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Forecasting Natural Gas Consumption of China Using a Novel Grey Model

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  • Chengli Zheng
  • Wen-Ze Wu
  • Jianming Jiang
  • Qi Li

Abstract

As is known, natural gas consumption has been acted as an extremely important role in energy market of China, and this paper is to present a novel grey model which is based on the optimized nonhomogeneous grey model (ONGM (1,1)) in order to accurately predict natural gas consumption. This study begins with proving that prediction results are independent of the first entry of original series using the product theory of determinant; on this basis, it is a reliable approach by inserting an arbitrary number in front of the first entry of original series to extract messages, which has been proved that it is an appreciable approach to increase prediction accuracy of the traditional grey model in the earlier literature. An empirical example often appeared in testing for prediction accuracy of the grey model is utilized to demonstrate the effectiveness of the proposed model; the numerical results indicate that the proposed model has a better prediction performance than other commonly used grey models. Finally, the proposed model is applied to predict China’s natural gas consumption from 2019 to 2023 in order to provide some valuable information for energy sectors and related enterprises.

Suggested Citation

  • Chengli Zheng & Wen-Ze Wu & Jianming Jiang & Qi Li, 2020. "Forecasting Natural Gas Consumption of China Using a Novel Grey Model," Complexity, Hindawi, vol. 2020, pages 1-9, March.
  • Handle: RePEc:hin:complx:3257328
    DOI: 10.1155/2020/3257328
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    Cited by:

    1. Erum Rehman & Muhammad Ikram & Shazia Rehman & Ma Tie Feng, 2021. "Growing green? Sectoral-based prediction of GHG emission in Pakistan: a novel NDGM and doubling time model approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 12169-12191, August.
    2. Xiong, Xin & Hu, Xi & Tian, Tian & Guo, Huan & Liao, Han, 2022. "A novel Optimized initial condition and Seasonal division based Grey Seasonal Variation Index model for hydropower generation," Applied Energy, Elsevier, vol. 328(C).
    3. Wang, Qi & Suo, Ruixia & Han, Qiutong, 2024. "A study on natural gas consumption forecasting in China using the LMDI-PSO-LSTM model: Factor decomposition and scenario analysis," Energy, Elsevier, vol. 292(C).
    4. Li, Fengyun & Li, Xingmei & Zheng, Haofeng & Yang, Fei & Dang, Ruinan, 2021. "How alternative energy competition shocks natural gas development in China: A novel time series analysis approach," Resources Policy, Elsevier, vol. 74(C).
    5. Tiwari, Aviral Kumar & Sharma, Gagan Deep & Rao, Amar & Hossain, Mohammad Razib & Dev, Dhairya, 2024. "Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting," Energy Economics, Elsevier, vol. 134(C).
    6. Xiong, Xin & Hu, Xi & Guo, Huan, 2021. "A hybrid optimized grey seasonal variation index model improved by whale optimization algorithm for forecasting the residential electricity consumption," Energy, Elsevier, vol. 234(C).
    7. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.

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