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Prediction interval forecasting of wind speed and wind power using modes decomposition based low rank multi-kernel ridge regression

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  • Naik, Jyotirmayee
  • Bisoi, Ranjeeta
  • Dash, P.K.

Abstract

In this paper a new hybrid method combining variational mode decomposition (VMD) and low rank Multi-kernel ridge regression (MKRR) is presented for direct and effective construction of prediction intervals (PIs) for short-term forecasting of wind speed and wind power. The original time series signals are decomposed using VMD approach to prevent the mutual effects among the different modes. The proposed VMD-MKRR method is used to construct the PIs with different confidence levels of 95%, 90% and 85% for wind speed and wind power of two wind farms which are located in the state of Wyoming, USA for time intervals of 10 min, 30 min and 1 h and in the state of California for time interval of 1 h respectively. Comparison with empirical mode decomposition (EMD) based low rank kernel ridge regression is also presented in the paper to validate the superiority of the VMD based wind speed and wind power model. Further to enhance the proposed model performance their parameters are optimized using Mutated Firefly Algorithm with Global optima concept (MFAGO).

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:129:y:2018:i:pa:p:357-383
    DOI: 10.1016/j.renene.2018.05.031
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    1. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
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    Cited by:

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    12. Li, Chaoshun & Tang, Geng & Xue, Xiaoming & Chen, Xinbiao & Wang, Ruoheng & Zhang, Chu, 2020. "The short-term interval prediction of wind power using the deep learning model with gradient descend optimization," Renewable Energy, Elsevier, vol. 155(C), pages 197-211.
    13. Xie, Yuying & Li, Chaoshun & Tang, Geng & Liu, Fangjie, 2021. "A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting," Energy, Elsevier, vol. 216(C).
    14. Jianzhong Zhou & Han Liu & Yanhe Xu & Wei Jiang, 2018. "A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network," Energies, MDPI, vol. 11(9), pages 1-18, August.
    15. Lv, Jiaqing & Zheng, Xiaodong & Pawlak, Mirosław & Mo, Weike & Miśkowicz, Marek, 2021. "Very short-term probabilistic wind power prediction using sparse machine learning and nonparametric density estimation algorithms," Renewable Energy, Elsevier, vol. 177(C), pages 181-192.
    16. Xu, Xuefang & Hu, Shiting & Shi, Peiming & Shao, Huaishuang & Li, Ruixiong & Li, Zhi, 2023. "Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm," Energy, Elsevier, vol. 262(PA).
    17. Heydari, Azim & Astiaso Garcia, Davide & Keynia, Farshid & Bisegna, Fabio & De Santoli, Livio, 2019. "A novel composite neural network based method for wind and solar power forecasting in microgrids," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    18. Naik, Jyotirmayee & Dash, Pradipta Kishore & Dhar, Snehamoy, 2019. "A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression," Renewable Energy, Elsevier, vol. 136(C), pages 701-731.
    19. Qiang Zhao & Kunkun Bao & Jia Wang & Yinghua Han & Jinkuan Wang, 2019. "An Online Hybrid Model for Temperature Prediction of Wind Turbine Gearbox Components," Energies, MDPI, vol. 12(20), pages 1-20, October.
    20. 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.
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    22. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.

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