Wind power day-ahead prediction with cluster analysis of NWP

被引：123

作者：

Dong, Lei ^{[1
]}

Wang, Lijie ^{[2
]}

Khahro, Shahnawaz Farhan ^{[1
]}

Gao, Shuang ^{[1
]}

Liao, Xiaozhong ^{[1
]}

机构：

[1] Beijing Inst Technol, Sch Automat, 5 Zhongguancun South St, Beijing 100081, Peoples R China

[2] Beijing Informat Sci & Technol, Dept Elect Engn, 12 Xiaoying East St, Beijing, Peoples R China

来源：

RENEWABLE & SUSTAINABLE ENERGY REVIEWS | 2016年 / 60卷

关键词：

Wind power prediction; Numerical weather prediction; Cluster analysis; Modeling; Daily similarity; SPEED; FORECASTS;

D O I：

10.1016/j.rser.2016.01.106

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The selection of training data for establishing a model directly affects the prediction precision. Wind power has the characteristic of daily similarity. The corresponding meteorological data also has the characteristic of daily similarity. This paper proposes a new model with cluster analysis of the numerical weather prediction information. The similar day with the predicted day is searched as training sample to a generalized regression neural network model. The numerical weather prediction data and actual wind power data from a wind farm are used in this study to test the model. The prediction results show that correct cluster analysis method is a useful tool in day-ahead wind power prediction. (C) 2016 Published by Elsevier Ltd.

引用

页码：1206 / 1212

页数：7

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