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Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting

Author

Listed:
  • Jiani Heng

    (School of Mathematics and Statistics, Lanzhou University, No. 222, TianShui South Road, ChengGuan District, LanZhou 730000, China)

  • Chen Wang

    (School of Mathematics and Statistics, Lanzhou University, No. 222, TianShui South Road, ChengGuan District, LanZhou 730000, China)

  • Xuejing Zhao

    (School of Mathematics and Statistics, Lanzhou University, No. 222, TianShui South Road, ChengGuan District, LanZhou 730000, China)

  • Liye Xiao

    (School of Physical Electronics, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chenghua District, Chengdu 610000, China)

Abstract

Wind energy is increasingly considered one of the most promising sustainable energy sources for its characteristics of cleanliness without any pollution. Wind speed forecasting is a vital problem in wind power industry. However, individual forecasting models ignore the significance of data preprocessing and model parameter optimization, which may lead to poor forecasting performance. In this paper, a novel hybrid [k, B t ] -ABBP (back propagation based on adaptive strategy with parameters k and B t ) model was developed based on an adaptive boosting (AB) strategy that integrates several BP (back propagation) neural networks for wind speed forecasting. The fast ensemble empirical mode decomposition technique is initially conducted in the preprocessing stage to reconstruct data, while a novel modified FPA (flower pollination algorithm) incorporating a conjugate gradient (CG) is proposed for searching for the optimal parameters of the [k, B t ] -ABBP mode. The case studies of five wind power stations in Penglai, China are used as illustrative examples for evaluating the effectiveness and efficiency of the developed hybrid forecast strategy. Numerical results show that the developed hybrid model is simple and can satisfactorily approximate the actual wind speed series. Therefore, the developed hybrid model can be an effective tool in mining and analysis for wind power plants.

Suggested Citation

  • Jiani Heng & Chen Wang & Xuejing Zhao & Liye Xiao, 2016. "Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting," Sustainability, MDPI, vol. 8(3), pages 1-25, March.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:3:p:235-:d:65003
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    References listed on IDEAS

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    Cited by:

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    3. Yuewei Liu & Shenghui Zhang & Xuejun Chen & Jianzhou Wang, 2018. "Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting," Sustainability, MDPI, vol. 10(12), pages 1-30, December.
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    5. Li, Jingrui & Wang, Jianzhou & Zhang, Haipeng & Li, Zhiwu, 2022. "An innovative combined model based on multi-objective optimization approach for forecasting short-term wind speed: A case study in China," Renewable Energy, Elsevier, vol. 201(P1), pages 766-779.
    6. Yechi Zhang & Jianzhou Wang & Haiyan Lu, 2019. "Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting," Energies, MDPI, vol. 12(10), pages 1-27, May.
    7. Pei Du & Yu Jin & Kequan Zhang, 2016. "A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed," Sustainability, MDPI, vol. 8(8), pages 1-25, August.
    8. Wei Sun & Ming Duan, 2019. "Analysis and Forecasting of the Carbon Price in China’s Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machin," Energies, MDPI, vol. 12(2), pages 1-27, January.

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