A comprehensive error evaluation method for short-term wind power prediction

Dianli Xitong Zidonghua/Automation of Electric Power Systems

Volumn 35, Issue 12, 2011, Pages 20-26

  Xu, Man ^a   Qiao, Ying ^a   Lu, Zongxiang ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Error evaluation; Error index; Numerical weather prediction; Short term wind power prediction

Indexed keywords

COMPREHENSIVE ASSESSMENT; COMPREHENSIVE EVALUATION; COMPREHENSIVE EVALUATION INDEX; CORRELATION FACTORS; ERROR EVALUATION; ERROR INDEX; EVALUATION METHOD; EXTREME CONDITIONS; INNER MONGOLIA; MEAN VALUES; NUMERICAL WEATHER PREDICTION; PHASE ERROR; POWER PREDICTIONS; PREDICTION ACCURACY; PREDICTION ERRORS; PREDICTION METHODS; PREDICTION SYSTEMS; SHORT-TERM WIND POWER PREDICTION; WIND FARM;

ELECTRIC UTILITIES; ERRORS; WIND POWER;

FORECASTING;

EID: 79960287766     PISSN: 10001026     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (17)

1. KAVASSERI G, SEETHARAMAN K. Day-ahead wind speed forecasting using f-ARIMA models [J]. Renewable Energy, 2009, 34 (5): 1388-1393.
2. FAN Gaofeng, WANG Weisheng, LIU Chun. Artificial neural network based wind power short term prediction system [J]. Power System Technology, 2008, 32 (22): 72-76.
3. YANG Qi, ZHANG Jianhua, WANG Xiangfeng, et al. Wind speed and generated wind power forecast based on wavelet-neural network [J]. Power System Technology, 2009, 33 (17): 44-48.
4. 2009.
5. 2009.
6. WANG Xiaolan, WANG Mingwei. Short-term wind speed forecasting based on wavelet decomposition and least square support vector machine [J]. Power System Technology, 2010, 34 (1): 179-184.
7. JING Tianjun, RUAN Rui, YANG Minghao. Wind power forecast based on equivalent average wind speed [J]. Automation of Electric Power Systems, 2009, 33 (24): 83-87.
8. LIU Hui, TIAN Hongqi, LI Yanfei. Short-term forecasting optimization algorithm for wind speed from wind farms based on wavelet analysis method and rolling time series method [J]. Journal of Central South University: Science and Technology, 2010, 41 (1): 370-375.
9. DOHERTY R, O'MALLEY M. A new approach to quantify reserve demand in systems with significant installed wind capacity [J]. IEEE Trans on Power Systems, 2005, 20 (2): 587-595.
10. FENG Shuanglei, WANG Weisheng, LIU Chun, et al. Study on the physical approach to wind power prediction [J]. Proceedings of the CSEE, 2010, 30 (2): 1-6.
11. A protocol for standardizing the performance evaluation of short-term wind power prediction models [EB/OL]. [2010-08-10]. http://anemos.cma.fr.
12. Next generation short-term forecasting of wind power: overview of the ANEMOS project [EB/OL]. [2010-08-10]. http://anemos.cma.fr.
13. ACKERMANN T. Wind power in power systems [M]. Chichester, England: John Wiley and Sons Ltd, 2005: 365-381.
14. The state-of-the-art in short-term prediction of wind power: a literature review [EB/OL]. [2010-08-10]. http://anemos.cma.fr.
15. LANDBERG L. Short-term prediction of local wind conditions [J]. Journal of Wind Engineering and Industrial Aerodynamics, 2001, 89 (3/4): 235-245.
16. 2002.
17. WANG Caixia, LU Zongxiang, QIAO Ying, et al. Short-term wind power forecast based on non-parametric regression model [J]. Automation of Electric Power Systems, 2010, 34 (16): 78-82.