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Optimal modeling and forecasting of the energy consumption and production in China

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  • Xiong, Ping-ping
  • Dang, Yao-guo
  • Yao, Tian-xiang
  • Wang, Zheng-xin

Abstract

Energy is of fundamental importance to a nation's economy. Accurate prediction of the energy consumption and production in China can play a guiding role in making the energy consumption plan, and facilitate timely and effective decision making of energy policy. This article proposes a novel GM (gray model) (1,1) model based on optimizing initial condition according to the principle of new information priority. The optimized model and five other GM (1,1) models are applied in the modeling of China's energy consumption and production. Both the simulation and prediction accuracy of the models are compared and analyzed. We obtain the result that the optimized model has higher prediction accuracy than the other five models. Therefore, the presented optimized model is further utilized to predict China's energy consumption and production from 2013 to 2017. The result indicates that China's energy consumption and production will keep increasing and the gap between the energy production and consumption will also be increasing. Finally, we predict Iran's and Argentina's energy consumption to further prove the effectiveness of the proposed model.

Suggested Citation

  • Xiong, Ping-ping & Dang, Yao-guo & Yao, Tian-xiang & Wang, Zheng-xin, 2014. "Optimal modeling and forecasting of the energy consumption and production in China," Energy, Elsevier, vol. 77(C), pages 623-634.
  • Handle: RePEc:eee:energy:v:77:y:2014:i:c:p:623-634
    DOI: 10.1016/j.energy.2014.09.056
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    2. Jean Gaston Tamba & Salomé Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouandélé & Bozidar Soldo & Donatien Njomo, 2018. "Forecasting Natural Gas: A Literature Survey," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 216-249.
    3. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, Open Access Journal, vol. 12(2), pages 1-30, January.
    4. Wang, Ce & Li, Bing-Bing & Liang, Qiao-Mei & Wang, Jin-Cheng, 2018. "Has China’s coal consumption already peaked? A demand-side analysis based on hybrid prediction models," Energy, Elsevier, vol. 162(C), pages 272-281.
    5. Ding, Song, 2018. "A novel self-adapting intelligent grey model for forecasting China's natural-gas demand," Energy, Elsevier, vol. 162(C), pages 393-407.
    6. Pruethsan Sutthichaimethee & Kuskana Kubaha, 2018. "The Efficiency of Long-Term Forecasting Model on Final Energy Consumption in Thailand’s Petroleum Industries Sector: Enriching the LT-ARIMAXS Model under a Sustainability Policy," Energies, MDPI, Open Access Journal, vol. 11(8), pages 1-18, August.
    7. Li, Bing-Bing & Liang, Qiao-Mei & Wang, Jin-Cheng, 2015. "A comparative study on prediction methods for China's medium- and long-term coal demand," Energy, Elsevier, vol. 93(P2), pages 1671-1683.
    8. Dedinec, Aleksandra & Filiposka, Sonja & Dedinec, Aleksandar & Kocarev, Ljupco, 2016. "Deep belief network based electricity load forecasting: An analysis of Macedonian case," Energy, Elsevier, vol. 115(P3), pages 1688-1700.
    9. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    10. Berk, Istemi & Ediger, Volkan Ş., 2016. "Forecasting the coal production: Hubbert curve application on Turkey's lignite fields," Resources Policy, Elsevier, vol. 50(C), pages 193-203.
    11. Nana Geng & Yong Zhang & Yixiang Sun & Yunjian Jiang & Dandan Chen, 2015. "Forecasting China’s Annual Biofuel Production Using an Improved Grey Model," Energies, MDPI, Open Access Journal, vol. 8(10), pages 1-20, October.
    12. Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
    13. Xu, Ning & Dang, Yaoguo & Gong, Yande, 2017. "Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China," Energy, Elsevier, vol. 118(C), pages 473-480.
    14. Feng Jiang & Xue Yang & Shuyu Li, 2018. "Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model," Sustainability, MDPI, Open Access Journal, vol. 10(7), pages 1-17, June.
    15. Xu, Ning & Ding, Song & Gong, Yande & Bai, Ju, 2019. "Forecasting Chinese greenhouse gas emissions from energy consumption using a novel grey rolling model," Energy, Elsevier, vol. 175(C), pages 218-227.
    16. 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.
    17. 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).
    18. Zeng, Bo & Duan, Huiming & Bai, Yun & Meng, Wei, 2018. "Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator," Energy, Elsevier, vol. 151(C), pages 238-249.
    19. Akdi, Yılmaz & Gölveren, Elif & Okkaoğlu, Yasin, 2020. "Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting," Energy, Elsevier, vol. 191(C).
    20. Yushen Tian & Siqin Xiong & Xiaoming Ma, 2017. "Analysis of the Potential Impacts on China’s Industrial Structure in Energy Consumption," Sustainability, MDPI, Open Access Journal, vol. 9(12), pages 1-13, December.
    21. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
    22. Ma, Xin & Mei, Xie & Wu, Wenqing & Wu, Xinxing & Zeng, Bo, 2019. "A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China," Energy, Elsevier, vol. 178(C), pages 487-507.
    23. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.

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