Short Term Wind Power Forecasting Using Time Series Neural Networks

被引：0

作者：

Zakerinia, Mohammadsaleh ^{[1
]}

Ghaderi, Seyed Farid ^{[1
]}

机构：

[1] Univ Tehran, Coll Engn, Dept Ind Engn, Syst Dynam Lab, Tehran, Iran

来源：

EMERGING M&S APPLICATIONS IN INDUSTRY & ACADEMIA SYMPOSIUM 2011 (EAIA 2011) - 2011 SPRING SIMULATION MULTICONFERENCE - BK 5 OF 8 | 2011年

关键词：

Wind power forecasting; neural networks; time series; SPEED;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Forecasting wind power energy is very important issue in a liberalized market and the prediction tools can make wind energy be competitive in these kinds of markets. This paper will study an application of time-series and neural network for predicting wind energy production in a short time 1 hour ahead. Several types of typical neural networks like adaptive linear element and black propagation (BP) are used. Moreover, the performance of different methods is assessed based on mean absolute error, root mean square error and mean absolute percentage error. The data that has been used in this paper is obtained from Renewable Energy Organization of Iran and applied in the site of Shiraz.

引用

页码：17 / 22

页数：6

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