Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

被引：127

作者：

Wan, Can ^{[1
]}

Lin, Jin ^{[2
]}

Song, Yonghua ^{[1
]}

Xu, Zhao ^{[3
]}

Yang, Guangya ^{[4
]}

机构：

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

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

[3] Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China

[4] Tech Univ Denmark, Ctr Elect Power & Energy CEE, DK-2800 Lyngby, Denmark

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2017年 / 32卷 / 03期

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Extreme learning machine; prediction intervals; probabilistic forecasting; PV power; quantile regression;

D O I：

10.1109/TPWRS.2016.2608740

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：

A novel efficient probabilistic forecasting approach is proposed to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming-based prediction interval construction model for PV power generation is established based on an extreme learning machine and quantile regression, featuring high reliability and computational efficiency. The proposed approach is validated through the numerical studies on PV data from Denmark.

引用

页码：2471 / 2472

页数：2

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