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Predicting the production and consumption of natural gas in China by using a new grey forecasting method

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  • Lao, Tongfei
  • Sun, Yanrui

Abstract

Reasonable prediction of natural gas consumption and production in China is helpful for decision-makers formulate policies to solve the increasingly prominent contradiction between supply and demand of natural gas in China. Taking into account the data environment of China’s small sample, this paper considers using the grey prediction model with characteristic of small sample modeling to study the consumption and production of natural gas in China. To achieve accurate prediction, this paper developed a novel discrete fractional nonlinear grey Bernoulli model with power term (DFNGBM(1,1, α)), and used the latest equilibrium optimizer to solve the hyper-parameters of the model. The proposed model has the advantages of most existing grey prediction models, such as fractional-order accumulation operation and time power term. In addition, the proposed model is a unified expression form of many other grey forecasting models. The consumption and production of natural gas in China from 2003 to 2020 are used as examples to verify the feasibility and validity of the proposed model compared with other competitive models that have been used to study natural gas. It turns out that the prediction ability of the proposed model is better than other models. Therefore, the feasibility and effectiveness of the proposed model have been confirmed. Finally, the DFNGBM (1,1, α) model is used to predict China’s natural gas consumption and production in the next few years, and some corresponding recommendations are given based on the prediction results.

Suggested Citation

  • Lao, Tongfei & Sun, Yanrui, 2022. "Predicting the production and consumption of natural gas in China by using a new grey forecasting method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 295-315.
    • Handle: RePEc:eee:matcom:v:202:y:2022:i:c:p:295-315
    DOI: 10.1016/j.matcom.2022.05.023
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    References listed on IDEAS

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    1. Qiao, Weibiao & Liu, Wei & Liu, Enbin, 2021. "A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S," Energy, Elsevier, vol. 235(C).
    2. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    3. Liu, Chong & Wu, Wen-Ze & Xie, Wanli & Zhang, Jun, 2020. "Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    4. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
    5. Chen, Chun-I, 2008. "Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 278-287.
    6. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2019. "Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model," Renewable Energy, Elsevier, vol. 140(C), pages 70-87.
    7. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).
    8. Özmen, Ayşe & Yılmaz, Yavuz & Weber, Gerhard-Wilhelm, 2018. "Natural gas consumption forecast with MARS and CMARS models for residential users," Energy Economics, Elsevier, vol. 70(C), pages 357-381.
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