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Wind Power Installed Capacity Forecast Based on a Two-Parameter Variable-Weight Buffer Operator and Subsidy Strategy Research

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  • Ye Li
  • Xue Bai
  • Giulio E. Cantarella

Abstract

Since the installed capacity of wind power is greatly affected by the subsidy policy, this paper predicts the installed wind power capacity in China under different policy scenarios. Firstly, a new two-parameter variable-weight buffer operator is proposed to quantify the impact of the policy shock, whose optimal parameters are obtained by the genetic algorithm, and combined with the grey GM(1,1) model to predict the installed capacity of wind power in China under the cessation of subsidy policy. Then, the GM(1,1) model with optimized background value is used to predict the installed capacity under continued subsidy. Finally, two policy subsidy strategy models are constructed based on the forecast data to simulate the future trend of wind power installed capacity under different subsidy policies and explore the strength of wind power subsidies in China during the “14th Five-Year Plan†period (2021–2025). The results show that both the GM(1,1) model based on the two-parameter variable-weight buffer operator and the GM(1,1) model with optimized background value have high fitting accuracy, with errors of 0.15% and 4.93%, respectively. Furthermore, in the case of the central government’s subsidy cancellation, the local government should take over the central government’s subsidy policy and adjust the subsidy intensity to 0.57–1 times of that during the “13th Five-Year Plan†period (2015–2020) to achieve the national planning target.

Suggested Citation

  • Ye Li & Xue Bai & Giulio E. Cantarella, 2022. "Wind Power Installed Capacity Forecast Based on a Two-Parameter Variable-Weight Buffer Operator and Subsidy Strategy Research," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-14, March.
  • Handle: RePEc:hin:jnddns:5380366
    DOI: 10.1155/2022/5380366
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    Cited by:

    1. Liu, Jinpeng & Lin, Yingwen & Jiang, Mingyue & Guo, Xia, 2024. "Exploring policy support for wind power development from a balancing perspective - A study of dynamic strategies based on evolutionary game," Energy Policy, Elsevier, vol. 188(C).
    2. Li, Zekai & Hu, Xi & Guo, Huan & Xiong, Xin, 2023. "A novel Weighted Average Weakening Buffer Operator based Fractional order accumulation Seasonal Grouping Grey Model for predicting the hydropower generation," Energy, Elsevier, vol. 277(C).

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