[1] 范高锋，裴哲义，辛耀中. 风电功率预测的发展现状与展望[J].中国电力，２０１１，４４（６）：３８－４１．
FAN Gaofeng, PEI Zheyi, XIN Yaozhong. Wind power prediction achievement and prospect [J]. Electric Power, 2011, 44(6): 38-41.
[2] LANDBERG L. Short-term prediction of local wind conditions[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2001, 89: 235-245
[3] LANDBERG L. Short-term prediction of the power production from wind farms[J]. Journal of Wind Engineering and Industrial Aerodynamics, 1999, 80: 207-220
[4] BOSSANYI E A. Short-term wind prediction using kalman filters[J]. Wind Engineering, 1985, 9(1): 1-8.
[5] MILLIGAN M, SCHWARTZ M, WAN Y. Statistical wind power forecasting models: Results for U.S. wind farms[C]//17th Conference on Probability and Statistics in the Atmospheric Sciences. Austin, Texas, 2003(5): 18-21.
[6] TORRES J L, GARCIA A, BLAS M D, et al. Forecast of hourly average wind speed with ARMA models in navarre(Spain) [J]. Solar Energy, 2005, 79(1): 65-77.
[7] 叶林，刘鹏. 基于经验模态分解和支持向量机的短期风电功率组合预测模型[J]. 中国电机工程学报，２０１１，３１（３１）：１０２－１０８．
YE Lin, LIU Peng. Combined model based on EMD-SVM for short-term wind power prediction[J]. Proceedings of the CSEE, 2011, 31(31): 102-108.
[8] 栗然，柯拥勤，张孝乾，等. 基于时序-支持向量机的风电场发电功率预测[J]. 中国电力，２０１２，４５（１）：６４－６８．
LI Ran, KE Yongqin, ZHANG Xiaoqian, et al. Wind power forecasting based on time series and SVM[J]. Electric Power, 2012, 45(1): 64-68.
[9] 叶林，赵永宁. 基于空间相关性的风电功率预测研究综述[J]. 电力系统自动化，２０１４，38（１４）：１２６－１３５．
YE Lin, ZHAO Yongning. A review on wind power prediction based on spatial correlation approach[J]. Automation of Electric Power Systems, 2014, 38(14): 126-135.
[10] 杨正瓴，冯勇，熊定方，等. 基于季风特性改进风电功率预测的研究展望[J]. 智能电网，２０１５，3（１）：１－７．
YANG Zhengling, FENG Yong, XIONG Dingfang, et al. Research prospects of improvement in wind power forecasting based on characteristics of monsoons [J]. Smart Grid, 2015, 3(1): 1-7.
[11] 张浩然，汪晓东． 回归最小二乘支持向量机的增量和在线式学习算法[J]． 计算机学报，２００６，２９（３）：４００－４０６．
ZHANG Haoran, WANG Xiaodong． Incremental and online learning algorithm for regression least squares support vector machine [J]． Chinese Journal of Computer, 2006, 29(3): 400-406.
[12] 孙斌，姚海涛. 基于PSO优化LSSVM的短期风速预测[J]. 电力系统保护与控制，２０１２，40（５）：８５－８９．
SUN Bin, YAO Haitao. The shortterm wind speed forecast analysis based on the PSO-LSSVM predict model[J]. Power System Protection and Control, 2012, 40(5): 85-89.
[13] 王贺，胡志坚，张翌晖，等. 基于IPSO-LSSVM的风电功率短期预测研究[J]. 电力系统保护与控制，２０１２，40（２４）：１０７－１１２．
WAN He, HU Zhijian, ZHANG Yihui. Shortterm prediction of wind power based on IPSO-LSSVM[J]. Power System Protection and Control, 2012, 40(24): 107-112.
[14] 张春晓，张涛. 基于最小二乘支持向量机和粒子群算法的两相流含油率软测量方法[J]. 中国电机工程学报，２０１０，30（２）：８６－９１．
ZHANG Chunxiao, ZHANG Tao. Soft measurement method for oil holdup of two phase flow based on leased squares support vector machine and particle swarm optimization[J]. Proceedings of the CSEE, 2010, 30(2): 86-91.
[15] 李玉军，汤晓君，刘君华. 基于粒子群优化的最小二乘支持向量机在混合气体定量分析中的应用[J]. 光谱学与光谱分析，２０１０，30（３）：７７４－７７８．
LI Yujun, TANG Xiaojun, LIU Junhua． Application of least square support vector machine based on particle swarm optimization in quantitative analysis of gas mixture [J]． Spectroscopy and Spectral Analysis, 2010, 30(3): 774-778． |
