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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.
    2. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
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    13. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
    14. 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.
    15. Zheng, Xidong & Zhou, Sheng & Jin, Tao, 2023. "A new machine learning-based approach for cross-region coupled wind-storage integrated systems identification considering electricity demand response and data integration: A new provincial perspective," Energy, Elsevier, vol. 283(C).
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    17. Hu, Jianming & Luo, Qingxi & Tang, Jingwei & Heng, Jiani & Deng, Yuwen, 2022. "Conformalized temporal convolutional quantile regression networks for wind power interval forecasting," Energy, Elsevier, vol. 248(C).
    18. Xiaomei Wu & Songjun Jiang & Chun Sing Lai & Zhuoli Zhao & Loi Lei Lai, 2022. "Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network," Energies, MDPI, vol. 15(18), pages 1-16, September.
    19. Taiyong Li & Yingrui Zhou & Xinsheng Li & Jiang Wu & Ting He, 2019. "Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors," Energies, MDPI, vol. 12(19), pages 1-25, September.
    20. Chao Fu & Guo-Quan Li & Kuo-Ping Lin & Hui-Juan Zhang, 2019. "Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    21. Li, Ranran & Jin, Yu, 2018. "A wind speed interval prediction system based on multi-objective optimization for machine learning method," Applied Energy, Elsevier, vol. 228(C), pages 2207-2220.
    22. Jiang, He & Tao, Changqi & Dong, Yao & Xiong, Ren, 2021. "Robust low-rank multiple kernel learning with compound regularization," European Journal of Operational Research, Elsevier, vol. 295(2), pages 634-647.
    23. 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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