Forecasting Quarterly Sales Volume of the New Energy Vehicles Industry in China Using a Data Grouping Approach-Based Nonlinear Grey Bernoulli Model

Pei, Ling-Ling; Li, Qin

doi:10.3390/su11051247

Cited by 24 publications

(9 citation statements)

References 33 publications

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“…[49] analyzed electricity consumption for China using the grey polynomial prediction model and forecasted from 2011 to 2015. [50] analyzed the energy vehicle industry for China using grouping approach-based nonlinear grey Bernoulli model (DGA-based NGBM(1, 1)) and GM(1, 1) forecasting from 2018Q1 to 2020Q4. [57] forecast for by using Self-adaptive intelligence grey predictive model and forecasted for 2014.…”

Section: Literature Reviewmentioning

confidence: 99%

How to Predict Energy Consumption in BRICS Countries?

Khan

Osińska

2021

Energies

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Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to predict energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the predictive ability of the with traditional ones, like standard and models. Moreover, it illustrates the view of BRICS’s nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of for a specific range of order parameters and the model and the usefulness of both approaches for energy consumption efficient forecasting.

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Section: Literature Reviewmentioning

confidence: 99%

How to Predict Energy Consumption in BRICS Countries?

Khan

Osińska

2021

Energies

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“…[42] analyzed electricity consumption for China by using the grey polynomial prediction model and forecasted from 2011 to 2015. [43] analyzed the energy vehicle industry for China by using grouping approach-based nonlinear grey Bernoulli model (DGA-based…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Forecasting Ability for Energy Consumption in BRICS: ARIMA (1,1,1) and FGM (1, 1) Models

Khan¹,

Osińska²

2021

Preprint

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Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to forecast energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the forecasting ability of the FGM(1,1) with traditional ones, like standard GM(1,1) and ARIMA(1,1,1) models. Also, it illustrates the view of BRICS's nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM(1,1) for a specific range of order parameters and the ARIMA(1,1,1) model and the usefulness of both approaches for energy consumption efficient forecasting.

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“…When γ is equal to 0, the NGBM(1,1) reduces to the GM(1,1) ( Wu et al., 2019a ). Researchers show that the NGBM(1,1) gives higher prediction performance than the GM(1,1) ( Chen, 2008 ; Chen et al., 2008 ; Chen et al., 2010 ; Hsu, 2010 ; Tsai, 2016 ; Pei and Li, 2019 ; Wu and Zhang, 2020 ). Chen et al.…”

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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Forecasting Quarterly Sales Volume of the New Energy Vehicles Industry in China Using a Data Grouping Approach-Based Nonlinear Grey Bernoulli Model

Cited by 24 publications

References 33 publications

How to Predict Energy Consumption in BRICS Countries?

How to Predict Energy Consumption in BRICS Countries?

Comparison of Forecasting Ability for Energy Consumption in BRICS: ARIMA (1,1,1) and FGM (1, 1) Models

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

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