A literature review of wind forecasting technology in the world

被引:186
作者
Wu, Yuan-Kang
Hong, Jing-Shan
机构
来源
2007 IEEE LAUSANNE POWERTECH, VOLS 1-5 | 2007年
关键词
wind power forecasting; numeric weather prediction; statistical methods;
D O I
10.1109/PCT.2007.4538368
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Large intermittent generations have grown the influence on the grid security, system operation, and market economics. Although wind energy may not be dispatched, the cost impacts of wind can be substantially reduced if the wind energy can be scheduled using accurate wind forecasting. In other words, the improvement of the performance of wind power forecasting tool has significant technology and economic impact on the system operation with increased wind power penetration. Forecasting has been a vital part of business planning in today's competitive environment, especially in areas characterized by a high concentration of wind generation and a limited capacity of network. The target of this paper is to present a critical literature review and an up-to-date bibliography on wind forecasting technologies over the world. Various forecasting aspects concerning the wind speed and power have been highlighted. These technologies based on numeric weather prediction (NWP) methods, statistical methods, methods based upon artificial neural networks (ANNs), and hybrid forecasting approaches will be discussed. Furthermore, the difference between wind speed and power forecasting, the lead time of forecasting, and the further research will also be discussed in this paper.
引用
收藏
页码:504 / 509
页数:6
相关论文
共 40 条
[11]  
GILMAN B, 2001, AWEA WINDP 2001 C WA
[12]  
Hong JS, 2003, WEATHER FORECAST, V18, P836, DOI 10.1175/1520-0434(2003)018<0836:EOTHMF>2.0.CO
[13]  
2
[14]  
HUTTING HK, 1999, P EUR WIND EN C NIC, P399
[15]  
Kariniotakis G., 1999, P 1999 EUR WIND EN C, P391
[16]  
Kariniotakis G, 2004, P 2004 SEATECH WEEK
[17]  
Kariniotakis GN, 2004, 2004 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS, P729
[18]   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
[19]  
LANDBERG L, 1998, WIND ENERGY, V1, P23, DOI DOI 10.1002/(SICI)1099-1824(199809)1:13.0.CO
[20]  
2-9