风电场功率超短期预测算法优化研究
|
摘要
风力发电的间歇性和波动性给电网调度带来了困难与挑战,风电场功率预测是有效的解决途径。将我国北方某风电场和美国德克萨斯州某风电场作为研究对象,进行了基于风电场历史运行数据和气象数据的风电场功率超短期预测算法研究与优化。
对风电场风电机组功率曲线特性进行了理论研究,结合支持向量机非线性拟合及小样本学习的优势,提出了基于支持向量机的分段预测模型(Piecewise Support Vector Machine, PSVM),短期预测的平均相对误差(Mean Relative Error, MRE)为17.50%,较单一模型降低4.76%；采用遗传算法优化模型参数,建立了遗传算法-最小二乘支持向量机超短期预测模型(Genetic Algorithm-Least Square Support Vector Machine, GA-LSSVM);利用小波变换的时频分析方法将风电机组出力功率序列在时域和频域分解,在各组分分别建模,小波重构后输出预测功率,建立小波-最小二乘支持向量机模型(Wavelet Transform-Least Square Support Vector Machine, WT-LSSVM)。算例表明GA-LSSVM和WT-LSSVM在有效利用了支持向量机算法优势的基础上提高了预测精度,但同时带来了较大的时间代价。
神经网络具有并行处理信息和自学习的能力,其在非线性拟合方面的优势适用于预测领域。基于上述特点建立了误差反向传播神经网络(Back Propagation Neural Network, BPNN)和径向基函数神经网络(Radial Basis Function Neural Network, RBFNN)预测模型并对预测效果进行对比,算例表明RBFNN更具适用性,较BPNN的平均绝对百分比误差(Mean Absolute Percentage Error, MAPE)和均方百分比误差(Mean Square Percentage Error, MSPE)分别降低了0.30%和0.14%；对风电场输出功率的时间序列进行希尔伯特黄变换,提出希尔伯特黄-神经网络预测模型(Hilbert Huang Transform-Artificial Neural Network, HHT-ANN),对于算例多数时段,HHT-ANN模型能显著提高预测精度,MAPE由3.39%降低到1.89%,但预测精度受输入数据HHT结果的影响较大。
为实现对风电机组功率平稳且高效的预报,充分发挥最小二乘支持向量机(Least Square Support Vector Machine, LSSVM)和径向基函数神经网络(Radial Basis Function Neural Network, RBFNN)在功率预测中的优势,利用灰色关联度分析原理提出了风电场功率超短期LSSVM和RBFNN组合预测算法,其MAPE较LSSVM和RBFNN分别减少0.29%和0.40%；为提高预测算法在不同风况下的适用性,在分析研究算例全年各月风速变化特性和分布规律的基础上,将风速按风频相似原理进行分段训练权值,建立各月特征权重数据库,提出了基于风速变化特性的组合预测模型,具有普适性且时间代价较小。经算例分析MAPE为2.37%。
模型预测结果与实际出力值总存在误差,对风电机组出力进行概率性预测较单值预测更加合理。在本文中对风电场功率预测误差分布特性进行了理论研究,提出一种基于蒙特卡罗原理的预测结果不确定性分析方法,通过有效性检验验证算法的可行性；将风电场功率预测算法及不确定性分析的研究成果应用到风电场功率预测系统。
The intermittence and fluctuation of wind power have brought difficults and challenges to grid dispatch, and wind power forecasting is the effective soltuion to this problem. In this thesis, a wind farm in North China and a wind farm in Texas U.S.A. are taken as study objects for very short-term wind power forecasting algorithm optimization based upon historical operation data and meteorological data.
Based on the theoretical study on power curve characteristics of wind turbine generator systems and the nonlinear abilities of support vector machine (SVM), Piecewise Support Vector Machine (PSVM) model is proposed for the intended application. According to the study results, the Mean Relative Error (MRE) of PSVM model is17.50%, which is4.76%lower than SVM model; the Genetic Algorithm (GA) is applied in the forecasting model with the aim of parameter optimization; wind power time series data is taken as signal, and with the decomposition process of wavelet transform, the WT-LSSVM model is built up. The case study show great performance of the proposed model while huge time loss for computing.
Neural Network has the parallel processing of information and self-learning ability, which is suitable for forecasting. Based on those abilities, Back Propagation Neural Network (BPNN) model and Radial Basis Function Neural Network (RBFNN) model are developed for comparison purpose. The RBFNN forecasting model has better accuracy and wider applications than that of BPNN model. Compare to the forecasting error of BPNN, the error of RBFNN decreases0.30%(Mean Absolute Percentage Error, MAPE) and0.14%(Mean Square Percentage Error, MSPE) respectively. Hilbert Huang Transform (HHT) is utilized in wind power time series data for model training, and the HHT-ANN model is obtained based on HHT and RBF neural network, which show the potential of forecasting accuracy improvement with the MAPE decreasing from3.39%to1.89%.
To realize the steady and efficient wind power forecasting along with the advantages of both individual forecasting models, which are Least Square Support Vector Machine (LSSVM) and Radial Basis Function Neural Network (RBFNN), a very short-term hybrid wind power forecasting model is developed by means of Grey Relational Analysis. Compared to LSSVM and RBFNN, the results show0.29%and0.4%reduction on MAPE respectively. Based upon the analysis of wind speed distribution features, the weights of each single model are pre-calculated according to different wind speed classifications. This forecasting model has wider application and faster computing speed. The case study results show that the proposed hybrid model has outperformed individual model approach. Its MAPE and RMSE are2.37%and 3.79%respectively.
In addition, there are always errors in forecasting models. Therefore it is acceptable to develop the probability forecasting. The forecasting error distribution characteristics are studied and a Monte Carlo Method is proposed. Its practicability is verified. The wind power forecasting algorithm and the research results of uncertainty analysis are applied in wind power forecasting system.
对风电场风电机组功率曲线特性进行了理论研究,结合支持向量机非线性拟合及小样本学习的优势,提出了基于支持向量机的分段预测模型(Piecewise Support Vector Machine, PSVM),短期预测的平均相对误差(Mean Relative Error, MRE)为17.50%,较单一模型降低4.76%；采用遗传算法优化模型参数,建立了遗传算法-最小二乘支持向量机超短期预测模型(Genetic Algorithm-Least Square Support Vector Machine, GA-LSSVM);利用小波变换的时频分析方法将风电机组出力功率序列在时域和频域分解,在各组分分别建模,小波重构后输出预测功率,建立小波-最小二乘支持向量机模型(Wavelet Transform-Least Square Support Vector Machine, WT-LSSVM)。算例表明GA-LSSVM和WT-LSSVM在有效利用了支持向量机算法优势的基础上提高了预测精度,但同时带来了较大的时间代价。
神经网络具有并行处理信息和自学习的能力,其在非线性拟合方面的优势适用于预测领域。基于上述特点建立了误差反向传播神经网络(Back Propagation Neural Network, BPNN)和径向基函数神经网络(Radial Basis Function Neural Network, RBFNN)预测模型并对预测效果进行对比,算例表明RBFNN更具适用性,较BPNN的平均绝对百分比误差(Mean Absolute Percentage Error, MAPE)和均方百分比误差(Mean Square Percentage Error, MSPE)分别降低了0.30%和0.14%；对风电场输出功率的时间序列进行希尔伯特黄变换,提出希尔伯特黄-神经网络预测模型(Hilbert Huang Transform-Artificial Neural Network, HHT-ANN),对于算例多数时段,HHT-ANN模型能显著提高预测精度,MAPE由3.39%降低到1.89%,但预测精度受输入数据HHT结果的影响较大。
为实现对风电机组功率平稳且高效的预报,充分发挥最小二乘支持向量机(Least Square Support Vector Machine, LSSVM)和径向基函数神经网络(Radial Basis Function Neural Network, RBFNN)在功率预测中的优势,利用灰色关联度分析原理提出了风电场功率超短期LSSVM和RBFNN组合预测算法,其MAPE较LSSVM和RBFNN分别减少0.29%和0.40%；为提高预测算法在不同风况下的适用性,在分析研究算例全年各月风速变化特性和分布规律的基础上,将风速按风频相似原理进行分段训练权值,建立各月特征权重数据库,提出了基于风速变化特性的组合预测模型,具有普适性且时间代价较小。经算例分析MAPE为2.37%。
模型预测结果与实际出力值总存在误差,对风电机组出力进行概率性预测较单值预测更加合理。在本文中对风电场功率预测误差分布特性进行了理论研究,提出一种基于蒙特卡罗原理的预测结果不确定性分析方法,通过有效性检验验证算法的可行性；将风电场功率预测算法及不确定性分析的研究成果应用到风电场功率预测系统。
The intermittence and fluctuation of wind power have brought difficults and challenges to grid dispatch, and wind power forecasting is the effective soltuion to this problem. In this thesis, a wind farm in North China and a wind farm in Texas U.S.A. are taken as study objects for very short-term wind power forecasting algorithm optimization based upon historical operation data and meteorological data.
Based on the theoretical study on power curve characteristics of wind turbine generator systems and the nonlinear abilities of support vector machine (SVM), Piecewise Support Vector Machine (PSVM) model is proposed for the intended application. According to the study results, the Mean Relative Error (MRE) of PSVM model is17.50%, which is4.76%lower than SVM model; the Genetic Algorithm (GA) is applied in the forecasting model with the aim of parameter optimization; wind power time series data is taken as signal, and with the decomposition process of wavelet transform, the WT-LSSVM model is built up. The case study show great performance of the proposed model while huge time loss for computing.
Neural Network has the parallel processing of information and self-learning ability, which is suitable for forecasting. Based on those abilities, Back Propagation Neural Network (BPNN) model and Radial Basis Function Neural Network (RBFNN) model are developed for comparison purpose. The RBFNN forecasting model has better accuracy and wider applications than that of BPNN model. Compare to the forecasting error of BPNN, the error of RBFNN decreases0.30%(Mean Absolute Percentage Error, MAPE) and0.14%(Mean Square Percentage Error, MSPE) respectively. Hilbert Huang Transform (HHT) is utilized in wind power time series data for model training, and the HHT-ANN model is obtained based on HHT and RBF neural network, which show the potential of forecasting accuracy improvement with the MAPE decreasing from3.39%to1.89%.
To realize the steady and efficient wind power forecasting along with the advantages of both individual forecasting models, which are Least Square Support Vector Machine (LSSVM) and Radial Basis Function Neural Network (RBFNN), a very short-term hybrid wind power forecasting model is developed by means of Grey Relational Analysis. Compared to LSSVM and RBFNN, the results show0.29%and0.4%reduction on MAPE respectively. Based upon the analysis of wind speed distribution features, the weights of each single model are pre-calculated according to different wind speed classifications. This forecasting model has wider application and faster computing speed. The case study results show that the proposed hybrid model has outperformed individual model approach. Its MAPE and RMSE are2.37%and 3.79%respectively.
In addition, there are always errors in forecasting models. Therefore it is acceptable to develop the probability forecasting. The forecasting error distribution characteristics are studied and a Monte Carlo Method is proposed. Its practicability is verified. The wind power forecasting algorithm and the research results of uncertainty analysis are applied in wind power forecasting system.
引文
[1]The World Wind Energy Association.2011 Annual Report[R].2012
[2]中国可再生能源学会风能专业委员会.2011年风电装机容量统计[R].2012
[3]S.Kumar. China Leads Growth in Global Wind Power Capacity[R]. Washington, D.C.,2012
[4]U.S.Department of Energy. Wind Energy and Economic Development in Nebraska[EB/OL]. http://wpadev.nrel. gov/pdfs/economic_development/2009/ne_economic_development_scenarios.pdf
[5]John W. Zack. Overview of the Current Status and Future Prospects of Wind Power Production Forecasting for the ERCOT System[R]. Austin, TX: the Electric Reliability Council of Texas (ERCOT),2009
[6]Jennifer Rogers, Kevin Porter. Central Wind Power Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities[R]. Columbia, Maryland,2009
[7]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究[J].中国电机工程学报,2005,25(11)：1-5
[8]韩爽.风电场功率短期预测方法研究[D].北京：华北电力大学,2008
[9]刘永前,韩爽,胡永生.风电场出力短期预报研究综述[J].现代电力,2007,24(90)：6-11
[10]P. Coppin. The Feasibility of Wind Power Production Forecasting in the Australian Context[R]. National Electricity Market Management Company Limited, CSIRO Atmospheric Research Report,2003
[11]G. Gow. Forecasting Short-term Wind Farm Production[R]. Technical Report, Garrad Hassan and Partners Ltd., Bristol, U.K.,2003
[12]潘迪夫,刘辉,李燕飞.基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型[J].电网技术,2008,32(7)：82-86
[13]J. D. Mario, C. Daniel, R. Jesus, et al. Short-term Wind Power Forecast Based on ARX Models[J]. Journal of Energy Engineering,2007,133(3): 172-180
[14]J. Copper, A. Baciu, D. Price. Predicting Wind Farm Electricity Output with a Neural Network Empirical Modeling Approach[C]. Power and Energy Engineering Conference, Wuhan,2009
[15]P. Flores, A. Tapia, G. Tapia. Application of a Control Algorithm for Wind Speed Prediction and Active Power Generation[J]. Renewable Energy, 2005,30(4):523-536
[16]M. C. Mabel, E. Fernandez. Analysis of Wind Power Gneration and Prediction[J]. Renewable Energy,2008,33(5):986-992
[17]T. Barbounis, J. Theocharis. Locally Recurrent Neural Networks for Wind Speed Prediction Using Spatial Correlation[J]. Information Science,2007, 177(24):5775-5797
[18]A. Kusiak, H. Zheng, Z. Song. Short-term Prediction of Wind Farm Power: a Data Mining Approach[J]. IEEE Transactions on Energy Conversion,2009, 24(1):125-136
[19]A. Kusiak, H. Zheng, Z. Song. Models for Monitoring Wind Farm Power[J]. Renewable Energy,2009,34(3):583-590
[20]G. H. Riahy, M. Abedi. Short Term Wind Speed Forecasting for Wind Turbine Applications Using Linear Prediction Method[J]. Renewable Energy, 2008,33(1):35-41
[21]L. Dong, S. Gao, X. Z. Liao, et al. Short-Term Wind Power Forecasting with Combined Prediction Based on Chaotic Analysis[C]. Prz Elektrotechniczn, 2012,88(5B):35-39
[22]C. X. Fan, S. Q. Liu. Wind Speed Forecasting Method:Gray Related Weighted Combination with Revised Parameter[J]. Enrgy Proced,2011,5: 550-554
[23]J. P. S. Catalao, H. M. I. Pousinho, V. M. F. Mendes. Hybrid Wavelet-PSO-ANFIS Approach for Short-term Wind Power Forecasting in Portugal[J]. Sustainable Energy, IEEE Transactions on,2011,2(1):50-59
[24]S. Al-Yahyaia, Y. Charabib, A. Gastlia. Review of the Use of Numerical Weather Prediction (NWP) Models for Wind Energy Assessment[J]. Renewable and Sustainable Energy Reviews,2010,14(9):3192-3198
[25]N. Amjady, F. Keynia, H. Zareipour. Short-term Wind Power Forecasting Using Ridgelet Neural Network[J]. Electric Power Systems Research,2011, 81(12):2099-2107
[26]C. C. Baniotopoulos, C. Borri, T. Stathopoulos, et al. Environmental Wind Engineering and Design of Wind Energy Structures[M]. New York:Springer Wien,2011
[27]R. Blonbou. Very Short-term Wind Power Forecasting with Neural Networks and Adaptive Bayesian Learning[J]. Renewable Energy,2011,36(3): 1118-1124
[28]T. Burton. Wind Energy Handbook[M].2nd ed. Chichester, West Sussex: Wiley,2011
[29]O. Edenhofer, R. P. Madruga, Y. Sokona, et al. Renewable Energy Sources and Climate Change Mitigation:Special Report of the Intergovernmental Panel on Climate Change[M]. New York:Cambridge University Press,2012
[30]D. S. Ginley, D. V. Kahen. Fundamentals of Materials for Energy and Environmental Sustainability[M]. Cambridge; New York:Cambridge University Press,2012
[31]S. Han, J. S. Li, Y. Q. Liu. Tabu Search Algorithm Optimized ANN Model for Wind Power Prediction with NWP[J]. Enrgy Proced,2011,12:733-740
[32]T. Huang, X. Wang, L. Xi Li, et al. Ultra-Short Term Prediction of Wind Power Based on Multiples Model Extreme Leaning Machine[J]. Lecture Notes in Computer Science,2011,6677:539-547
[33]D. S. Kenney, R. Wilkinson. The Water-energy Nexus in the American West[M]. Cheltenham, U.K.; Northampton, MA:Edward Elgar Publishing, 2011
[34]J. Shi, Y. Q. Liu, Y. P. Yang, et al. Short-term Wind Power Prediction Based on Wavelet Transform-Support Vector Machine and Statistic Characteristics Analysis[C].2011 IEEE Industrial and Commercial Power Systems Technical Conference, Baltimore,2011
[35]J. A. Lee, W. C. Kolczynski, T. C. Mccandless, et al. An Objective Methodology for Configuring and Down-Selecting an NWP Ensemble for Low-level Wind Prediction[J]. Monthly Weather Review,2012,140(7): 2270-2286
[36]S. J. Mitchell, N. Lanquaye-Opoku, H. Modzelewski, et al. Comparison of Wind Speeds Obtained Using Numerical Weather Prediction Models and Topographic Exposure Indices for Predicting Windthrow in Mountainous Terrain[J]. Forest Ecol Manag,2008,254(2):193-204
[37]Q. Y. Zhao, J. Cook, Q. Xu, et al. Using Radar Wind Observations to Improve Mesoscale Numerical Weather Prediction[J]. Weather Forecast,2006, 21(4):502-522
[38]P. H. Abelson. Power From Wind Turbines[J]. Science,1993,261(5126): 1255
[39]T. G. Barbounis, J. B. Theocharis, M. C. Alexiadis, et al. Long-term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models[J]. IEEE Transactions on Energy Conversion,2006,21(1):273-284
[40]Y. Y. Hong, H. L. Chang, C. S. Chiu. Hour-ahead Wind Power and Speed Forecasting Using Simultaneous Perturbation Stochastic Approximation (SPSA) Algorithm and Neural Network with Fuzzy Inputs[J]. Energy,2010,35(9): 3870-3876
[41]H. Mori, E. Kurata. Application of Gaussian Process to Wind Speed Forecasting for Wind Power Generation[C].2008 IEEE International Conference on Sustainable Energy Technologies, Singapore,2008,956-959
[42]H. A. Nielsen, H. Madsen, T. S. Nielsen. Using Quantile Regression to Extend an Existing Wind Power Forecasting System with Probabilistic Forecasts[J]. Wind Energy,2006,9(1-2):95-108
[43]E. W.Peterson. Wind Power[J]. Science,1974,185(4150):480
[44]J. Shi, J. M. Guo, S. T. Zheng. Evaluation of Hybrid Forecasting Approaches for Wind Speed and Power Generation Time Series[J]. Renewable and Sustainable Energy Reviews,2012,16(5):3471-3480
[45]X. Wang, G. Sideratos, N. Hatziargyriou, et al. Wind Speed Forecasting for Power System Operational Planning[C].2004 International Conference on Probabilistic Methods Applied to Power Systems, Ames,2004,470-474
[46]X. L. An, D. X. Jiang, C. Liu, et al. Wind Farm Power Prediction Based on Wavelet Decomposition and Chaotic Time Series[J]. Expert Systems with Applications,2011,38(9):11280-11285
[47]J. W. Taylor, P. E. McSharry, R. Buizza. Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models[J]. IEEE Transactions Energy Conversion,2009,24(3):775-782
[48]T. V. Dawnnews. Plan B:Solar Energy, Wind Energy, Biofuels, Consumer Level Solutions[M]. Karachi:DawnNews,2011
[49]P. H. Jensen, International Solar Energy Society. Wind Energy Pocket Reference[M]. London:Earthscan,2007
[50]S. A. P. Kani, M. M. Ardehali. Very Short-term Wind Speed Prediction:A New Artificial Neural Network-Markov Chain Model[J]. Energy Conversion Manage,2011,52(1):738-745
[51]S. A. P.Kani, G. H. Riahy, D. Mazhari. An Innovative Hybrid Algorithm for Very Short-term Wind Speed Prediction Using Linear Prediction and Markov Chain Approach[J]. International Journal of Green Energy,2011,8(2): 147-62
[52]A. A. Marcus. Cross-sector Leadership for the Green Economy: Integrating Research and Practice on Sustainable Enterprise[M]. New York, NY:Pal grave Macmillan,2011
[53]S. M. Muyeen. Wind Energy Conversion Systems:Technology and Trends[M]. London; New York:Springer,2012
[54]National Center for Construction Education and Reaseach (U.S.). Introduction to Wind Energy:Trainee Guide[M].1st ed. Boston:Prentice Hall for National Center for Construction Education and Research,2011
[55]J. Shi, X. L. Qu, S. T. Zeng, et al. Short-term Wind Power Generation Forecasting:Direct Versus Indirect Arima-based Approaches[J]. International Journal of Green Energy,2011,8(1):100-112
[56]B. Wu. Power Conversion and Control of Wind Energy Systems[M]. IEEE Press series on power engineering,2011
[57]X. X. Zhu, M. G. Genton. Short-term Wind Speed Forecasting for Power System Operations[J]. International statistics Review,2012,80(1):2-23
[58]S. F. Zyga, K. G. Callister. Wind and Hydroropower Technologies Program (U.S.). Offshore Wind Energy in the U.S.:Development Strategy and Resources[M]. Hauppauge, N.Y.:Nova Science Publishers,2011
[59]Y. Kimy, M. S. Nadr, A. Bilgin. Wavelet-based Compressed Sensing Using a Gaussian Scale Mixture Model[J]. IEEE Transaction Image Process, 2012,21(6):3102-3108
[60]G. C. Leite, D. M. Ushizima, F. N. Medeiros, et al. Wavelet Analysis for Wind Fields Estimation[J]. Sensors (Basel),2010,10(6):5994-6016
[61]S. L. Zhou, M. Q. Mao, C. Liuchen, et al. Forecasting of Wind Speed Based on Wavelet Analysis and Support Vector Machine[J]. IEEE Pedg 2010: The 2nd International Symposium on Power Electronics for Distributed Generation Systems,2010,864-867
[62]A. Costa, A. Crespo, J. Navarro, et al. A Review on the Young History of the Wind Power Short-term Prediction[J]. Renewable and Sustainable Energy Reviews,2008,12(6):1725-1744
[63]A. Sfetsos. A Comparison of Various Forecasting Techniques Applied to Mean Hourly Wind Speed Time Series[J]. Renewable Energy,2000,21(1): 23-35
[64]L. Landberg, S. J. Watson. Short-term Prediction of Local Wind Conditions[J]. Boundary-Layer Meteorology,1994,70(1):171-195
[65]G. Giebel, L. Landberg, T. S. Nielsen, et al. The Zephyr Project-The Next Generation Prediction System[C]. Proceedings of European wind energy conference, Copenhagen,2001.
[66]I. Marti, T. S.Nielsen, H. Madsen, et al. Prediction Models in Complex Terrain[C]. Proceedings of European wind energy conference, Copenhagen, 2001.
[67]A. Costa, A. CRESPO, E. Feitosa. Mathematical and Physical Wind Power Forecasting Models:A Proposal for the UPMPREDICTION Project[C]. Proceedings of the second joint action symposium on windforecasting techniques, International Energy Agency (IEA), Lyngby.2004.
[68]H. Madsen, G. Kariniotakis, H. A. Nielsen, et al. A protocol for standardizing the performance evaluation of short-term wind power prediction models[C]. Proceedings of the 2004 Global Windpower Conference and Exhibition.2004.3
[69]P. Pinson, G. Kariniotakis. On-line Assessment of Prediction Risk for Wind Power Production Forecasts[J]. Wind Energy,2004,7(2):119-132
[70]J.L. Torres, A. Garcia, M. D. Blas, et al. Forecast of Hourly Average Wind Speed with ARMA Models in Navarre (Spain)[J]. Solar Energy,2005,79(1): 65-77
[71]朴金姬.神经网络算法在风电场功率预测中的应用研究[D].华北电力大学(北京),2011
[72]李文良,卫志农,孙国强.基于改进空间相关法和径向基神经网络的风电场短期风速分时预测模型[J].电力自动化设备,2009,29(6)：89-92
[73]管胜利.基于局部波分解及时间序列的风电场风速预测研究[J].华北电力技术,2009(1)：10-13
[74]杨志凌.风电场功率短期预测方法优化的研究[D].华北电力大学(北京),2011
[75]陶玉飞,李伟宏,杨喜峰.风电场风速预测模型研究[J].电网与清洁 能源,2009,25(3)：167-172
[76]刘纯.中国电科院风电功率预测系统[R].北京：中国电力科学研究院新能源研究所,2008
[77]中国气象局风能太阳能资源评估中心.风电场风电功率预报[EB/OL]. http://cwera.cma.gov.cn/Website/index.php?ChannelID=144&WCHID=128
[78]A. Bracale, G. Carpinelli, D. Proto, et al. New Approaches for Very Short-term Steady-state Analysis of an Electrical Distribution System with Wind Farms[J]. Energies,2010,3(4):650-670
[79]L. Landberg. Short-term Prediction of the Power Production from Wind Farms[J]. Journal of Wind Engineering and Industrial Aerodynamics,1999, 80(1-2):207-220
[80]C. Potter, M. Negnevitsky. Very Short-term Wind Forecasting for Tasmanian Power Generation[J]. IEEE Power Engineering Society General Meeting,2006,3620-3623
[81]M. Mohandes, T. Halawani, S. Rehman, et al. Support Vector Machines for Wind Speed Prediction[J]. Renewable Energy,2004,29(6):939-947
[82]S. R. Gunn. Support Vector Machines Classification and Regression[D], University of Southampton,1998
[83]邓乃杨,田英杰.数据挖掘中的新方法-支持向量机[M].北京：科学出版社,2004
[84]C. Hsu, C. Lin. A Comparison of Methods for Multiclass Support Vector Machines[J]. IEEE Transactions on Neural Networks,2002,13(2):415-425
[85]H. Drucker, C. J. C. Bureges, L. Kaufman, et al. Support Vector Regression Machines[R]. Advances in Neural Information Processing Systems 9, Cambridge,1997:155-161
[86]J. P. Molly. Wind Energy[M]. Muller,1990
[87]勒古里雷斯D.风力机的理论与设计[M].北京：机械工业出版社,1987
[88]史洁.基于支持向量机的风电场功率短期预测方法研究[D].华北电力大学(北京),2009
[89]陈国良.遗传算法及其应用[M].人民邮电出版社,1996
[90]雷英杰.MATLAB遗传算法工具箱及应用[M].西安电子科技大学出版社,2005
[91]A. Takotomamonjy. Non-parametric Regression with Wavelet Kernels[J]. John Wiley and Sons Ltd,2005,21(2):153-163
[92]A. Palomares, M. Castro. Short-term Wind Prediction Model at the Strait of Gibraltar Based on a Perfect Prognosis Statistical Downscaling Method[C], Proceedings of European Wind Energy Conference, Madrid,2003
[93]P. Flores, A. Tapia, G. Tapia. Application of a Control Algorithm for Wind Speed Prediction and Active Power Generation[J]. Renewable Energy, 2005,30(4):523-536
[94]H. Wegley, M. Kosprok, W. Formica. Subhourly Wind Forecasting Techniques for Wind Turbine Operations[R]. Pacific Northwest Laboratory, 1984
[95]冯双磊,王伟胜,刘纯.风电场功率预测物理方法研究[J].中国电机工程学报,2010,30(2)：1-6
[96]C. Notis, P. N. Laborator. Learning to Forecast Wind at Remote Sites for Wind Energy Applications:Final Report[M]. Pacific Northwest Laboratory, 1983
[97]H. Geerts. Short Range Prediction of Wind Speeds:A System-theoretic Approach[C]. Proceedings of the European Wind Energy conference,1984, Hamburg
[98]Y. Zhang, K. W. Chan. The Impact of Wind Forecasting in Power System Reliability[C]. DRPT2008, Nanjing.
[99]何育,高山,陈昊.基于ARMA-ARCH模型的风电场风速预测研究[J].江苏电机工程,2009,03)：1-4
[100]T. El-fouly, E. El-saadany, M. Salama. Grey Predictor for Wind Energy Conversion Systems Output Power Prediction[J]. IEEE Transactions on Power Systems,2006,21(3):1450-1452
[101]M. Negnevitsky, P. Johnson. Very Short Term Wind Power Prediction:A Data Mining Approach[C], IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century,20-24 July 2008:1-3
[102]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究[J].中国电机工程学报,2005,25(11)：1-5
[103]K. Sreelakshmi, P. R. Kumar. Short Term Wind Speed Prediction Using Support Vector Machine Model[J]. WSEAS Transactions on Computers,2008, 7(11):1828-37
[104]S. A. P. Kani, G. H. Riahy. A New ANN-based Methodology for Very Short-term Wind Speed Prediction Using Markov Chain Approach[J].2008 IEEE Electrical Power and Energy Conference,2008,566-571
[105]D. Armentrout, P. Armentrout. Wind Energy[M]. Rourke Publishing, 2008
[106]R. Bowden. Energy Sources:the Impact of Science and Technology[M]. Pleasantville, NY:Gareth Stevens Publication,2010
[107]A. Bridgewater, G. Bridgewater. Renewable Energy for Your Home: Using Off-Grid Energy to Reduce Your Footprint, Lower Your Bills and Be More Self-Sufficient[M]. Berkeley, CA:Ulysses Press,2009
[108]王健,严干贵,宋薇等.风电功率预测技术综述[J].东北电力大学学报,2011,3：20-24
[109]R. Degunther. Alternative Energy for Dummies[M]. Hoboken, NJ:Wiley, 2009
[110]European Power Electronics and Drives Association. Wind Energy Chapter,2008 proceedings of the EPE Wind Energy Chapter-1st seminar:Delft, Netherlands 27-28 March 2008[M]. United States:IEEE,2008
[111]European Wind Energy Association. Wind Energy-the Facts:A Guide to the Technology, Economics and Future of Wind Power[M]. London; Sterling, VA:Earthscan,2009
[112]R. B. Gupta. Hydrogen Fuel:Production, Transport, and Storage[M]. Boca Raton:CRC Press,2009
[113]M. Junginger, W. V. Sark, A. Faaij. Technological Learning in the Energy Sector:Lessons for Policy, Industry and Science[M]. Cheltenham, UK, Northampton, MA:Edward Elgar,2010
[114]C. Lever-tracy. Routledge Handbook of Climate Change and Society[M]. New York:Routledge,2010
[115]尹炼,刘文洲.风力发电.北京：中国电力出版社,2002
[116]L. L. Li, M. H. Wang, F. F. Zhu, et al. Wind Power Forecasting Based on Time Series and Neural Network[J]. Proceedings of International Symposium on Computer Science and Computational Technology (Iscsct 2009),2009, 293-297
[117]T. J. Weller. Evaluating Preconstruction Sampling Regimes for Assessing Patterns of Bat Activity at a Wind Energy Development in Southern California[M]. Sacramento, California Energy Commission,2008
[118]Y. Q. Liu, J. Shi, Y. P. Yang, et al. Piecewise Support Vector Machine Model for Short Term Wind Power Prediction[J]. International Journal of Green Energy,2009,6(5):479-489
[119]M. Kittipong, W. J. Lee, C. Yingvivatanapong, et al. An Integration of ANN Wind Power Estimation into UC Considering the Forecasting Uncertainty [J].2005 IEEE Industrial and Commercial Power Systems Technical Conference, Conference Record,2005,116-119
[120]I. J. Ramirez-rosado, L. A. Fernandez-jimenez, C. Monteiro. Artificial Neural Network Models for Wind Power Short-term Forecasting Using Weather Predictions[J]. Proceedings of the 25th IASTED International Conference on Modelling, Identification, and Control,2006,128-132
[121]田雨波.混合神经网络技术[M].科学出版社,2009
[122]T. S. Nielsen, H. Madsen, J. Tofting. Experiences with Statistical Methods for Wind Power Prediction[C]. Wind Energy Conference, Nice,1999 [123] T. Burton, D. Sharpe, N. Jenkins, et al. Wind Energy:Handbook[M]. Wiley Online Library,2001
[124]J. M. Bates, C. W. J. Granger. The Combination of Forecasts[J]. OR, 1969,451-468
[125]D. Legourieres, P. South. Wind Power Plants-theory and Design[M]. Pergamon Press (Oxford and New York),1982
[126]孙川永.风电场风电功率短期预报技术研究[D].兰州大学,2009
[127]张德丰.MATLAB神经网络应用设计/MATLAB工程应用书库[M].机械工业出版社,2009
[128]牛东晓.电力负荷预测技术及其应用[M].中国电力出版社,1999
[129]N. E. Huang, Z. Shen, S. R. Long. A New View of Nonlinear Water Waves:The Hilbert Spectrum[J]. Annual Review of Fluid Mechanics,1999, 31(1):417-457
[130]N. E. Huang, Z. Shen, S. R. Long, et al. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis[J]. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences,1998,454(1971):903-995
[131]纪跃波,秦树人,柏林,仲佑明.有限区间信号边界效应问题的研究[J].振动与冲击,2002,(4)：108-111
[132]Z. Wu, N. E. Huang. A Study of the Characteristics of White Noise Using the Empirical Mode Decomposition Method[J]. Proceedings of the Royal Society of London Series A:Mathematical, Physical and Engineering Sciences, 2004,460(2046):1597-1611
[133]李俊峰,戴文战.基于灰色关联度和神经网络的变权组合预测模型研究[J].2006中国控制与决策学术年会论文集,2006
[134]Y. S. Huang, H. J.Zhang. Grey Correlation in the Combined Weights of Power Load Forecasting Application[J]. Proceedings of the International Conference on Information Management, Innovation Management and Industrial Engineering,2008, (1):292-295
[135]张青.基于神经网络最优组合预测方法的应用研究[J].系统工程理论与实践,2001,21(9)：90-93
[136]D. W. Bunn. Statistical Efficiency in the Linear Combination of Forecasts[J]. International Journal of Forecasting,1985,1(2):151-163
[137]李学全,李春生.一种改进的模糊变权重组合预测方法[J].中南工业大学学报,2003,34(006)：708-710
[138]周宗放,唐小我,路应金.正交条件下变权组合预测的误差界[J].管理工程学报,2003,17(003)：8-11
[139]宫靖远,贺德馨,孙如林,吴运东.风电场工程技术手册[M].北京：机械工业出版社,2004
[140]陈仲生.基于Matlab7.0的统计信息处理[M].长沙：湖南科学技术出版社,2005
[141]G. Heydt, N. Farahbakhshian. A Method for the Forecasting of the Probability Density Function of Power System Loads[J]. IEEE Transaction on power apparatus and systems,1981,100(12):9
[142]D. K. Rana Weera, G. G. Karady, R. G. Farmer. A Probabilistic Approach to Handle Uncertainties in Load Forecasting[C]. Proceedings of the the 57th Annual American Power Conference,1995, Chicago. IL. USA
[143]穆钢,侯凯元,杨右军等.负荷预报中符合规律性评价方法的研究[J].中国电机工程学报,2001,21(10)：96-101
[144]W. C. M. S. Charytoniuk, P. Kotas. Demand Forecasting in Power Distribution Systems Using Nonparametric Probability Density Estimation[J]. IEEE Transactions on Power Systems,1999,14(4):1200-1206
[145]D. H. Matthias Lange. Relating the Uncertainty of Short-term Wind Speed Predictions to Meteorological Situations with Methods from Synoptic Climatology[M]. European Wind Energy Conference & Exhibition EWEC. Madrid, Spain.2003
[146]J. B. Bremnes. Probabilistic wind power forecasts by means of statistical model[C]. Proceedings of the IEA R&D Wind Annex XI Joint Action Symposium on Wind Forecasting Techniques,2002, Norrkoping
[147]M. Kittipong, W. J. Lee, J. R. Liao, et al. An Integration of ANN Wind Power Estimation into Unit Commitment Considering the Forecasting Uncertainty [J]. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2007,43(6):1441-1448
[148]P. Pinson G. N. Wind Power Forecasting Using Fuzzy Neural Networks Enhanced with On-line Prediction Risk Assessment[M]. IEEE Bologna Power Technology Conference,2003
[149]周松林,茆美琴,苏建徽.风电功率短期预测及非参数区间估计[J].中国电机工程学报,2011,31(25)：10-16[150]范高锋,王伟胜,刘纯,戴慧珠.基于人工神经网络的风电功率预测[J].中国电机工程学报,2008,28(34)：118-123
[151]J. S. Liu. Monte Carlo Strategies in Scientific Computing[M]. Springer Verlag,2008
[152]K. Binder. Monte-Carlo Methods[M]. Wiley Online Library,1979
[153]盛骤,谢式千,潘承毅.概率论与数理统计[M].北京：高等教育出版社,2001
[154]杨文佳,康重庆,夏清.基于预测误差分布特性统计分析的概率性短期负荷预测[J].电力系统自动化,2006,30(19)：47-52
[155]I. L. L.Troen. Short Term Prediction of Local Wind Conditions[C]. Proceedings of the European community wind energy conference,1990, Madrid, Spain
[156]H. D. T. Beyer, J. Hausmann, M. Hoffmann, et al. Short Term Prediction of Wind Speed and Power Output of a Wind Turbine with Neural Networks[C]. Proceedings of the European wind energy conference,1994, Thessaloniki-Macedonia, Greece
[157]H. H. D. Beyer, H. Mellingoff, K. Monnich, et al. Forecast of Regional Power Output of Wind Turbines[C]. Proceedings of the European wind energy conference,1999, Nice
[158]E. E. A. Kalnay. The NCEP/NCAR 40-year Reanalysis Project[J]. Bullectin of the American Meteorological Society 1996,77(3):437-471
[159]O. R. J. Sanchezi, C. Usaola, et al. Sipreolico-a Wind Power Prediction System Based On Flexible Combination Of Dynamic Models Application To The Spanish Power System[M]. First IEA Joint Action Symposium on Wind Forecasting Techniques. Norrkoping, Sweden.2002
[160]G. D. J. GRELL, D. Stanffer. A Description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5)[R]. NCAR Technical Note,1995
[161]J. M. C. Jorgensen, B. O. Gallag-oir. HIRPOM:Description of an Operational Numerical Wind Power Prediction Model for Large Scale Integration of on and Offshore Wind Power in Denmard[C]. Proceedings of the Global Windpower Conference and Exhibition,2002, Paris, France
[162]G. Gow. Short Term Wind Forecasting[C]. Proceedings of the first joint action symposium on wind forecasting techniques, International Energy Agency (IEA),2002, Norrkoping
[163]G. M. D. Kariniotaks, J. Moussafir, R. Chevallaz-perrier, et al. ANEMOS: Development of a Next Generation Wind Power Forecasting Systerm for the Large-Scale Integration of Onshore&Offshore Wind Farms[C]. Proceedings of the European wind energy conference,2003, Madri
[2]中国可再生能源学会风能专业委员会.2011年风电装机容量统计[R].2012
[3]S.Kumar. China Leads Growth in Global Wind Power Capacity[R]. Washington, D.C.,2012
[4]U.S.Department of Energy. Wind Energy and Economic Development in Nebraska[EB/OL]. http://wpadev.nrel. gov/pdfs/economic_development/2009/ne_economic_development_scenarios.pdf
[5]John W. Zack. Overview of the Current Status and Future Prospects of Wind Power Production Forecasting for the ERCOT System[R]. Austin, TX: the Electric Reliability Council of Texas (ERCOT),2009
[6]Jennifer Rogers, Kevin Porter. Central Wind Power Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities[R]. Columbia, Maryland,2009
[7]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究[J].中国电机工程学报,2005,25(11)：1-5
[8]韩爽.风电场功率短期预测方法研究[D].北京：华北电力大学,2008
[9]刘永前,韩爽,胡永生.风电场出力短期预报研究综述[J].现代电力,2007,24(90)：6-11
[10]P. Coppin. The Feasibility of Wind Power Production Forecasting in the Australian Context[R]. National Electricity Market Management Company Limited, CSIRO Atmospheric Research Report,2003
[11]G. Gow. Forecasting Short-term Wind Farm Production[R]. Technical Report, Garrad Hassan and Partners Ltd., Bristol, U.K.,2003
[12]潘迪夫,刘辉,李燕飞.基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型[J].电网技术,2008,32(7)：82-86
[13]J. D. Mario, C. Daniel, R. Jesus, et al. Short-term Wind Power Forecast Based on ARX Models[J]. Journal of Energy Engineering,2007,133(3): 172-180
[14]J. Copper, A. Baciu, D. Price. Predicting Wind Farm Electricity Output with a Neural Network Empirical Modeling Approach[C]. Power and Energy Engineering Conference, Wuhan,2009
[15]P. Flores, A. Tapia, G. Tapia. Application of a Control Algorithm for Wind Speed Prediction and Active Power Generation[J]. Renewable Energy, 2005,30(4):523-536
[16]M. C. Mabel, E. Fernandez. Analysis of Wind Power Gneration and Prediction[J]. Renewable Energy,2008,33(5):986-992
[17]T. Barbounis, J. Theocharis. Locally Recurrent Neural Networks for Wind Speed Prediction Using Spatial Correlation[J]. Information Science,2007, 177(24):5775-5797
[18]A. Kusiak, H. Zheng, Z. Song. Short-term Prediction of Wind Farm Power: a Data Mining Approach[J]. IEEE Transactions on Energy Conversion,2009, 24(1):125-136
[19]A. Kusiak, H. Zheng, Z. Song. Models for Monitoring Wind Farm Power[J]. Renewable Energy,2009,34(3):583-590
[20]G. H. Riahy, M. Abedi. Short Term Wind Speed Forecasting for Wind Turbine Applications Using Linear Prediction Method[J]. Renewable Energy, 2008,33(1):35-41
[21]L. Dong, S. Gao, X. Z. Liao, et al. Short-Term Wind Power Forecasting with Combined Prediction Based on Chaotic Analysis[C]. Prz Elektrotechniczn, 2012,88(5B):35-39
[22]C. X. Fan, S. Q. Liu. Wind Speed Forecasting Method:Gray Related Weighted Combination with Revised Parameter[J]. Enrgy Proced,2011,5: 550-554
[23]J. P. S. Catalao, H. M. I. Pousinho, V. M. F. Mendes. Hybrid Wavelet-PSO-ANFIS Approach for Short-term Wind Power Forecasting in Portugal[J]. Sustainable Energy, IEEE Transactions on,2011,2(1):50-59
[24]S. Al-Yahyaia, Y. Charabib, A. Gastlia. Review of the Use of Numerical Weather Prediction (NWP) Models for Wind Energy Assessment[J]. Renewable and Sustainable Energy Reviews,2010,14(9):3192-3198
[25]N. Amjady, F. Keynia, H. Zareipour. Short-term Wind Power Forecasting Using Ridgelet Neural Network[J]. Electric Power Systems Research,2011, 81(12):2099-2107
[26]C. C. Baniotopoulos, C. Borri, T. Stathopoulos, et al. Environmental Wind Engineering and Design of Wind Energy Structures[M]. New York:Springer Wien,2011
[27]R. Blonbou. Very Short-term Wind Power Forecasting with Neural Networks and Adaptive Bayesian Learning[J]. Renewable Energy,2011,36(3): 1118-1124
[28]T. Burton. Wind Energy Handbook[M].2nd ed. Chichester, West Sussex: Wiley,2011
[29]O. Edenhofer, R. P. Madruga, Y. Sokona, et al. Renewable Energy Sources and Climate Change Mitigation:Special Report of the Intergovernmental Panel on Climate Change[M]. New York:Cambridge University Press,2012
[30]D. S. Ginley, D. V. Kahen. Fundamentals of Materials for Energy and Environmental Sustainability[M]. Cambridge; New York:Cambridge University Press,2012
[31]S. Han, J. S. Li, Y. Q. Liu. Tabu Search Algorithm Optimized ANN Model for Wind Power Prediction with NWP[J]. Enrgy Proced,2011,12:733-740
[32]T. Huang, X. Wang, L. Xi Li, et al. Ultra-Short Term Prediction of Wind Power Based on Multiples Model Extreme Leaning Machine[J]. Lecture Notes in Computer Science,2011,6677:539-547
[33]D. S. Kenney, R. Wilkinson. The Water-energy Nexus in the American West[M]. Cheltenham, U.K.; Northampton, MA:Edward Elgar Publishing, 2011
[34]J. Shi, Y. Q. Liu, Y. P. Yang, et al. Short-term Wind Power Prediction Based on Wavelet Transform-Support Vector Machine and Statistic Characteristics Analysis[C].2011 IEEE Industrial and Commercial Power Systems Technical Conference, Baltimore,2011
[35]J. A. Lee, W. C. Kolczynski, T. C. Mccandless, et al. An Objective Methodology for Configuring and Down-Selecting an NWP Ensemble for Low-level Wind Prediction[J]. Monthly Weather Review,2012,140(7): 2270-2286
[36]S. J. Mitchell, N. Lanquaye-Opoku, H. Modzelewski, et al. Comparison of Wind Speeds Obtained Using Numerical Weather Prediction Models and Topographic Exposure Indices for Predicting Windthrow in Mountainous Terrain[J]. Forest Ecol Manag,2008,254(2):193-204
[37]Q. Y. Zhao, J. Cook, Q. Xu, et al. Using Radar Wind Observations to Improve Mesoscale Numerical Weather Prediction[J]. Weather Forecast,2006, 21(4):502-522
[38]P. H. Abelson. Power From Wind Turbines[J]. Science,1993,261(5126): 1255
[39]T. G. Barbounis, J. B. Theocharis, M. C. Alexiadis, et al. Long-term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models[J]. IEEE Transactions on Energy Conversion,2006,21(1):273-284
[40]Y. Y. Hong, H. L. Chang, C. S. Chiu. Hour-ahead Wind Power and Speed Forecasting Using Simultaneous Perturbation Stochastic Approximation (SPSA) Algorithm and Neural Network with Fuzzy Inputs[J]. Energy,2010,35(9): 3870-3876
[41]H. Mori, E. Kurata. Application of Gaussian Process to Wind Speed Forecasting for Wind Power Generation[C].2008 IEEE International Conference on Sustainable Energy Technologies, Singapore,2008,956-959
[42]H. A. Nielsen, H. Madsen, T. S. Nielsen. Using Quantile Regression to Extend an Existing Wind Power Forecasting System with Probabilistic Forecasts[J]. Wind Energy,2006,9(1-2):95-108
[43]E. W.Peterson. Wind Power[J]. Science,1974,185(4150):480
[44]J. Shi, J. M. Guo, S. T. Zheng. Evaluation of Hybrid Forecasting Approaches for Wind Speed and Power Generation Time Series[J]. Renewable and Sustainable Energy Reviews,2012,16(5):3471-3480
[45]X. Wang, G. Sideratos, N. Hatziargyriou, et al. Wind Speed Forecasting for Power System Operational Planning[C].2004 International Conference on Probabilistic Methods Applied to Power Systems, Ames,2004,470-474
[46]X. L. An, D. X. Jiang, C. Liu, et al. Wind Farm Power Prediction Based on Wavelet Decomposition and Chaotic Time Series[J]. Expert Systems with Applications,2011,38(9):11280-11285
[47]J. W. Taylor, P. E. McSharry, R. Buizza. Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models[J]. IEEE Transactions Energy Conversion,2009,24(3):775-782
[48]T. V. Dawnnews. Plan B:Solar Energy, Wind Energy, Biofuels, Consumer Level Solutions[M]. Karachi:DawnNews,2011
[49]P. H. Jensen, International Solar Energy Society. Wind Energy Pocket Reference[M]. London:Earthscan,2007
[50]S. A. P. Kani, M. M. Ardehali. Very Short-term Wind Speed Prediction:A New Artificial Neural Network-Markov Chain Model[J]. Energy Conversion Manage,2011,52(1):738-745
[51]S. A. P.Kani, G. H. Riahy, D. Mazhari. An Innovative Hybrid Algorithm for Very Short-term Wind Speed Prediction Using Linear Prediction and Markov Chain Approach[J]. International Journal of Green Energy,2011,8(2): 147-62
[52]A. A. Marcus. Cross-sector Leadership for the Green Economy: Integrating Research and Practice on Sustainable Enterprise[M]. New York, NY:Pal grave Macmillan,2011
[53]S. M. Muyeen. Wind Energy Conversion Systems:Technology and Trends[M]. London; New York:Springer,2012
[54]National Center for Construction Education and Reaseach (U.S.). Introduction to Wind Energy:Trainee Guide[M].1st ed. Boston:Prentice Hall for National Center for Construction Education and Research,2011
[55]J. Shi, X. L. Qu, S. T. Zeng, et al. Short-term Wind Power Generation Forecasting:Direct Versus Indirect Arima-based Approaches[J]. International Journal of Green Energy,2011,8(1):100-112
[56]B. Wu. Power Conversion and Control of Wind Energy Systems[M]. IEEE Press series on power engineering,2011
[57]X. X. Zhu, M. G. Genton. Short-term Wind Speed Forecasting for Power System Operations[J]. International statistics Review,2012,80(1):2-23
[58]S. F. Zyga, K. G. Callister. Wind and Hydroropower Technologies Program (U.S.). Offshore Wind Energy in the U.S.:Development Strategy and Resources[M]. Hauppauge, N.Y.:Nova Science Publishers,2011
[59]Y. Kimy, M. S. Nadr, A. Bilgin. Wavelet-based Compressed Sensing Using a Gaussian Scale Mixture Model[J]. IEEE Transaction Image Process, 2012,21(6):3102-3108
[60]G. C. Leite, D. M. Ushizima, F. N. Medeiros, et al. Wavelet Analysis for Wind Fields Estimation[J]. Sensors (Basel),2010,10(6):5994-6016
[61]S. L. Zhou, M. Q. Mao, C. Liuchen, et al. Forecasting of Wind Speed Based on Wavelet Analysis and Support Vector Machine[J]. IEEE Pedg 2010: The 2nd International Symposium on Power Electronics for Distributed Generation Systems,2010,864-867
[62]A. Costa, A. Crespo, J. Navarro, et al. A Review on the Young History of the Wind Power Short-term Prediction[J]. Renewable and Sustainable Energy Reviews,2008,12(6):1725-1744
[63]A. Sfetsos. A Comparison of Various Forecasting Techniques Applied to Mean Hourly Wind Speed Time Series[J]. Renewable Energy,2000,21(1): 23-35
[64]L. Landberg, S. J. Watson. Short-term Prediction of Local Wind Conditions[J]. Boundary-Layer Meteorology,1994,70(1):171-195
[65]G. Giebel, L. Landberg, T. S. Nielsen, et al. The Zephyr Project-The Next Generation Prediction System[C]. Proceedings of European wind energy conference, Copenhagen,2001.
[66]I. Marti, T. S.Nielsen, H. Madsen, et al. Prediction Models in Complex Terrain[C]. Proceedings of European wind energy conference, Copenhagen, 2001.
[67]A. Costa, A. CRESPO, E. Feitosa. Mathematical and Physical Wind Power Forecasting Models:A Proposal for the UPMPREDICTION Project[C]. Proceedings of the second joint action symposium on windforecasting techniques, International Energy Agency (IEA), Lyngby.2004.
[68]H. Madsen, G. Kariniotakis, H. A. Nielsen, et al. A protocol for standardizing the performance evaluation of short-term wind power prediction models[C]. Proceedings of the 2004 Global Windpower Conference and Exhibition.2004.3
[69]P. Pinson, G. Kariniotakis. On-line Assessment of Prediction Risk for Wind Power Production Forecasts[J]. Wind Energy,2004,7(2):119-132
[70]J.L. Torres, A. Garcia, M. D. Blas, et al. Forecast of Hourly Average Wind Speed with ARMA Models in Navarre (Spain)[J]. Solar Energy,2005,79(1): 65-77
[71]朴金姬.神经网络算法在风电场功率预测中的应用研究[D].华北电力大学(北京),2011
[72]李文良,卫志农,孙国强.基于改进空间相关法和径向基神经网络的风电场短期风速分时预测模型[J].电力自动化设备,2009,29(6)：89-92
[73]管胜利.基于局部波分解及时间序列的风电场风速预测研究[J].华北电力技术,2009(1)：10-13
[74]杨志凌.风电场功率短期预测方法优化的研究[D].华北电力大学(北京),2011
[75]陶玉飞,李伟宏,杨喜峰.风电场风速预测模型研究[J].电网与清洁 能源,2009,25(3)：167-172
[76]刘纯.中国电科院风电功率预测系统[R].北京：中国电力科学研究院新能源研究所,2008
[77]中国气象局风能太阳能资源评估中心.风电场风电功率预报[EB/OL]. http://cwera.cma.gov.cn/Website/index.php?ChannelID=144&WCHID=128
[78]A. Bracale, G. Carpinelli, D. Proto, et al. New Approaches for Very Short-term Steady-state Analysis of an Electrical Distribution System with Wind Farms[J]. Energies,2010,3(4):650-670
[79]L. Landberg. Short-term Prediction of the Power Production from Wind Farms[J]. Journal of Wind Engineering and Industrial Aerodynamics,1999, 80(1-2):207-220
[80]C. Potter, M. Negnevitsky. Very Short-term Wind Forecasting for Tasmanian Power Generation[J]. IEEE Power Engineering Society General Meeting,2006,3620-3623
[81]M. Mohandes, T. Halawani, S. Rehman, et al. Support Vector Machines for Wind Speed Prediction[J]. Renewable Energy,2004,29(6):939-947
[82]S. R. Gunn. Support Vector Machines Classification and Regression[D], University of Southampton,1998
[83]邓乃杨,田英杰.数据挖掘中的新方法-支持向量机[M].北京：科学出版社,2004
[84]C. Hsu, C. Lin. A Comparison of Methods for Multiclass Support Vector Machines[J]. IEEE Transactions on Neural Networks,2002,13(2):415-425
[85]H. Drucker, C. J. C. Bureges, L. Kaufman, et al. Support Vector Regression Machines[R]. Advances in Neural Information Processing Systems 9, Cambridge,1997:155-161
[86]J. P. Molly. Wind Energy[M]. Muller,1990
[87]勒古里雷斯D.风力机的理论与设计[M].北京：机械工业出版社,1987
[88]史洁.基于支持向量机的风电场功率短期预测方法研究[D].华北电力大学(北京),2009
[89]陈国良.遗传算法及其应用[M].人民邮电出版社,1996
[90]雷英杰.MATLAB遗传算法工具箱及应用[M].西安电子科技大学出版社,2005
[91]A. Takotomamonjy. Non-parametric Regression with Wavelet Kernels[J]. John Wiley and Sons Ltd,2005,21(2):153-163
[92]A. Palomares, M. Castro. Short-term Wind Prediction Model at the Strait of Gibraltar Based on a Perfect Prognosis Statistical Downscaling Method[C], Proceedings of European Wind Energy Conference, Madrid,2003
[93]P. Flores, A. Tapia, G. Tapia. Application of a Control Algorithm for Wind Speed Prediction and Active Power Generation[J]. Renewable Energy, 2005,30(4):523-536
[94]H. Wegley, M. Kosprok, W. Formica. Subhourly Wind Forecasting Techniques for Wind Turbine Operations[R]. Pacific Northwest Laboratory, 1984
[95]冯双磊,王伟胜,刘纯.风电场功率预测物理方法研究[J].中国电机工程学报,2010,30(2)：1-6
[96]C. Notis, P. N. Laborator. Learning to Forecast Wind at Remote Sites for Wind Energy Applications:Final Report[M]. Pacific Northwest Laboratory, 1983
[97]H. Geerts. Short Range Prediction of Wind Speeds:A System-theoretic Approach[C]. Proceedings of the European Wind Energy conference,1984, Hamburg
[98]Y. Zhang, K. W. Chan. The Impact of Wind Forecasting in Power System Reliability[C]. DRPT2008, Nanjing.
[99]何育,高山,陈昊.基于ARMA-ARCH模型的风电场风速预测研究[J].江苏电机工程,2009,03)：1-4
[100]T. El-fouly, E. El-saadany, M. Salama. Grey Predictor for Wind Energy Conversion Systems Output Power Prediction[J]. IEEE Transactions on Power Systems,2006,21(3):1450-1452
[101]M. Negnevitsky, P. Johnson. Very Short Term Wind Power Prediction:A Data Mining Approach[C], IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century,20-24 July 2008:1-3
[102]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究[J].中国电机工程学报,2005,25(11)：1-5
[103]K. Sreelakshmi, P. R. Kumar. Short Term Wind Speed Prediction Using Support Vector Machine Model[J]. WSEAS Transactions on Computers,2008, 7(11):1828-37
[104]S. A. P. Kani, G. H. Riahy. A New ANN-based Methodology for Very Short-term Wind Speed Prediction Using Markov Chain Approach[J].2008 IEEE Electrical Power and Energy Conference,2008,566-571
[105]D. Armentrout, P. Armentrout. Wind Energy[M]. Rourke Publishing, 2008
[106]R. Bowden. Energy Sources:the Impact of Science and Technology[M]. Pleasantville, NY:Gareth Stevens Publication,2010
[107]A. Bridgewater, G. Bridgewater. Renewable Energy for Your Home: Using Off-Grid Energy to Reduce Your Footprint, Lower Your Bills and Be More Self-Sufficient[M]. Berkeley, CA:Ulysses Press,2009
[108]王健,严干贵,宋薇等.风电功率预测技术综述[J].东北电力大学学报,2011,3：20-24
[109]R. Degunther. Alternative Energy for Dummies[M]. Hoboken, NJ:Wiley, 2009
[110]European Power Electronics and Drives Association. Wind Energy Chapter,2008 proceedings of the EPE Wind Energy Chapter-1st seminar:Delft, Netherlands 27-28 March 2008[M]. United States:IEEE,2008
[111]European Wind Energy Association. Wind Energy-the Facts:A Guide to the Technology, Economics and Future of Wind Power[M]. London; Sterling, VA:Earthscan,2009
[112]R. B. Gupta. Hydrogen Fuel:Production, Transport, and Storage[M]. Boca Raton:CRC Press,2009
[113]M. Junginger, W. V. Sark, A. Faaij. Technological Learning in the Energy Sector:Lessons for Policy, Industry and Science[M]. Cheltenham, UK, Northampton, MA:Edward Elgar,2010
[114]C. Lever-tracy. Routledge Handbook of Climate Change and Society[M]. New York:Routledge,2010
[115]尹炼,刘文洲.风力发电.北京：中国电力出版社,2002
[116]L. L. Li, M. H. Wang, F. F. Zhu, et al. Wind Power Forecasting Based on Time Series and Neural Network[J]. Proceedings of International Symposium on Computer Science and Computational Technology (Iscsct 2009),2009, 293-297
[117]T. J. Weller. Evaluating Preconstruction Sampling Regimes for Assessing Patterns of Bat Activity at a Wind Energy Development in Southern California[M]. Sacramento, California Energy Commission,2008
[118]Y. Q. Liu, J. Shi, Y. P. Yang, et al. Piecewise Support Vector Machine Model for Short Term Wind Power Prediction[J]. International Journal of Green Energy,2009,6(5):479-489
[119]M. Kittipong, W. J. Lee, C. Yingvivatanapong, et al. An Integration of ANN Wind Power Estimation into UC Considering the Forecasting Uncertainty [J].2005 IEEE Industrial and Commercial Power Systems Technical Conference, Conference Record,2005,116-119
[120]I. J. Ramirez-rosado, L. A. Fernandez-jimenez, C. Monteiro. Artificial Neural Network Models for Wind Power Short-term Forecasting Using Weather Predictions[J]. Proceedings of the 25th IASTED International Conference on Modelling, Identification, and Control,2006,128-132
[121]田雨波.混合神经网络技术[M].科学出版社,2009
[122]T. S. Nielsen, H. Madsen, J. Tofting. Experiences with Statistical Methods for Wind Power Prediction[C]. Wind Energy Conference, Nice,1999 [123] T. Burton, D. Sharpe, N. Jenkins, et al. Wind Energy:Handbook[M]. Wiley Online Library,2001
[124]J. M. Bates, C. W. J. Granger. The Combination of Forecasts[J]. OR, 1969,451-468
[125]D. Legourieres, P. South. Wind Power Plants-theory and Design[M]. Pergamon Press (Oxford and New York),1982
[126]孙川永.风电场风电功率短期预报技术研究[D].兰州大学,2009
[127]张德丰.MATLAB神经网络应用设计/MATLAB工程应用书库[M].机械工业出版社,2009
[128]牛东晓.电力负荷预测技术及其应用[M].中国电力出版社,1999
[129]N. E. Huang, Z. Shen, S. R. Long. A New View of Nonlinear Water Waves:The Hilbert Spectrum[J]. Annual Review of Fluid Mechanics,1999, 31(1):417-457
[130]N. E. Huang, Z. Shen, S. R. Long, et al. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis[J]. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences,1998,454(1971):903-995
[131]纪跃波,秦树人,柏林,仲佑明.有限区间信号边界效应问题的研究[J].振动与冲击,2002,(4)：108-111
[132]Z. Wu, N. E. Huang. A Study of the Characteristics of White Noise Using the Empirical Mode Decomposition Method[J]. Proceedings of the Royal Society of London Series A:Mathematical, Physical and Engineering Sciences, 2004,460(2046):1597-1611
[133]李俊峰,戴文战.基于灰色关联度和神经网络的变权组合预测模型研究[J].2006中国控制与决策学术年会论文集,2006
[134]Y. S. Huang, H. J.Zhang. Grey Correlation in the Combined Weights of Power Load Forecasting Application[J]. Proceedings of the International Conference on Information Management, Innovation Management and Industrial Engineering,2008, (1):292-295
[135]张青.基于神经网络最优组合预测方法的应用研究[J].系统工程理论与实践,2001,21(9)：90-93
[136]D. W. Bunn. Statistical Efficiency in the Linear Combination of Forecasts[J]. International Journal of Forecasting,1985,1(2):151-163
[137]李学全,李春生.一种改进的模糊变权重组合预测方法[J].中南工业大学学报,2003,34(006)：708-710
[138]周宗放,唐小我,路应金.正交条件下变权组合预测的误差界[J].管理工程学报,2003,17(003)：8-11
[139]宫靖远,贺德馨,孙如林,吴运东.风电场工程技术手册[M].北京：机械工业出版社,2004
[140]陈仲生.基于Matlab7.0的统计信息处理[M].长沙：湖南科学技术出版社,2005
[141]G. Heydt, N. Farahbakhshian. A Method for the Forecasting of the Probability Density Function of Power System Loads[J]. IEEE Transaction on power apparatus and systems,1981,100(12):9
[142]D. K. Rana Weera, G. G. Karady, R. G. Farmer. A Probabilistic Approach to Handle Uncertainties in Load Forecasting[C]. Proceedings of the the 57th Annual American Power Conference,1995, Chicago. IL. USA
[143]穆钢,侯凯元,杨右军等.负荷预报中符合规律性评价方法的研究[J].中国电机工程学报,2001,21(10)：96-101
[144]W. C. M. S. Charytoniuk, P. Kotas. Demand Forecasting in Power Distribution Systems Using Nonparametric Probability Density Estimation[J]. IEEE Transactions on Power Systems,1999,14(4):1200-1206
[145]D. H. Matthias Lange. Relating the Uncertainty of Short-term Wind Speed Predictions to Meteorological Situations with Methods from Synoptic Climatology[M]. European Wind Energy Conference & Exhibition EWEC. Madrid, Spain.2003
[146]J. B. Bremnes. Probabilistic wind power forecasts by means of statistical model[C]. Proceedings of the IEA R&D Wind Annex XI Joint Action Symposium on Wind Forecasting Techniques,2002, Norrkoping
[147]M. Kittipong, W. J. Lee, J. R. Liao, et al. An Integration of ANN Wind Power Estimation into Unit Commitment Considering the Forecasting Uncertainty [J]. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2007,43(6):1441-1448
[148]P. Pinson G. N. Wind Power Forecasting Using Fuzzy Neural Networks Enhanced with On-line Prediction Risk Assessment[M]. IEEE Bologna Power Technology Conference,2003
[149]周松林,茆美琴,苏建徽.风电功率短期预测及非参数区间估计[J].中国电机工程学报,2011,31(25)：10-16[150]范高锋,王伟胜,刘纯,戴慧珠.基于人工神经网络的风电功率预测[J].中国电机工程学报,2008,28(34)：118-123
[151]J. S. Liu. Monte Carlo Strategies in Scientific Computing[M]. Springer Verlag,2008
[152]K. Binder. Monte-Carlo Methods[M]. Wiley Online Library,1979
[153]盛骤,谢式千,潘承毅.概率论与数理统计[M].北京：高等教育出版社,2001
[154]杨文佳,康重庆,夏清.基于预测误差分布特性统计分析的概率性短期负荷预测[J].电力系统自动化,2006,30(19)：47-52
[155]I. L. L.Troen. Short Term Prediction of Local Wind Conditions[C]. Proceedings of the European community wind energy conference,1990, Madrid, Spain
[156]H. D. T. Beyer, J. Hausmann, M. Hoffmann, et al. Short Term Prediction of Wind Speed and Power Output of a Wind Turbine with Neural Networks[C]. Proceedings of the European wind energy conference,1994, Thessaloniki-Macedonia, Greece
[157]H. H. D. Beyer, H. Mellingoff, K. Monnich, et al. Forecast of Regional Power Output of Wind Turbines[C]. Proceedings of the European wind energy conference,1999, Nice
[158]E. E. A. Kalnay. The NCEP/NCAR 40-year Reanalysis Project[J]. Bullectin of the American Meteorological Society 1996,77(3):437-471
[159]O. R. J. Sanchezi, C. Usaola, et al. Sipreolico-a Wind Power Prediction System Based On Flexible Combination Of Dynamic Models Application To The Spanish Power System[M]. First IEA Joint Action Symposium on Wind Forecasting Techniques. Norrkoping, Sweden.2002
[160]G. D. J. GRELL, D. Stanffer. A Description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5)[R]. NCAR Technical Note,1995
[161]J. M. C. Jorgensen, B. O. Gallag-oir. HIRPOM:Description of an Operational Numerical Wind Power Prediction Model for Large Scale Integration of on and Offshore Wind Power in Denmard[C]. Proceedings of the Global Windpower Conference and Exhibition,2002, Paris, France
[162]G. Gow. Short Term Wind Forecasting[C]. Proceedings of the first joint action symposium on wind forecasting techniques, International Energy Agency (IEA),2002, Norrkoping
[163]G. M. D. Kariniotaks, J. Moussafir, R. Chevallaz-perrier, et al. ANEMOS: Development of a Next Generation Wind Power Forecasting Systerm for the Large-Scale Integration of Onshore&Offshore Wind Farms[C]. Proceedings of the European wind energy conference,2003, Madri