Current methods and advances in forecasting of wind power generation

被引:863
作者
Foley, Aoife M. [1 ,2 ,3 ]
Leahy, Paul G. [2 ,3 ]
Marvuglia, Antonino [4 ]
McKeogh, Eamon J. [2 ,3 ]
机构
[1] Queens Univ Belfast, Sch Mech & Aerosp Engn, Belfast BT9 5AH, Antrim, North Ireland
[2] Natl Univ Ireland Univ Coll Cork, Sch Engn, Dept Civil & Environm Engn, Cork, Ireland
[3] Natl Univ Ireland Univ Coll Cork, Environm Res Inst, Cork, Ireland
[4] Natl Univ Ireland Univ Coll Cork, Cork Constraint Computat Ctr 4C, Cork, Ireland
关键词
Meteorology; Numerical weather prediction; Probabilistic forecasting; Wind integration wind power forecasting; SHORT-TERM PREDICTION; SPEED PREDICTION; ENERGY; INTEGRATION; ALGORITHMS; FRAMEWORK; TERRAIN; MODELS; OUTPUT;
D O I
10.1016/j.renene.2011.05.033
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised. (C) 2011 Elsevier Ltd. All rights reserved.
引用
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页码:1 / 8
页数:8
相关论文
共 70 条
[11]  
CENA A, 2006, AVANZANDO GESTIONABI
[12]  
COSTELLO R, 2002, P 3 MED POW C 2002 A
[13]   A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation [J].
Damousis, IG ;
Alexiadis, MC ;
Theocharis, JB ;
Dokopoulos, PS .
IEEE TRANSACTIONS ON ENERGY CONVERSION, 2004, 19 (02) :352-361
[14]  
Damousis IG, 2001, P IEEE INT C POW IND
[16]   Grey predictor for wind energy conversion systems output power prediction [J].
El-Fouly, T. H. M. ;
El-Saadany, E. F. ;
Salama, M. M. A. .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2006, 21 (03) :1450-1452
[17]   Forecasting the Wind Generation Using a Two-Stage Network Based on Meteorological Information [J].
Fan, Shu ;
Liao, James R. ;
Yokoyama, Ryuichi ;
Chen, Luonan ;
Lee, Wei-Jen .
IEEE TRANSACTIONS ON ENERGY CONVERSION, 2009, 24 (02) :474-482
[18]   Short-term prediction of the aggregated power output of wind farms -: a statistical analysis of the reduction of the prediction error by spatial smoothing effects [J].
Focken, U ;
Lange, M ;
Mönnich, K ;
Waldl, HP ;
Beyer, HG ;
Luig, A .
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 2002, 90 (03) :231-246
[19]  
Fugon Lionel, 2008, P EUR WIND EN C EWEC
[20]  
Giebel G., 2005, 1527 RIS NAT LAB