IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v151y2018icp238-249.html
   My bibliography  Save this article

Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator

Author

Listed:
  • Zeng, Bo
  • Duan, Huiming
  • Bai, Yun
  • Meng, Wei

Abstract

China has the richest shale gas resources worldwide. However, the exploitation of shale gas in China is very recent, and historical data on the output of shale gas are extremely limited (only five data points exist). Consequently, common mathematical models designed for use with big data cannot be used to forecast the shale gas output in China. Grey models can be constructed by using small samples; however, traditional grey models have the drawback of 'misplaced replacement' during the conversion from a difference equation to a differential equation. Thus, a new unbiased grey prediction model called UGM(1,1) is proposed and optimised in this study. A grey weakening buffer operator was employed to pre-process the primary data on Chinese shale gas output to eliminate the contradiction between the prediction results of models and the conclusions of qualitative analysis. The UGM(1,1) model was then used to simulate the output of shale gas in China, and found to outperform other grey models. Finally, we forecasted the output of shale gas in China from 2017 to 2025, and analysed the rationality of the prediction data. The study findings will be of important reference value for use by the Chinese government to formulate energy policies.

Suggested Citation

  • Zeng, Bo & Duan, Huiming & Bai, Yun & Meng, Wei, 2018. "Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator," Energy, Elsevier, vol. 151(C), pages 238-249.
    • Handle: RePEc:eee:energy:v:151:y:2018:i:c:p:238-249
    DOI: 10.1016/j.energy.2018.03.045
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054421830447X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.03.045?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. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
    2. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    3. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    4. Xiong, Ping-ping & Dang, Yao-guo & Yao, Tian-xiang & Wang, Zheng-xin, 2014. "Optimal modeling and forecasting of the energy consumption and production in China," Energy, Elsevier, vol. 77(C), pages 623-634.
    5. Kim, Tae Hong & Cho, Jinhyung & Lee, Kun Sang, 2017. "Evaluation of CO2 injection in shale gas reservoirs with multi-component transport and geomechanical effects," Applied Energy, Elsevier, vol. 190(C), pages 1195-1206.
    6. Wu, Lifeng & Liu, Sifeng & Liu, Dinglin & Fang, Zhigeng & Xu, Haiyan, 2015. "Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model," Energy, Elsevier, vol. 79(C), pages 489-495.
    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. Weiwei Pan & Lirong Jian & Tao Liu, 2019. "Grey system theory trends from 1991 to 2018: a bibliometric analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1407-1434, December.
    2. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    3. Nana Geng & Yong Zhang & Yixiang Sun & Yunjian Jiang & Dandan Chen, 2015. "Forecasting China’s Annual Biofuel Production Using an Improved Grey Model," Energies, MDPI, vol. 8(10), pages 1-20, October.
    4. Qian, Wuyong & Wang, Jue, 2020. "An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China," Energy, Elsevier, vol. 209(C).
    5. Zhou, Wenhao & Zeng, Bo & Wang, Jianzhou & Luo, Xiaoshuang & Liu, Xianzhou, 2021. "Forecasting Chinese carbon emissions using a novel grey rolling prediction model," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    6. Xu, Ning & Dang, Yaoguo & Gong, Yande, 2017. "Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China," Energy, Elsevier, vol. 118(C), pages 473-480.
    7. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    8. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    9. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    10. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    11. 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.
    12. Pruethsan Sutthichaimethee & Kuskana Kubaha, 2018. "The Efficiency of Long-Term Forecasting Model on Final Energy Consumption in Thailand’s Petroleum Industries Sector: Enriching the LT-ARIMAXS Model under a Sustainability Policy," Energies, MDPI, vol. 11(8), pages 1-18, August.
    13. Hamid R. Khosravani & María Del Mar Castilla & Manuel Berenguel & Antonio E. Ruano & Pedro M. Ferreira, 2016. "A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building," Energies, MDPI, vol. 9(1), pages 1-24, January.
    14. Bo Zeng & Shuliang Li & Wei Meng & Dehai Zhang, 2019. "An improved gray prediction model for China’s beef consumption forecasting," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-18, September.
    15. Xu, Ning & Ding, Song & Gong, Yande & Bai, Ju, 2019. "Forecasting Chinese greenhouse gas emissions from energy consumption using a novel grey rolling model," Energy, Elsevier, vol. 175(C), pages 218-227.
    16. Wang, Zheng-Xin & He, Ling-Yang & Zheng, Hong-Hao, 2019. "Forecasting the residential solar energy consumption of the United States," Energy, Elsevier, vol. 178(C), pages 610-623.
    17. Feng Jiang & Xue Yang & Shuyu Li, 2018. "Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model," Sustainability, MDPI, vol. 10(7), pages 1-17, June.
    18. Duan, Huiming & Pang, Xinyu, 2021. "A multivariate grey prediction model based on energy logistic equation and its application in energy prediction in China," Energy, Elsevier, vol. 229(C).
    19. Huang, Jian-Bai & Luo, Yu-Mei & Feng, Chao, 2019. "An overview of carbon dioxide emissions from China's ferrous metal industry: 1991-2030," Resources Policy, Elsevier, vol. 62(C), pages 541-549.
    20. Ling-Ling Pei & Qin Li & Zheng-Xin Wang, 2018. "The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China," IJERPH, MDPI, vol. 15(3), pages 1-17, March.

    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:151:y:2018:i:c:p:238-249. 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.