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

Jyotirmayee Naik, Ranjeeta Bisoi and P.K. Dash

Renewable Energy, 2018, vol. 129, issue PA, 357-383

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).

Keywords: Wind speed and wind power forecasting; Empirical mode decomposition; Variational mode decomposition; Kernel ridge regression; Mutated firefly algorithm (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:129:y:2018:i:pa:p:357-383

DOI: 10.1016/j.renene.2018.05.031

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