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Incorporating the influence of China's industrial capacity elimination policies in electricity demand forecasting

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  • Song, Zongyun
  • Niu, Dongxiao
  • Dai, Shuyu
  • Xiao, Xinli
  • Wang, Yuwei

Abstract

Elimination of high-emitting industrial capacity can be an effective way to control and relieve air pollution but it will also influence demand. Accurate forecasting of electricity demand requires consideration of the impact of capacity elimination policies. This paper applies a modified firefly algorithm improved by Gaussian disturbance to optimize the parameters of Support Vector Machine (SVM) in order to quantify the impact of capacity elimination policies. We consider three policy scenarios and three growth scenarios in our analysis. The results demonstrate that modified firefly algorithm (MFA) can improve the forecasting performance of SVM and electricity demand forecasting considering the influence of capacity elimination policies, and provide good reference for policy analysis and electricity demand analysis.

Suggested Citation

  • Song, Zongyun & Niu, Dongxiao & Dai, Shuyu & Xiao, Xinli & Wang, Yuwei, 2017. "Incorporating the influence of China's industrial capacity elimination policies in electricity demand forecasting," Utilities Policy, Elsevier, vol. 47(C), pages 1-11.
    • Handle: RePEc:eee:juipol:v:47:y:2017:i:c:p:1-11
    DOI: 10.1016/j.jup.2017.05.004
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    References listed on IDEAS

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    1. Liu, Xuhua & Li, Na & Hu, Yuqin, 2015. "Combining inferences on the common mean of several inverse Gaussian distributions based on confidence distribution," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 136-142.
    2. Roughgarden, Tim & Schneider, Stephen H., 1999. "Climate change policy: quantifying uncertainties for damages and optimal carbon taxes," Energy Policy, Elsevier, vol. 27(7), pages 415-429, July.
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    Cited by:

    1. Song, Yi & Huang, Jian-Bai & Feng, Chao, 2018. "Decomposition of energy-related CO2 emissions in China's iron and steel industry: A comprehensive decomposition framework," Resources Policy, Elsevier, vol. 59(C), pages 103-116.
    2. Jinchai Lin & Kaiwei Zhu & Zhen Liu & Jenny Lieu & Xianchun Tan, 2019. "Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation," Energies, MDPI, vol. 12(11), pages 1-28, June.
    3. Yujing Liu & Ruoyun Du & Dongxiao Niu, 2022. "Forecast of Coal Demand in Shanxi Province Based on GA—LSSVM under Multiple Scenarios," Energies, MDPI, vol. 15(17), pages 1-16, September.
    4. Lin, Jiang & Xu Liu, & Gang He,, 2020. "Regional electricity demand and economic transition in China," Utilities Policy, Elsevier, vol. 64(C).
    5. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    6. Wang, Yongli & Zhou, Minhan & Zhang, Fuli & Zhang, Yuli & Ma, Yuze & Dong, Huanran & Zhang, Danyang & Liu, Lin, 2021. "Chinese grid investment based on transmission and distribution tariff policy: An optimal coordination between capacity and demand," Energy, Elsevier, vol. 219(C).

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