IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v140y2017ip1p941-951.html
   My bibliography  Save this article

Forecasting China’s natural gas demand based on optimised nonlinear grey models

Author

Listed:
  • Shaikh, Faheemullah
  • Ji, Qiang
  • Shaikh, Pervez Hameed
  • Mirjat, Nayyar Hussain
  • Uqaili, Muhammad Aslam

Abstract

Natural gas increasingly has become an important policy choice for China to modify its high carbon energy consumption structure. Natural gas is a low carbon energy option for China’s government to fulfil its volunteer commitments with the international community to mitigate greenhouse gas emissions. This study has constructed China’s natural gas consumption forecasting model by utilising two optimised nonlinear grey models: the Grey Verhulst Model and the Nonlinear Grey Bernoulli Model. Both of these models have precisely adapted China’s actual natural gas consumption and forecasted that the country’s natural gas demand will reach 315 billion m3 by 2020. In addition, the existing and projected natural gas supplies and the capacities of imports, such as liquefied natural gas and pipeline natural gas, have been evaluated to gain a better understanding of the supply-demand and import trends. Accordingly, it has been observed that China’s existing and planned natural gas supplies and LNG and PNG infrastructure will be sufficient to cope with the growing energy demand for the period 2014–2020. However, this situation will cause a significant increase in its import dependency.

Suggested Citation

  • Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
    • Handle: RePEc:eee:energy:v:140:y:2017:i:p1:p:941-951
    DOI: 10.1016/j.energy.2017.09.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217315591
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.09.037?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    2. Azadeh, A. & Asadzadeh, S.M. & Saberi, M. & Nadimi, V. & Tajvidi, A. & Sheikalishahi, M., 2011. "A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: The cases of Bahrain, Saudi Arabia, Syria, and UAE," Applied Energy, Elsevier, vol. 88(11), pages 3850-3859.
    3. Lin, Chiun-Sin & Liou, Fen-May & Huang, Chih-Pin, 2011. "Grey forecasting model for CO2 emissions: A Taiwan study," Applied Energy, Elsevier, vol. 88(11), pages 3816-3820.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    6. Bianco, Vincenzo & Scarpa, Federico & Tagliafico, Luca A., 2014. "Scenario analysis of nonresidential natural gas consumption in Italy," Applied Energy, Elsevier, vol. 113(C), pages 392-403.
    7. Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.
    8. Yao, Albert W.L. & Chi, S.C. & Chen, C.K., 2005. "Development of an integrated Grey–fuzzy-based electricity management system for enterprises," Energy, Elsevier, vol. 30(15), pages 2759-2771.
    9. Lin, Boqiang & Wang, Ting, 2012. "Forecasting natural gas supply in China: Production peak and import trends," Energy Policy, Elsevier, vol. 49(C), pages 225-233.
    10. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    11. Gutiérrez, R. & Nafidi, A. & Gutiérrez Sánchez, R., 2005. "Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model," Applied Energy, Elsevier, vol. 80(2), pages 115-124, February.
    12. Li, Junchen & Dong, Xiucheng & Shangguan, Jianxin & Hook, Mikael, 2011. "Forecasting the growth of China’s natural gas consumption," Energy, Elsevier, vol. 36(3), pages 1380-1385.
    13. Melikoglu, Mehmet, 2013. "Vision 2023: Forecasting Turkey's natural gas demand between 2013 and 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 393-400.
    14. Wang, Jianliang & Feng, Lianyong & Zhao, Lin & Snowden, Simon, 2013. "China's natural gas: Resources, production and its impacts," Energy Policy, Elsevier, vol. 55(C), pages 690-698.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Jianzhou & Jiang, Haiyan & Zhou, Qingping & Wu, Jie & Qin, Shanshan, 2016. "China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1149-1167.
    2. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    3. Jean Gaston Tamba & Salom Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouand l & Bozidar Soldo & Donatien Njomo, 2018. "Forecasting Natural Gas: A Literature Survey," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 216-249.
    4. Ravnik, J. & Hriberšek, M., 2019. "A method for natural gas forecasting and preliminary allocation based on unique standard natural gas consumption profiles," Energy, Elsevier, vol. 180(C), pages 149-162.
    5. Ding, Song, 2018. "A novel self-adapting intelligent grey model for forecasting China's natural-gas demand," Energy, Elsevier, vol. 162(C), pages 393-407.
    6. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra & Agca Aktunc, Esra, 2021. "Forecasting models for daily natural gas consumption considering periodic variations and demand segregation," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    7. Ergun Yukseltan & Ahmet Yucekaya & Ayse Humeyra Bilge & Esra Agca Aktunc, 2020. "Forecasting Models for Daily Natural Gas Consumption Considering Periodic Variations and Demand Segregation," Papers 2003.13385, arXiv.org.
    8. Potočnik, Primož & Soldo, Božidar & Šimunović, Goran & Šarić, Tomislav & Jeromen, Andrej & Govekar, Edvard, 2014. "Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia," Applied Energy, Elsevier, vol. 129(C), pages 94-103.
    9. Song, Jiancai & Zhang, Liyi & Jiang, Qingling & Ma, Yunpeng & Zhang, Xinxin & Xue, Guixiang & Shen, Xingliang & Wu, Xiangdong, 2022. "Estimate the daily consumption of natural gas in district heating system based on a hybrid seasonal decomposition and temporal convolutional network model," Applied Energy, Elsevier, vol. 309(C).
    10. Sen, Doruk & Günay, M. Erdem & Tunç, K.M. Murat, 2019. "Forecasting annual natural gas consumption using socio-economic indicators for making future policies," Energy, Elsevier, vol. 173(C), pages 1106-1118.
    11. Guo-Feng Fan & An Wang & Wei-Chiang Hong, 2018. "Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting," Energies, MDPI, vol. 11(7), pages 1-21, June.
    12. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
    13. Lee, Yi-Shian & Tong, Lee-Ing, 2012. "Forecasting nonlinear time series of energy consumption using a hybrid dynamic model," Applied Energy, Elsevier, vol. 94(C), pages 251-256.
    14. Zhou, Zhongbing & Qin, Quande, 2020. "Decoding China's natural gas development: A critical discourse analysis of the five-year plans," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    15. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    16. Wang, Jianliang & Mohr, Steve & Feng, Lianyong & Liu, Huihui & Tverberg, Gail E., 2016. "Analysis of resource potential for China’s unconventional gas and forecast for its long-term production growth," Energy Policy, Elsevier, vol. 88(C), pages 389-401.
    17. Bartłomiej Gaweł & Andrzej Paliński, 2021. "Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree," Energies, MDPI, vol. 14(16), pages 1-26, August.
    18. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    19. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    20. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:140:y:2017:i:p1:p:941-951. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.