An advanced statistical method for wind power forecasting

被引:416
作者
Sideratos, George [1 ]
Hatziargyriou, Nikos D. [1 ]
机构
[1] Natl Tech Univ Athens, GR-15773 Zografos, Greece
关键词
fuzzy sets; radial base function networks; self-organized map; wind power forecasting;
D O I
10.1109/TPWRS.2006.889078
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
This paper presents an advanced statistical method for wind power forecasting based on artificial intelligence techniques. The method requires as input past power measurements and meteorological forecasts of wind speed and direction interpolated at the site of the wind farm. A self-organized map is trained to classify the forecasted local wind speed provided by the meteorological services. A unique feature of the method is that following a preliminary wind power prediction, it provides an estimation of the quality of the meteorological forecasts that is subsequently used to improve predictions. The proposed method is suitable for operational planning of power systems with increased wind power penetration, i.e., forecasting horizon of 48h ahead and for wind farm operators trading in electricity markets. Application of the forecasting method on the power production of an actual wind farm shows the validity of the method.
引用
收藏
页码:258 / 265
页数:8
相关论文
共 22 条
[11]  
Hatziargyriou N, 2002, 2002 IEEE POWER ENGINEERING SOCIETY WINTER MEETING, VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P335, DOI 10.1109/PESW.2002.985008
[12]  
HATZIARGYRIOU N, 2004, P CIGR SESS PAR FRAN
[13]  
KARINIOTAKIS G, 2004, P GLOB WINDP C CDROM
[14]  
KARINIOTAKIS G, 2004, P EWEC LOND UK NOV 2
[15]   Wind power forecasting using advanced neural networks models. [J].
Kariniotakis, GN ;
Stavrakakis, GS ;
Nogaret, EF .
IEEE TRANSACTIONS ON ENERGY CONVERSION, 1996, 11 (04) :762-767
[16]  
Land JA, 2003, EU PR GY OB, V6, P13
[17]  
MADSEN JH, 2004, P GLOB WIND POW C CD
[18]  
NIELSEN TS, 2002, P GLOB WINDP C EXH P
[19]  
NIELSEN TS, 1997, P EWEC DUBL IR OCT
[20]  
PARSONS B, 2003, P EWEC MADR SPAIN