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Study on the forecast model of electricity substitution potential in Beijing-Tianjin-Hebei region considering the impact of electricity substitution policies

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  • Wang, Yongli
  • Wang, Shuo
  • Song, Fuhao
  • Yang, Jiale
  • Zhu, Jinrong
  • Zhang, Fuwei

Abstract

In recent years, China has introduced a series of energy substitution policies, aiming to achieve a fundamental change in the way of energy development. Firstly, based on the world energy model constructed by IEA, this paper determines the main driving factors in the field of power substitution, including economy, technology, policy, energy price and environmental factors. Secondly, based on the principle of system dynamics, a causal relationship diagram of five driving factors is constructed to clarify the feedback and linkage relationship between the factors. Finally, this paper proposes a hybrid prediction model combining Salp Swarm Algorithm (SSA) and Least Squares Support Vector Machines (LSSVM), namely SSA-LSSVM, which fully considers the influence of relevant factors of electric energy substitution, while avoiding the subjectivity of model parameter setting. The results show that the future development of electric energy substitution in the Beijing-Tianjin-Hebei region has maintained a rapid pace. The potential for electric energy substitution in the industrial production sector is large, and there is a large space for mining and operation optimization in the residential heating and transportation sectors. This paper provides a basis for the future development and improvement of the strategic plan of electric energy substitution in Beijing-Tianjin-Hebei region.

Suggested Citation

  • Wang, Yongli & Wang, Shuo & Song, Fuhao & Yang, Jiale & Zhu, Jinrong & Zhang, Fuwei, 2020. "Study on the forecast model of electricity substitution potential in Beijing-Tianjin-Hebei region considering the impact of electricity substitution policies," Energy Policy, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:enepol:v:144:y:2020:i:c:s0301421520304158
    DOI: 10.1016/j.enpol.2020.111686
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    References listed on IDEAS

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    1. Carvallo, Juan Pablo & Larsen, Peter H. & Sanstad, Alan H. & Goldman, Charles A., 2018. "Long term load forecasting accuracy in electric utility integrated resource planning," Energy Policy, Elsevier, vol. 119(C), pages 410-422.
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    Cited by:

    1. Chen, Hai-Bao & Pei, Ling-Ling & Zhao, Yu-Feng, 2021. "Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach," Energy, Elsevier, vol. 222(C).
    2. Zhao, Zhenyu & Zhang, Yao & Yang, Yujia & Yuan, Shuguang, 2022. "Load forecasting via Grey Model-Least Squares Support Vector Machine model and spatial-temporal distribution of electric consumption intensity," Energy, Elsevier, vol. 255(C).
    3. Dongxiao Niu & Tian Gao & Zhengsen Ji & Yujing Liu & Gengqi Wu, 2021. "Analysis of the Efficiency of Provincial Electricity Substitution in China Based on a Three-Stage DEA Model," Energies, MDPI, vol. 14(20), pages 1-17, October.

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