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Coal consumption forecasting using an optimized grey model: The case of the world's top three coal consumers

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  • Tong, Mingyu
  • Dong, Jingrong
  • Luo, Xilin
  • Yin, Dejun
  • Duan, Huiming

Abstract

An accurate and objective prediction of coal consumption is important for stabilizing the coal market, ensuring the operating results of coal enterprises. This paper starts with the background value of the traditional GM(1,1) model, extends the model by the extrapolation method, proposes an optimized grey prediction model, deduces the time response equation, studies the relationship between model parameters and model accuracy. Then the model is optimized by simulated annealing algorithm. Three cases verify the validity of model, the results show that its lowest prediction error percentage is 3.1286%, which is better than two classical grey prediction models, Finally, the model is applied to forecast the next five years' consumption of China, India and the United States, which are the world's top three coal consumers. The errors of the proposed models are all about 5%, which is obviously better than the comparison models. The results show that coal consumption in China and India will continue to rise over the next five years, while the United States will decline. These findings are consistent with the current development of the three countries; therefore, the optimized prediction model can effectively predict the coal consumption of the three countries.

Suggested Citation

  • Tong, Mingyu & Dong, Jingrong & Luo, Xilin & Yin, Dejun & Duan, Huiming, 2022. "Coal consumption forecasting using an optimized grey model: The case of the world's top three coal consumers," Energy, Elsevier, vol. 242(C).
  • Handle: RePEc:eee:energy:v:242:y:2022:i:c:s0360544221030358
    DOI: 10.1016/j.energy.2021.122786
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    References listed on IDEAS

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    1. 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.
    2. Crompton, Paul & Wu, Yanrui, 2005. "Energy consumption in China: past trends and future directions," Energy Economics, Elsevier, vol. 27(1), pages 195-208, January.
    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. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    5. Azadeh, A. & Tarverdian, S., 2007. "Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption," Energy Policy, Elsevier, vol. 35(10), pages 5229-5241, October.
    6. Chen, Yan & Lifeng, Wu & Lianyi, Liu & Kai, Zhang, 2020. "Fractional Hausdorff grey model and its properties," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    7. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
    8. 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.
    9. Ma, Xin & Mei, Xie & Wu, Wenqing & Wu, Xinxing & Zeng, Bo, 2019. "A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China," Energy, Elsevier, vol. 178(C), pages 487-507.
    10. Duan, Huiming & Pang, Xinyu, 2021. "A multivariate grey prediction model based on energy logistic equation and its application in energy prediction in China," Energy, Elsevier, vol. 229(C).
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    Cited by:

    1. Yijue Sun & Fenglin Zhang, 2022. "Grey Multivariable Prediction Model of Energy Consumption with Different Fractional Orders," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    2. Wang, Delu & Tian, Cuicui & Mao, Jinqi & Chen, Fan, 2023. "Forecasting coal demand in key coal consuming industries based on the data-characteristic-driven decomposition ensemble model," Energy, Elsevier, vol. 282(C).

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