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Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation


  • Wang, Xiaoyu
  • Luo, Dongkun
  • Zhao, Xu
  • Sun, Zhu


Primary energy plays a critical role in the socio-economic development of China, and accurate energy consumption forecasting can help the government to formulate energy policies. To do this, the present study aims to apply a self-adaptive multi-verse optimizer (AMVO) to optimize the parameters of the support vector machine (SVM). It employs a rolling cross-validation scheme to predict China's primary energy consumption in which the independent variables are gross domestic product (GDP) per capita, population, the urbanization rate, the share of the industry in GDP and coal's share of primary energy consumption. The results indicate that the hybrid AMVO-SVM model has higher precision than other models. Finally, we apply the hybrid AMVO-SVM model to predict the energy consumption of China between 2017 and 2030 in five scenarios. In the reference scenario, China's primary energy consumption will reach 4839.3 Mtce in 2020 and 5656.2 Mtce in 2030.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:energy:v:152:y:2018:i:c:p:539-548
    DOI: 10.1016/

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    Cited by:

    1. 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.
    2. Yixi Xue & Jie Ren & Xiaohang Bi, 2019. "Impact of Influencing Factors on CO 2 Emissions in the Yangtze River Delta during Urbanization," Sustainability, MDPI, Open Access Journal, vol. 11(15), pages 1-19, August.
    3. Linlin Zhao & Zhansheng Liu & Jasper Mbachu, 2019. "Energy Management through Cost Forecasting for Residential Buildings in New Zealand," Energies, MDPI, Open Access Journal, vol. 12(15), pages 1-24, July.
    4. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
    5. Ying Wang & Peipei Shang & Lichun He & Yingchun Zhang & Dandan Liu, 2018. "Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?," Energies, MDPI, Open Access Journal, vol. 11(10), pages 1-32, October.
    6. Yu, Kunjie & Qu, Boyang & Yue, Caitong & Ge, Shilei & Chen, Xu & Liang, Jing, 2019. "A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module," Applied Energy, Elsevier, vol. 237(C), pages 241-257.


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