基于主成分分析与人工神经网络的风电功率预测Prediction of Wind Power Based on Principal Component Analysis and Artificial Neural Network
周松林;茆美琴;苏建徽;
摘要(Abstract):
提出了主成分分析与前馈神经网络相结合的风电功率预测模型。采用主成分分析法对原始多维输入变量进行预处理,选择输入变量的主成分作为神经网络的输入,既减少了输入变量的维数,又消除了各输入变量的相关性,从而简化了网络的结构,提高了网络收敛性和稳定性。仿真结果表明,相对于一般神经网络模型,基于主成分分析的神经网络模型预测精度更高、泛化性能更好。
关键词(KeyWords): 风电功率预测;主成分分析;前馈神经网络;泛化性能
基金项目(Foundation): 国家重点基础研究发展计划项目(973项目)(2009CB219708)~~
作者(Author): 周松林;茆美琴;苏建徽;
Email:
DOI: 10.13335/j.1000-3673.pst.2011.09.004
参考文献(References):
- [1]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究[J].中国机电工程学报,2005,25(11):1-5.Yang Xiuyuan,Xiao Yang,Chen Shuyong.Wind speed and generated power forecasting in wind farm[J].Proceedings of the CSEE,2005,25(11):1-5(in Chinese).
- [2]潘迪夫,刘辉,李燕飞.风电场风速短期多步预测改进算[J].中国电机工程学报,2008,28(26):87-91.Pan Difu,Liu Hui,Li Yanfei.Optimization algorithm of short-term multi-step wind speed forecast[J].Proceedings of the CSEE,2008,28(26):87-91(in Chinese).
- [3]Bossanyi E A.Short-term wind prediction using Kalmanfilters[J].Wind Engineering,1985,9(1):1-8.
- [4]Mohamed A M,Shafiqur R,Talal O H.A neural networks approach for wind speed prediction[J].Renewable Energy,1998,13(3):345-354.
- [5]范高锋,王伟胜,刘纯.基于人工神经网络的风电功率短期预测系统[J].电网技术,2008,32(22):82-86.Fan Gaofeng,Wang Weisheng,Liu Chun.Artificial neural network based wind power short term prediction system[J].Power System Technology,2008,32(22):82-86(in Chinese).
- [6]白永祥,房大中,侯佑华,等.内蒙古电网区域风电功率预测系统[J].电网技术,2010,34(10):158-162.Bai Yongxiang,Fang Dazhong,Hou Youhua,et al.Regional wind power forecasting system for Inner Mongolia power grid[J].Power System Technology,2010,34(10):158-162(in Chinese).
- [7]张彦宁,康龙云,周世琼,等.小波分析应用于风力发电预测控制系统中的风速预测[J].太阳能学报,2008,29(5):520-524.Zhang Yanning,Kang Longyun,Zhou Shiqiong,et al.Wavelet analysis applied to wind speed prediction in predicate control system of wind turbine[J].Acta Energiae Solaris Sinica,2008,29(5):520-524(in Chinese).
- [8]王丽婕,冬雷,廖晓钟,等.基于小波分析的风电场短期发电功率预测[J].中国电机工程学报.2009,29(28):30-33.Wang Lijie,Dong Lei,Liao Xiaozhong,et al.Short-term power prediction of a wind farm based on wavelet analysis[J].Proceedings of the CSEE,2009,29(28):30-33(in Chinese).
- [9]Pai Pingfeng,Hong Weichiang.Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms[J].Electric Power Systems Research,2005,74(3):417-425.
- [10]戚双斌,王维庆,张新燕.基于支持向量机的风速与风功率预测方法研究[J].华东电力,2009,37(9):1600-1603.Qi Shuangbin,Wang Weiqing,Zhang Xinyan.Wind speed and wind power prediction based on SVM[J].East China Electric Power,2009,37(9):1600-1603(in Chinese).
- [11]王丽婕,廖晓钟,高爽,等.并网型大型风电场风力发电功率–时间序列的混沌属性分析[J].北京理工大学学报,2007,27(12):1077-1080.Wang Lijie,Liao Xiaozhong,Gao Shuang,et al.Chaos characteristics analysis of wind generationtime series for a grid connecting wind farm[J].Transactions of Beijing Institute of Technology,2007,27(12):1077-1080(in Chinese).
- [12]罗海洋,刘天琪,李兴源,等.风电场短期风速的混沌预测方法[J].电网技术,2009,33(9):67-71.Luo Haiyang,Liu Tianqi,Li Xingyuan,et al.Chaotic forecasting method of short-term wind speed in wind farm[J].Power System Technology,2009,33(9):67-71(in Chinese).
- [13]刘纯,范高锋,王伟胜,等.风电场输出功率的组合预测模型[J].电网技术,2009,33(13):74-79.Liu Chun,Fan Gaofeng,Wang Weisheng,et al.A combination forecasting model for wind farm output power[J].Power System Technology,2009,33(13):74-79(in Chinese).
- [14]王学仁,王松桂.实用多元统计分析[M].上海:上海科学技术出版社,1990:270-344.
- [15]王华,王连华,葛岭梅.主成分分析与BP神经网络在煤耗氧速度预测中的应用[J].煤炭学报,2008,33(8):920-925.Wang Hua,Wang Lianhua,Ge Lingmei.Application of principal component analysis and BP neural network in the rate of coal oxygen consumption prediction[J].Journal of China Coal Society,2008,33(8):920-925(in Chinese).
- [16]Lexiadis M A,Dokopoulo S P,Samanoglou S H,et al.Short term forecasting of wind speed and related electrical power[J].Solar Energy,1998,63(1):61-68.
- [17]Sandhya S.史晓霞译.神经网络在应用科学和工程中的应用—从基本原理到复杂的模式识别[M].北京:机械工业出版社,2009:7.
- [18]邓乃扬,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,2004:173-192.
- [19]Vapnik V.The nature of statistical learning theory[M].New York:Springer-Verlag,1995:84-93.
- [20]Jeremie J,Lionel F,George K.Probabilistic short-term wind power forecasting based on kernel density estimators[C]//European Wind Energy Conference.Milan,Italy:European Wind Enengy Association,2007:7-10.