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Big Data Analysis of Energy Economics in Oil Market

In: Big Data in Energy Economics

Author

Listed:
  • Hui Liu

    (Central South University)

  • Nikolaos Nikitas

    (University of Leeds)

  • Yanfei Li

    (Hunan Agricultural University)

  • Rui Yang

    (Central South University)

Abstract

As industrial blood and raw material, crude oil occupies an important strategic position in the economic market of global energy. In this chapter, the economic significance and social value of forecasting crude oil prices are first introduced. Then the factors that affect the fluctuation in crude oil prices are expounded, including the relationship between supply and demand, economic development, policy formulation, global emergencies, etc. Next, this chapter conducts price prediction modeling on West Texas Intermediate crude oil and Brent crude oil, the two most important benchmark oils in the international crude oil market. The experiment compares the prediction capabilities of bi-directional long short-term memory network and deep belief network. Finally, this chapter provides an econometric analysis of the international crude oil market, big data technologies, and policy recommendations for developing the energy economy.

Suggested Citation

  • Hui Liu & Nikolaos Nikitas & Yanfei Li & Rui Yang, 2022. "Big Data Analysis of Energy Economics in Oil Market," Management for Professionals, in: Big Data in Energy Economics, chapter 0, pages 43-65, Springer.
    • Handle: RePEc:spr:mgmchp:978-981-16-8965-9_2
    DOI: 10.1007/978-981-16-8965-9_2
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    Citations

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    Cited by:

    1. Xia, Xiqiang & Chishti, Muhammad Zubair & Dogan, Eyup, 2024. "Transition towards the sustainable development: unraveling the effects of mineral markets, Belt & Road Initiative, and the Paris Agreement on green economic growth," Resources Policy, Elsevier, vol. 91(C).
    2. Bożena Gajdzik & Magdalena Jaciow & Radosław Wolniak & Robert Wolny & Wieslaw Wes Grebski, 2024. "Diagnosis of the Development of Energy Cooperatives in Poland—A Case Study of a Renewable Energy Cooperative in the Upper Silesian Region," Energies, MDPI, vol. 17(3), pages 1-27, January.
    3. Xu, Mengjie & Li, Xiang & Li, Qianwen & Sun, Chuanwang, 2024. "LNBi-GRU model for coal price prediction and pattern recognition analysis," Applied Energy, Elsevier, vol. 365(C).
    4. Abbasi, H.N. & Zeeshan, Muhammad, 2023. "An integrated Geographic Information System and Analytical Hierarchy process based approach for site suitability analysis of on-grid hybrid concentrated solar-biomass powerplant," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).

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