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

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  • Deyun Wang

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China
    Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China
    Key Laboratory for the Land and Resources Strategic Studies, Ministry of Land and Resources, Wuhan 430074, China
    Université de Bourgogne Franche-Comté, UTBM, IRTES, Rue Thierry Mieg, Belfort CEDEX 90010, France)

  • 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
    Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China
    Key Laboratory for the Land and Resources Strategic Studies, Ministry of Land and Resources, Wuhan 430074, China)

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.

Suggested Citation

  • Deyun Wang & Yanling Liu & Zeng Wu & Hongxue Fu & Yong Shi & Haixiang Guo, 2018. "Scenario Analysis of Natural Gas Consumption in China Based on Wavelet Neural Network Optimized by Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 11(4), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:825-:d:139356
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    References listed on IDEAS

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    Cited by:

    1. Konstantinos Papageorgiou & Elpiniki I. Papageorgiou & Katarzyna Poczeta & Dionysis Bochtis & George Stamoulis, 2020. "Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 13(9), pages 1-32, May.
    2. Gejirifu De & Wangfeng Gao, 2018. "Forecasting China’s Natural Gas Consumption Based on AdaBoost-Particle Swarm Optimization-Extreme Learning Machine Integrated Learning Method," Energies, MDPI, vol. 11(11), pages 1-20, October.
    3. Haikun Shang & Junyan Xu & Zitao Zheng & Bing Qi & Liwei Zhang, 2019. "A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory," Energies, MDPI, vol. 12(20), pages 1-22, October.
    4. Longfeng Zhang & Xin Ma & Hui Zhang & Gaoxun Zhang & Peng Zhang, 2022. "Multi-Step Ahead Natural Gas Consumption Forecasting Based on a Hybrid Model: Case Studies in The Netherlands and the United Kingdom," Energies, MDPI, vol. 15(19), pages 1-26, October.
    5. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    6. Pedro J. Zarco-Periñán & Irene M. Zarco-Soto & Fco. Javier Zarco-Soto & Rafael Sánchez-Durán, 2021. "Influence of Population Income on Energy Consumption for Heating and Its CO 2 Emissions in Cities," Energies, MDPI, vol. 14(15), pages 1-18, July.

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