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The research on modeling and application of dynamic grey forecasting model based on energy price-energy consumption-economic growth

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  • Li, Hui
  • Wu, Zixuan
  • Yuan, Xing
  • Yang, Yixuan
  • He, Xiaoqiang
  • Duan, Huiming

Abstract

The unbalanced development among energy price, energy consumption, and economic growth will destroy the stability of the energy market. The nonlinear differential equations among several factors are established based on the actual situation of energy market changes and the causal relationship between energy price and economic growth in an economic stage and discuss the relationship among the factors. The nonlinear grey prediction model group of energy price-energy consumption-economic growth is established by using grey difference information. The least-square method is utilized to give the parameter estimation formula of the prediction model group, and the approximate time response formula of the model is obtained by recursive relation transformation. Finally, the new models are applied to the prediction of coal price and coal consumption in China, and the effectiveness of the two models analyzes the same data set. It shows that the prediction results of coal price and coal consumption are better than the other three classical grey prediction models, and it can effectively predict the coal price and consumption in China from 2021 to 2025. The prediction results can provide adequate information for China's energy system and have theoretical and practical significance for the stability of the energy system.

Suggested Citation

  • Li, Hui & Wu, Zixuan & Yuan, Xing & Yang, Yixuan & He, Xiaoqiang & Duan, Huiming, 2022. "The research on modeling and application of dynamic grey forecasting model based on energy price-energy consumption-economic growth," Energy, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:energy:v:257:y:2022:i:c:s0360544222017042
    DOI: 10.1016/j.energy.2022.124801
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    1. Kang, Yuxiao & Mao, Shuhua & Zhang, Yonghong, 2022. "Fractional time-varying grey traffic flow model based on viscoelastic fluid and its application," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 149-174.
    2. Wang, Xiaoyu & Luo, Dongkun & Zhao, Xu & Sun, Zhu, 2018. "Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation," Energy, Elsevier, vol. 152(C), pages 539-548.
    3. Zhou, Jie & Sun, Mei & Han, Dun & Gao, Cuixia, 2021. "Analysis of oil price fluctuation under the influence of crude oil stocks and US dollar index — Based on time series network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    4. Lin, Boqiang & Xie, Chunping, 2013. "Estimation on oil demand and oil saving potential of China's road transport sector," Energy Policy, Elsevier, vol. 61(C), pages 472-482.
    5. Kohler, Marcel, 2014. "Differential electricity pricing and energy efficiency in South Africa," Energy, Elsevier, vol. 64(C), pages 524-532.
    6. Sun, Mei & Tian, Lixin & Fu, Ying, 2007. "An energy resources demand–supply system and its dynamical analysis," Chaos, Solitons & Fractals, Elsevier, vol. 32(1), pages 168-180.
    7. Hu, Huanling & Wang, Lin & Peng, Lu & Zeng, Yu-Rong, 2020. "Effective energy consumption forecasting using enhanced bagged echo state network," Energy, Elsevier, vol. 193(C).
    8. Lee, Chien-Chiang & Chiu, Yi-Bin, 2011. "Oil prices, nuclear energy consumption, and economic growth: New evidence using a heterogeneous panel analysis," Energy Policy, Elsevier, vol. 39(4), pages 2111-2120, April.
    9. 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).
    10. Sun, Qi & Xu, Lin & Yin, Hua, 2016. "Energy pricing reform and energy efficiency in China: Evidence from the automobile market," Resource and Energy Economics, Elsevier, vol. 44(C), pages 39-51.
    11. 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).
    12. Liu, Chong & Wu, Wen-Ze & Xie, Wanli & Zhang, Jun, 2020. "Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    13. Ding, Song & Li, Ruojin & Wu, Shu & Zhou, Weijie, 2021. "Application of a novel structure-adaptative grey model with adjustable time power item for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 298(C).
    14. Zeng, Yu-Rong & Zeng, Yi & Choi, Beomjin & Wang, Lin, 2017. "Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network," Energy, Elsevier, vol. 127(C), pages 381-396.
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