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China's primary energy demands in 2020: Predictions from an MPSO-RBF estimation model

Author

Listed:
  • Shiwei Yu
  • Yi-Ming Wei

    () (Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology)

  • Ke Wang

Abstract

In the present study, a Mix-encoding Particle Swarm Optimization and Radial Basis Function (MPSO-RBF) network-based energy demand forecasting model is proposed and appliedto forecast China's energy consumption until 2020. The energy demand isanalyzed for the period from 1980 to 2009 based on GDP, population, proportion of industry in GDP, urbanization rate, and share of coal energy. The results reveal that the proposed MPSO-RBF based model has fewer hidden nodes andsmaller estimated errors compared with other ANN-based estimation models. The average annual growth of China's energy demand will be 6.70%, 2.81%, and 5.08% for the period between 2010 and 2020 in three scenarios and could reach 6.25 billion, 4.16 billion, and 5.29 billion tons coal equivalentin 2020.Regardless of future scenarios, China's energy efficiency in 2020 will increase by more than 30% compared with 2009.

Suggested Citation

  • Shiwei Yu & Yi-Ming Wei & Ke Wang, 2011. "China's primary energy demands in 2020: Predictions from an MPSO-RBF estimation model," CEEP-BIT Working Papers 15, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
  • Handle: RePEc:biw:wpaper:15
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    File URL: http://ceep.bit.edu.cn/docs/2018-10/20181011134518426873.pdf
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    References listed on IDEAS

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    1. 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.
    2. Amarawickrama, Himanshu A. & Hunt, Lester C., 2008. "Electricity demand for Sri Lanka: A time series analysis," Energy, Elsevier, vol. 33(5), pages 724-739.
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    Cited by:

    1. Xu Tang & Benjamin C. McLellan & Simon Snowden & Baosheng Zhang & Mikael Höök, 2015. "Dilemmas for China: Energy, Economy and Environment," Sustainability, MDPI, Open Access Journal, vol. 7(5), pages 1-13, May.
    2. repec:spr:anresc:v:58:y:2017:i:2:d:10.1007_s00168-016-0782-5 is not listed on IDEAS
    3. Zhen-Yao Chen & R. J. Kuo, 0. "Combining SOM and evolutionary computation algorithms for RBF neural network training," Journal of Intelligent Manufacturing, Springer, vol. 0, pages 1-18.

    More about this item

    Keywords

    China's energy demand; forecasting; Radial Basis Function (RBF) neural network; energy intensity;

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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