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基于SVM的风速风功率预测模型 被引量:34

Model building for wind speed and wind power prediction based on SVM
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摘要 风电是一种最方便、最成熟的可再生能源。风力发电具有波动性、间歇性和随机性,大容量的风力发电接入电网,对电力系统的安全、稳定运行带来影响。通过风速风功率预测,对风电场的出力进行短期预报,是解决这一问题的有效途径。常用的预测方法中,要么预测结果偏差太大,要么存在过学习、维数灾难和局部极值问题。支持向量机(SVM)应用于风速风功率预测,明显优于常用方法,得到相当可观的结果。 Wind power is the most convenient and matured form of renewable energy.But wind power is fluctuant,intermittent and stochastic.The large capacity wind power connecting into grid will bring serious challenge to the safety and stabilization of power system.Wind speed and wind power prediction is an effective way to solve this problem.The commonly used forecasting methods,or the prediction error is too large,or there are the problems such as over-learning,dimensionality disasters and the local extremum.Applying the Support Vector Machine(SVM) in the wind speed wind power prediction,will be significantly better than the usual method,the results is positive.
出处 《可再生能源》 CAS 北大核心 2010年第4期25-28,32,共5页 Renewable Energy Resources
基金 国家基金自然科学项目(50867004 50767003) 新疆维吾尔自治区重大攻关计划项目(200732141-3)
关键词 支持向量机 核函数 风速 风功率 预测 support vector machine(SVM) kernel wind speed wind power prediction
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  • 1杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究[J].中国电机工程学报,2005,25(11):1-5. 被引量:583
  • 2杨金芳,翟永杰,王东风,徐大平.基于支持向量回归的时间序列预测[J].中国电机工程学报,2005,25(17):110-114. 被引量:65
  • 3王维,李洪儒.BP神经网络在状态监测数据趋势预测中的应用[J].微计算机信息,2005,21(11S):141-143. 被引量:14
  • 4[9]李国正,王猛,曾华军译.支持向量机导论.北京:电子工业出版社,2004. 被引量:2
  • 5[4]Jinlong An;Zheng-Ou Wang;Qingxin Yang;Zhenping Ma.A SVM Function Approximation Approach with Good Performances in Interpolation and Extrapolation.Proceedings of the Fourth International Conference on Machine Learning and Cybernetics,2005,1648 -1653 被引量:1
  • 6[5]Niu Lin;Zhao Jian-guo;Du Zhi-gang;Jin Xiao-ling.Application of Time Series Forecasting Algorithm via Support Vector Machines to Power System Wide-area Stability Prediction.2005 IEEE/PES Transmission and Distribution Conference and Exhibition:Asia and Pacific,2005,1-6 被引量:1
  • 7[6]Vanajakshi,L.Rilett,L.R..A Comparison of the Performance of Artificial Neural Networks and Support Vector Machines for the Prediction of Traffic Speed.2004 IEEE Intelligent Vehicles Symposium,2004,194-199 被引量:1
  • 8刘永前,韩爽,杨勇平,高辉.提前三小时风电机组出力组合预报研究[J].太阳能学报,2007,28(8):839-843. 被引量:21
  • 9Garrad Hassan&Partners Ltd. Forecasting Short- Term Wind Farm Production. http: //www. garradhassan.com. 被引量:1
  • 10Peter Coppin, Jack Katzfey. The Feasibility of Wind Power Production Forecasting in the Australian Con text [R]. CSIRO Atmospheric Research. Report to: National Electricity Market Management Company Limited, 2003, 12. 被引量:1

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