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Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy

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  • Li, Yanfei
  • Shi, Huipeng
  • Han, Fengze
  • Duan, Zhu
  • Liu, Hui

Abstract

Big multi-step wind speed forecasting is hard to be realized due to the high -requirement of the built forecasting models. However, the big multi-step forecasting is expected in the wind power systems, which can provide sufficient time for the wind grids to be operated in the emergency cases. In the study, a new hybrid computational framework for the big multi-step wind speed forecasting is proposed, consisting of Wavelet Packet Decomposition (WPD), Elman Neural Networks (ENN), boosting algorithms and Wavelet Packet Filter (WPF). The novelty of the study is to investigate the big multi-step wind speed forecasting performance using various computing strategies in the proposed new hybrid WPD-Boost-ENN-WPF framework. Four different wind speed time series data are provided to complete the real forecasting experiments. The experimental results indicate that: (a) all of the proposed hybrid models have better performance than the corresponding single forecasting models in the big multi-step predictions. The 9 step MAE errors for the experimental data #1 from the proposed four hybrid forecasting models are only 1.2821 m/s, 1.1276 m/s, 1.1718 m/s and 1.2684 m/s, respectively; (b) the proposed four hybrid forecasting models have no significant forecasting difference; and (c) all of them are suitable for the big multi-step wind speed forecasting.

Suggested Citation

  • Li, Yanfei & Shi, Huipeng & Han, Fengze & Duan, Zhu & Liu, Hui, 2019. "Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy," Renewable Energy, Elsevier, vol. 135(C), pages 540-553.
    • Handle: RePEc:eee:renene:v:135:y:2019:i:c:p:540-553
    DOI: 10.1016/j.renene.2018.12.035
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    as
    1. Moreno, Sinvaldo Rodrigues & dos Santos Coelho, Leandro, 2018. "Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System," Renewable Energy, Elsevier, vol. 126(C), pages 736-754.
    2. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    3. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing, 2017. "Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction," Renewable Energy, Elsevier, vol. 113(C), pages 1345-1358.
    4. Chitsazan, Mohammad Amin & Sami Fadali, M. & Trzynadlowski, Andrzej M., 2019. "Wind speed and wind direction forecasting using echo state network with nonlinear functions," Renewable Energy, Elsevier, vol. 131(C), pages 879-889.
    5. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    6. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    7. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    8. Bontempi, Gianluca & Ben Taieb, Souhaib, 2011. "Conditionally dependent strategies for multiple-step-ahead prediction in local learning," International Journal of Forecasting, Elsevier, vol. 27(3), pages 689-699, July.
    9. Liu, Hui & Mi, Xiwei & Li, Yanfei, 2018. "An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm," Renewable Energy, Elsevier, vol. 123(C), pages 694-705.
    10. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    11. Xiong, Tao & Bao, Yukun & Hu, Zhongyi, 2013. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices," Energy Economics, Elsevier, vol. 40(C), pages 405-415.
    12. Wang, Jianzhou & Heng, Jiani & Xiao, Liye & Wang, Chen, 2017. "Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting," Energy, Elsevier, vol. 125(C), pages 591-613.
    13. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    14. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    15. Liu, Hui & Duan, Zhu & Li, Yanfei & Lu, Haibo, 2018. "A novel ensemble model of different mother wavelets for wind speed multi-step forecasting," Applied Energy, Elsevier, vol. 228(C), pages 1783-1800.
    16. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
    17. Hu, Jianming & Wang, Jianzhou & Xiao, Liqun, 2017. "A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts," Renewable Energy, Elsevier, vol. 114(PB), pages 670-685.
    18. Masseran, Nurulkamal, 2016. "Modeling the fluctuations of wind speed data by considering their mean and volatility effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 777-784.
    19. Yang, Zhongshan & Wang, Jian, 2018. "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Applied Energy, Elsevier, vol. 230(C), pages 1108-1125.
    20. Niu, Tong & Wang, Jianzhou & Zhang, Kequan & Du, Pei, 2018. "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Elsevier, vol. 118(C), pages 213-229.
    21. Naik, Jyotirmayee & Bisoi, Ranjeeta & Dash, P.K., 2018. "Prediction interval forecasting of wind speed and wind power using modes decomposition based low rank multi-kernel ridge regression," Renewable Energy, Elsevier, vol. 129(PA), pages 357-383.
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