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Combining STRIPAT model and gated recurrent unit for forecasting nature gas consumption of China

Author

Listed:
  • Yi Xiao

    (Central China Normal University)

  • Keying Li

    (Central China Normal University)

  • Yi Hu

    (University of Chinese Academy of Sciences)

  • Jin Xiao

    (Sichuan University)

  • Shouyang Wang

    (Chinese Academy of Sciences)

Abstract

With the orderly advancement of China Energy Development Strategic Action Plan, clean energy has become a major trend in the energy market. As a major industry of clean energy, natural gas industry plans to consume at least 10% of the total primary energy by 2020. The energy structure will be improved in an orderly manner to achieve the goal of energy conservation, consumption reduction, and emission reduction. To achieve energy saving and emission reduction, and using clean energy effectively, accurate prediction of natural gas consumption is of great importance. Because of the many influencing factors affecting natural gas demand, this paper first utilizes STRIPAT to analyze the factors affecting natural gas consumption and then uses a deep learning ensemble approach to analyze and predict China’s natural gas consumption. One is an advanced deep neural network model named gated recurrent unit model which is used to model the nonlinear and complex relationships of natural gas consumption with its factors. The other is a powerful ensemble method named bootstrap aggregation which generates multiple data sets for training a set of base models. Our approach combines the advantages of these two technologies to forecast the demand for China’s natural gas market. In empirical research, our method has been tested by some competitive methods and has shown superiority.

Suggested Citation

  • Yi Xiao & Keying Li & Yi Hu & Jin Xiao & Shouyang Wang, 2020. "Combining STRIPAT model and gated recurrent unit for forecasting nature gas consumption of China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(7), pages 1325-1343, October.
    • Handle: RePEc:spr:masfgc:v:25:y:2020:i:7:d:10.1007_s11027-020-09918-1
    DOI: 10.1007/s11027-020-09918-1
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    References listed on IDEAS

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