications

References 40 publications

Future of renewable energy consumption in France, Germany, Italy, Spain, Turkey and UK by 2030 using optimized fractional nonlinear grey Bernoulli model

Future of renewable energy consumption in France, Germany, Italy, Spain, Turkey and UK by 2030 using optimized fractional nonlinear grey Bernoulli model

Prediction on transaction amounts of China’s CBEC with improved GM (1, 1) models based on the principle of new information priority

Air pollution forecasting application based on deep learning model and optimization algorithm

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