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A novel dynamic time-delay grey model of energy prices and its application in crude oil price forecasting

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  • Duan, Huiming
  • Liu, Yunmei
  • Wang, Guan
Abstract
The correct forecasting of energy prices can better regulate energy market allocation, reduce the unit energy allocation cost, and maximize the economic benefits of the energy allocation cost. First, this paper aims to study the time-delay in the energy supply and demand relationship, which directly affects changes and fluctuations in the energy price. Starting from the market economic model, this paper analyses the dynamic time-delay process of energy prices by using the relationship between energy supply, demand and prices, and it establishes the time-delay differential equation of energy prices. Second, a novel dynamic grey time-delay forecasting model of energy prices is selected based on the differential information of the differential equation and difference equation and the principle of data reduction. The parameters and optimization methods of the model are studied to obtain the modeling process of the model. Finally, the novel model is applied to predict the Novel York Mercantile Exchange (NYMEX) West Texas Intermediate (WTI) crude oil futures price, the Intercontinental Exchange (ICE) Brent crude oil futures price, and the Organization of the Petroleum Exporting Countries (OPEC) basket crude oil spot price. The forecasting results are better than those of the other four grey prediction models, which indicates that the novel model can effectively predict the oil price and that the novel model can describe the dynamic change law of the energy price system with a time-delay.

Suggested Citation

  • Duan, Huiming & Liu, Yunmei & Wang, Guan, 2022. "A novel dynamic time-delay grey model of energy prices and its application in crude oil price forecasting," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222008714
    DOI: 10.1016/j.energy.2022.123968
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    References listed on IDEAS

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    Cited by:

    1. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    2. Zhou, Huimin & Dang, Yaoguo & Yang, Yingjie & Wang, Junjie & Yang, Shaowen, 2023. "An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles," Energy, Elsevier, vol. 263(PC).
    3. Liu, Longlong & Zhou, Suyu & Jie, Qian & Du, Pei & Xu, Yan & Wang, Jianzhou, 2024. "A robust time-varying weight combined model for crude oil price forecasting," Energy, Elsevier, vol. 299(C).
    4. Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
    5. Costin Radu Boldea & Bogdan Ion Boldea & Tiberiu Iancu, 2023. "The Pandemic Waves’ Impact on the Crude Oil Price and the Rise of Consumer Price Index: Case Study for Six European Countries," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    6. An, Sufang & An, Feng & Gao, Xiangyun & Wang, Anjian, 2023. "Early warning of critical transitions in crude oil price," Energy, Elsevier, vol. 280(C).

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