On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power

Cristobal Gallego-Castillo, Ricardo Bessa, Laura Cavalcante and Oscar Lopez-Garcia

Energy, 2016, vol. 113, issue C, 355-365

Abstract: Wind power probabilistic forecast is being used as input in several decision-making problems, such as stochastic unit commitment, operating reserve setting and electricity market bidding. This work introduces a new on-line quantile regression model based on the Reproducing Kernel Hilbert Space (RKHS) framework. Its application to the field of wind power forecasting involves a discussion on the choice of the bias term of the quantile models, and the consideration of the operational framework in order to mimic real conditions. Benchmark against linear and splines quantile regression models was performed for a real case study during a 18 months period. Model parameter selection was based on k-fold crossvalidation. Results showed a noticeable improvement in terms of calibration, a key criterion for the wind power industry. Modest improvements in terms of Continuous Ranked Probability Score (CRPS) were also observed for prediction horizons between 6 and 20 h ahead.

Keywords: Wind power; Quantile regression; Reproducing Kernel Hilbert Space (RKHS); Probabilistic forecast; Short-term; On-line (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:113:y:2016:i:c:p:355-365

DOI: 10.1016/j.energy.2016.07.055

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