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

Yi Xiao (), Keying Li (), Yi Hu (), Jin Xiao () and Shouyang Wang ()
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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

Mitigation and Adaptation Strategies for Global Change, 2020, vol. 25, issue 7, No 8, 1325-1343

Abstract: 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.

Keywords: Natural gas consumption forecasting; Emission reduction; Deep learning; STRIPAT model; Gated recurrent unit model; Bagging (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11027-020-09918-1

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