A High-Accuracy Wind Power Forecasting Model

被引：66

作者：

Fang, Shengchen ^{[1
]}

Chiang, Hsiao-Dong ^{[2
]}

机构：

[1] Tianjin Univ, Sch Elect Engn & Automat, Tianjin 300072, Peoples R China

[2] Cornell Univ, Sch Elect & Comp Engn, Ithaca, NY 14853 USA

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2017年 / 32卷 / 02期

基金：

美国国家科学基金会; 中国国家自然科学基金;

关键词：

Gaussian processes; wind power forecasting; composite covariance function;

D O I：

10.1109/TPWRS.2016.2574700

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：

In this letter, a forecasting model consisting of the Gaussian process with a novel composite covariance function for high-accuracy wind power forecasting is presented. The proposed composite covariance function is based on the exploration of joint effects between numerical weather prediction features. The performance of the proposed forecasting model is evaluated using the 2012 global energy forecasting competition wind power forecasting data, and the proposed model outperforms all of the competitors.

引用

页码：1589 / 1590

页数：2

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