Nonparametric Prediction Intervals of Wind Power via Linear Programming

This letter proposes a machine learning-based linear programming model that quickly establishes the nonparametric prediction intervals of wind power by integrating extreme learning machine and quantile regression. The proportions of quantiles can be adaptively determined via sensitivity analysis. Th...

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Bibliographic Details
Published in:IEEE transactions on power systems 2018-01, Vol.33 (1), p.1074-1076
Main Authors: Wan, Can, Wang, Jianhui, Lin, Jin, Song, Yonghua, Dong, Zhao Yang
Format: Article
Language:English
Subjects:
Adaptation models
Extreme learning machine
Forecasting
Linear programming
prediction interval
Probabilistic logic
Reliability
Sensitivity analysis
wind power
Wind power generation
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Summary:This letter proposes a machine learning-based linear programming model that quickly establishes the nonparametric prediction intervals of wind power by integrating extreme learning machine and quantile regression. The proportions of quantiles can be adaptively determined via sensitivity analysis. The proposed method has been proven to be significantly efficient and reliable, with a high application potential in power systems.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2017.2716658