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Energies 2018, 11(4), 825; https://doi.org/10.3390/en11040825

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

1,2,3,4,*, 1, 1, 1, 4 and 1,2,3
1
School of Economics and Management, China University of Geosciences, Wuhan 430074, China
2
Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China
3
Key Laboratory for the Land and Resources Strategic Studies, Ministry of Land and Resources, Wuhan 430074, China
4
Université de Bourgogne Franche-Comté, UTBM, IRTES, Rue Thierry Mieg, Belfort CEDEX 90010, France
*
Author to whom correspondence should be addressed.
Received: 9 February 2018 / Revised: 17 March 2018 / Accepted: 27 March 2018 / Published: 3 April 2018
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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. View Full-Text
Keywords: natural gas consumption forecasting; particle swarm optimization; wavelet neural networks; scenario analysis natural gas consumption forecasting; particle swarm optimization; wavelet neural networks; scenario analysis
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Wang, D.; Liu, Y.; Wu, Z.; Fu, H.; Shi, Y.; Guo, H. Scenario Analysis of Natural Gas Consumption in China Based on Wavelet Neural Network Optimized by Particle Swarm Optimization Algorithm. Energies 2018, 11, 825.

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