A new hybrid iterative method for short-term wind speed forecasting

被引：31

作者：

Amjady, Nima ^{[1
]}

Keynia, Farshid ^{[1
]}

Zareipour, Hamidreza ^{[2
]}

机构：

[1] Semnan Univ, Dept Elect Engn, Semnan, Iran

[2] Univ Calgary, Schulich Sch Engn, Dept Elect & Comp Engn, Calgary, AB, Canada

来源：

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER | 2011年 / 21卷 / 01期

关键词：

wind power; wind speed forecast; hybrid iterative forecast method; neural network; feature selection; MUTUAL INFORMATION; POWER-GENERATION; NEURAL-NETWORKS; SYSTEMS; PREDICTION;

D O I：

10.1002/etep.463

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：

Forecasting wind power is recognized as a tool in mitigating the operational challenges imposed on power systems by large-scale integration of intermittent wind-powered generators. Wind energy is directly dependent upon wind speed, which is a complex signal to model and forecast. In this paper, a new Hybrid Iterative Forecast Method (HIFM) for wind speed forecasting is presented which takes into account the interactions of temperature and wind speed. To select the most relevant and the less redundant input variables from the available data, a two-stage feature selection technique is also introduced. The forecast accuracy of the proposed wind power prediction strategy is evaluated by means of real data of wind power farms of Iran and Spain's power systems. Copyright (C) 2010 John Wiley & Sons, Ltd.

引用

页码：581 / 595

页数：15

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