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Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM

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  • Wang, Qiang
  • Song, Xiaoxin

Abstract

To more accurate forecast China's oil consumption, the major driving force of global new added oil demand, a new nonlinear-dynamic grey model is developed, namely NMGM (1, 1, α). The proposed NMGM (1, 1, α) upgrades the nonlinear grey model (GM) from stationary to dynamic model through effectively integrating nonlinear forecasting technique and the biological metabolism idea. The proposed NMGM (1, 1, α), and other three existing grey models (linear GM (1, 1), nonlinear GM (1, 1, α), metabolism GM (1, 1)) are run respectively to simulate and forecast Chinese oil consumption from 1990 to 2026. The simulation results show that our proposed NMGM (1, 1, α) are higher accurate than the other three models. In addition to better forecast energy consumption, the proposed NMGM (1, 1, α) also can be used to forecast in other fields. The modeling results based on the NMGM (1, 1, a) show that China oil consumption in the next decade (2017–2026) will be increased by 51%. The better forecasting Chinese oil consumption by using the proposed model can provide useful information for the researchers, policymakers and others stakeholders in Chinese and global oil market.

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  • Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.
    • Handle: RePEc:eee:energy:v:183:y:2019:i:c:p:160-171
    DOI: 10.1016/j.energy.2019.06.139
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    7. Wang, Yong & Sun, Lang & Yang, Rui & He, Wenao & Tang, Yanbing & Zhang, Zejia & Wang, Yunhui & Sapnken, Flavian Emmanuel, 2023. "A novel structure adaptive fractional derivative grey model and its application in energy consumption prediction," Energy, Elsevier, vol. 282(C).
    8. Pan, Xunzhang & Wang, Lining & Dai, Jiaquan & Zhang, Qi & Peng, Tianduo & Chen, Wenying, 2020. "Analysis of China’s oil and gas consumption under different scenarios toward 2050: An integrated modeling," Energy, Elsevier, vol. 195(C).
    9. Liu, Yitong & Xue, Dingyu & Yang, Yang, 2021. "Two types of conformable fractional grey interval models and their applications in regional electricity consumption prediction," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    10. Wang, Meng & Wang, Wei & Wu, Lifeng, 2022. "Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China," Energy, Elsevier, vol. 243(C).
    11. 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).
    12. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    13. Pingping Xiong & Xiaojie Wu & Jing Ye, 2023. "Building a novel multivariate nonlinear MGM(1,m,N|γ) model to forecast carbon emissions," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9647-9671, September.
    14. Xu, Haitao & Pan, Xiongfeng & Guo, Shucen & Lu, Yuduo, 2021. "Forecasting Chinese CO2 emission using a non-linear multi-agent intertemporal optimization model and scenario analysis," Energy, Elsevier, vol. 228(C).
    15. Ofosu-Adarkwa, Jeffrey & Xie, Naiming & Javed, Saad Ahmed, 2020. "Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
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