风电场发电功率组合预测方法研究 

胡婷1，刘观起1，邵龙1，刘哲2，孙勃3 
1 华北电力大学，河北 保定 071000；
2 河北省电力科学研究院，河北 石家庄 050000；
3 图们市供电分公司，吉林 延边 133100 
 

摘 要：针对风电场发电功率的短期预测，阐述了组合预测的方法原理。分别建立基于相空间重构的RBF 神经网络模型、时间序列模型、支持向量机模型三种单项预测模型，并在此基础上确立加权系数，得到了两个组合预测模型。预测结果显示组合预测较单项预测的效果有了很大的改善，具有实际意义和应用价值。
关键词： 神经网络；时间序列；支持向量机；组合预测
中图分类号：TM614 文献标识码：A 文章编号：1007-3175(2013)05-0023-05

Study on Combination Model of Wind Power Generation Prediction 

HU Ting1, LIU Guan-qi1, SHAO Long1, LIU Zhe2, SUN Bo3 
1 North China Electrical Power University, Baoding 071000, China;
2 Electric Power Research Institute of Hebei Province, Shijiazhuang 050000, China;
3 Tumen Power Supply Subsidiary, Yanbian 133100, China 
 

Abstract: Aiming at short-term predication of wind generation power, this paper described the method and principle of combined prediction. This paper constructed three kinds of single predicting models, including radial basis function (RBF) neural network model based on phase space reconstruction, time series model and support vector machine model, and on this basis, weighing coefficients were determined to get two groups of combined prediction models. The predicting result shows that the effect of combined prediction is improved more than that of single prediction, with practical significance and applicable value.
Key words: neural network; time series; support vector machine; combined prediction

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