风电场输出功率预测中两种神经网络算法的研究  

Study on Two Neural Network Algorithms to Predict Wind Power

作　　者：刘永前[1] 朴金姬[2] 韩爽[1] 

机构地区：[1]华北电力大学可再生能源学院,北京102206 [2]华北电力大学能源动力与机械工程学院,北京102206

出　　处：《现代电力》2011年第2期49-52,共4页Modern Electric Power

基　　金：国家高技术研究发展计划(863计划);(2007AA05Z428);国家科技支撑计划(2008BAA14B03);中央高校基本科研业务费专项资金(09MG17)

摘　　要：神经网络是风电功率预测系统中应用最广泛的方法,而其训练算法是影响预测精度的重要因素之一。探讨了采用聚类法和正交最小二乘算法两种训练方法。以中国北方某风电场的实际数据以及数值天气预报数据为依据,对RBF聚类法和正交最小二乘算法进行了验证,最终研究并比较RBF不同预测情况与BP的差异。结果表明:对于提前24h的风电功率预测,RBF神经网络模型预测精度要好于BP神经网络模型,尤其以正交最小二乘算法为训练方法建立的RBF模型,预测精度较高,能够很好拟合实际功率曲线。Neural network is a widely used method in the prediction of wind power,and its training algorithm is one of important factors that affect the prediction accuracy.The authors explore two training methods： clustering method and orthogonal least square algorithm.Based on the actual data that coming from a wind farm in North China and the numerical weather prediction data,the clustering method and orthogonal least square algorithm are verified,and the difference between RBF predict models and BP predict model is finally studied.The results show that the prediction of wind power before 24 hours by using of RBF neural network model is better than that by BP neural network model.Especially RBF model based on orthogonal least squares algorithm has higher accuracy,and its prediction curve can well fit the actual power curve.

关 键 词：风电场 功率预测 神经网络 数值天气预报 聚类法 正交最小二乘算法 

分 类 号：TM614[电气工程—电力系统及自动化]