Scenario Analysis of Natural Gas Consumption in China Based on Wavelet Neural Network Optimized by Particle Swarm Optimization Algorithm

Deyun Wang, Yanling Liu, Zeng Wu, Hongxue Fu, Yong Shi and Haixiang Guo
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Deyun Wang: School of Economics and Management, China University of Geosciences, Wuhan 430074, China
Yanling Liu: School of Economics and Management, China University of Geosciences, Wuhan 430074, China
Zeng Wu: School of Economics and Management, China University of Geosciences, Wuhan 430074, China
Hongxue Fu: School of Economics and Management, China University of Geosciences, Wuhan 430074, China
Yong Shi: Université de Bourgogne Franche-Comté, UTBM, IRTES, Rue Thierry Mieg, Belfort CEDEX 90010, France
Haixiang Guo: School of Economics and Management, China University of Geosciences, Wuhan 430074, China

Energies, 2018, vol. 11, issue 4, 1-16

Abstract: Natural gas consumption has increased with an average annual growth rate of about 10% between 2012 and 2017. Total natural gas consumption accounted for 6.4% of consumed primary energy resources in 2016, up from 5.4% in 2012, making China the world’s third-largest gas user. Therefore, accurately predicting natural gas consumption has become very important for market participants to organize indigenous production, foreign supply contracts and infrastructures in a better way. This paper first presents the main factors affecting China’s natural gas consumption, and then proposes a hybrid forecasting model by combining the particle swarm optimization algorithm and wavelet neural network (PSO-WNN). In PSO-WNN model, the initial weights and wavelet parameters are optimized using PSO algorithm and updated through a dynamic learning rate to improve the training speed, forecasting precision and reduce fluctuation of WNN. The experimental results show the superiority of the proposed model compared with ANN and WNN based models. Then, this study conducts the scenario analysis of the natural gas consumption from 2017 to 2025 in China based on three scenarios, namely low scenario, reference scenario and high scenario, and the results illustrate that the China’s natural gas consumption is going to be 342.70, 358.27, 366.42 million tce (“standard” tons coal equivalent) in 2020, and 407.01, 437.95, 461.38 million tce in 2025 under the low, reference and high scenarios, respectively. Finally, this paper provides some policy suggestions on natural gas exploration and development, infrastructure construction and technical innovations to promote a sustainable development of China’s natural gas industry.

Keywords: natural gas consumption forecasting; particle swarm optimization; wavelet neural networks; scenario analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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