Nonparametric Prediction Intervals of Wind Power via Linear Programming

被引：70

作者：

Wan, Can ^{[1
]}

Wang, Jianhui ^{[2
]}

Lin, Jin ^{[3
]}

Song, Yonghua ^{[1
]}

Dong, Zhao Yang ^{[4
]}

机构：

[1] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China

[2] Argonne Natl Lab, Lemont, IL 60439 USA

[3] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China

[4] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2018年 / 33卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Extreme learning machine; forecasting; linear programming; prediction interval; sensitivity analysis; wind power; QUANTILE REGRESSION; GENERATION;

D O I：

10.1109/TPWRS.2017.2716658

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：1074 / 1076

页数：3

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