Artificial neural network models for wind power short-term forecasting using weather predictions

被引：0

作者：

Ramírez-Rosado, IJ ^{[1
]}

Fernández-Jiménez, LA ^{[1
]}

Monteiro, C ^{[1
]}

机构：

[1] Univ La Rioja, Dept Elect Engn, Logrono, Spain

来源：

PROCEEDINGS OF THE 25TH IASTED INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION, AND CONTROL | 2006年

关键词：

short-term forecasting; wind power prediction; time series; neural networks;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The use of wind energy has developed significantly worldwide. Wind power is the strongest growing form of renewable energy, ideal for a future with pollution-free electric power. But the integration of wind farms in power networks has become an important problem for the unit commitment and control of power plants in electric power systems. The intermittent nature of wind makes it difficult to forecast wind-produced electric energy in a wind farm even in the next hours. This paper compares the results obtained with a set of selected models for hourly electric power production forecasting in a real-life wind farm. The results show a significant improvement if previous numerical weather forecasts are used as input in hourly power forecasting models.

引用

页码：128 / +

页数：2

共 10 条

[1] NEURAL NETWORKS AND PRINCIPAL COMPONENT ANALYSIS - LEARNING FROM EXAMPLES WITHOUT LOCAL MINIMA [J].