Wind Power Forecasting Based on Time Series and Neural Network

被引：0

作者：

Li, Lingling ^{[1
]}

Wang, Minghui

Zhu, Fenfen

Wang, Chengshan ^{[1
]}

机构：

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

来源：

PROCEEDINGS OF INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND COMPUTATIONAL TECHNOLOGY (ISCSCT 2009) | 2009年

关键词：

wind power; forecasting; time series; RBF neural network; SUPPORT VECTOR MACHINES;

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 wind farm output power have the characteristics of dynamic, random, large capacity etc, which brought great difficulty for incorporating the wind farm in the bulk power system. In order to rationally regulate the power supply system in large grid connected wind power system and reduce the spinning reserve capacity of the power supply system and operating costs, it is necessary to forecasting the capacity of wind power. For the randomness of the wind farm output, we use the ARMA (q, p) model of time series to forecast wind speed and atmospheric pressure, and using the RBF neural network based on this to forecast wind power. Taking the data of measured wind speed and atmospheric pressure from a wind farm as example, to validate the method described above, and the result show that the method has a certain practicality.

引用

页码：293 / 297

页数：5

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