Projections of Turkey's electricity generation and installed capacity from total renewable and hydro energy using fractional nonlinear grey Bernoulli model and its reduced forms

Şahin, Utkucan

doi:10.1016/j.spc.2020.04.004

Cited by 77 publications

(25 citation statements)

References 35 publications

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“…Carbon dioxide is the main greenhouse gas that causes global warming, and the burning of fossil fuels such as coal and petroleum will cause a large amount of carbon dioxide emissions. In the latest research, a large number of scholars have studied various energy issues, such as the industrial solar energy (Wang et al 2020 ), clean energy (Wang 2015 ), coal (Shou et al 2020 ), renewable and hydro energy (Utkucan Sahin 2020 ), and natural gas (Wang and Li 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Forecasting the carbon dioxide emissions in 53 countries and regions using a non-equigap grey model

Liu

2020

Environ Sci Pollut Res

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Non-equigap GM(1,1) model with conformable fractional accumulation (CFNGM(1,1)) is proposed to analyze the relationship between energy consumption and carbon dioxide emissions. Two cases are used to prove the validity of the model. In this article, energy consumption is used as input and carbon dioxide emissions are used as output. Carbon dioxide emissions of 53 countries and regions in North America, South America, Europe, Commonwealth of Independent States (CIS), Middle East, Africa, and Asia Pacific are predicted. The forecast results show that the carbon dioxide emissions of 30 countries and regions have risen to varying degrees. The top three countries with carbon dioxide emissions in the next three years are China, the USA, and India. More attention should be paid to the carbon dioxide emissions of China.

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Section: Introductionmentioning

confidence: 99%

Forecasting the carbon dioxide emissions in 53 countries and regions using a non-equigap grey model

Liu

2020

Environ Sci Pollut Res

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“…The novel model, is called fractional grey prediction model, inspired many researchers due to its superior predictive performance, especially used as a forecasting tool in energy researches. Fractional grey prediction models have been applied to predict China's electricity consumption ( Yang and Xue, 2016 ), China's nuclear energy consumption ( Wu et al., 2018 ), China's crude oil consumption ( Duan et al., 2018 ), China's wind energy consumption ( Zhang et al., 2019 ), China's natural gas and coal consumption ( Ma et al., 2019b ), renewable energy consumption in China ( Wu et al., 2019a ), China's energy consumption ( Wu et al., 2019b ), natural gas consumption of countries ( Ma et al., 2020 ), Turkey's electricity generation and installed capacity ( Şahin, 2020 ) and China's annual electricity consumption ( Xie et al., 2020 ). Recently, Wu et al.…”

Section: Introductionmentioning

confidence: 99%

“…These two parameters characterize the model's ability to adapt to actual data. Then, Şahin, 2020 used the FANGBM(1,1) to forecast Turkey's electricity generation and installed capacity from total renewable and hydro energy. In another study ( Şahin and Şahin, 2020 ), this model was used to forecast the cumulative number of confirmed cases of COVID-19 in many countries.…”

Section: Introductionmentioning

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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“…Thus, it is pertinent to consider FAGM(1,1) by incorporating fractional-order accumulation into GM(1,1) to mitigate such a restriction [ 23 , 24 ]. Several variants have been proposed to strengthen the FAGM(1,1), such as fractional NGBM(1,1) (FANGBM(1,1)) by Wu et al [ 22 ] and Şahin [ 28 ] and fractional GM( q ,1) by Mao et al [ 29 ]. Despite the usefulness of fractional-order accumulation, limited studies related to grey prediction, apart from the fractional time-delayed grey model (FTDGM) of Ma et al [ 30 ], the nonhomogeneous grey model of Wu et al [ 31 ], and the discrete fractional GM(1,1) by Gao et al [ 32 ], have addressed CO 2 emission forecasting using FAGM(1,1).…”

Section: Introductionmentioning

confidence: 99%

An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting

Jiang

Tsai

et al. 2021

IJERPH

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Because grey prediction does not demand that the collected data have to be in line with any statistical distribution, it is pertinent to set up grey prediction models for real-world problems. GM(1,1) has been a widely used grey prediction model, but relevant parameters, including the control variable and developing coefficient, rely on background values that are not easily determined. Furthermore, one-order accumulation is usually incorporated into grey prediction models, which assigns equal weights to each sample, to recognize regularities embedded in data sequences. Therefore, to optimize grey prediction models, this study employed a genetic algorithm to determine the relevant parameters and assigned appropriate weights to the sample data using fractional-order accumulation. Experimental results on the carbon dioxide emission data reported by the International Energy Agency demonstrated that the proposed grey prediction model was significantly superior to the other considered prediction models.

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Projections of Turkey's electricity generation and installed capacity from total renewable and hydro energy using fractional nonlinear grey Bernoulli model and its reduced forms

Cited by 77 publications

References 35 publications

Forecasting the carbon dioxide emissions in 53 countries and regions using a non-equigap grey model

Forecasting the carbon dioxide emissions in 53 countries and regions using a non-equigap grey model

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

An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting

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