期刊
ENERGY
卷 113, 期 -, 页码 355-365出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2016.07.055
关键词
Wind power; Quantile regression; Reproducing Kernel Hilbert Space (RKHS); Probabilistic forecast; Short-term; On-line
资金
- DAVE - Departamento de Aeronaves y Vehculos Espaciales (UPM) through the Bolsa de Apoyo a Jovenes Investigadores
- ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE Programme [POCI-01-0145-FEDER-006961]
- National Funds through the FCT - Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) [UID/EEA/50014/2013]
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 cross-validation. 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. (C) 2016 Elsevier Ltd. All rights reserved.
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