基于时间连续性及季节周期性的风速短期组合预测方法  

A Short-term Combination Wind Speed Forecasting Method Considering Seasonal Periodicity and Time-continuity

作　　者：蒋小亮[1] 蒋传文[1] 彭明鸿[1] 林海涛[1] 李子林 

机构地区：[1]上海交通大学电子信息与电气工程学院,上海市200240 [2]上海市风力发电有限公司,上海市200090

出　　处：《电力系统自动化》2010年第15期75-79,共5页Automation of Electric Power Systems

基　　金：国家高技术研究发展计划(863计划)资助项目(2007AA05Z458)~~

摘　　要：根据风电机组功率曲线,可由风速计算出风电机组出力,因此,风电功率预测问题可转化为风速预测问题。基于风电场气象及风速数据的时间连续性及季节周期性,提出了一种风速短期组合预测模型。该模型采用模式识别技术分别提取时间连续性的样本及季节周期性的样本,以反向传播(BP)神经网络作为预测模型,得到风速横向预测值和纵向预测值,最后再通过BP神经网络进行组合预测。对国内某风电场的风速预测结果表明,所提出的风速预测模型可行、有效,具有较好的预测精度。As long as the wind speed can be forecasted,the output of a wind power unit can be calculated according to the unit’s power curve. A short-term combination forecasting model of wind speed is developed based on the seasonal periodicity and time-continuity of wind energy. With the pattern recognition technique,two kinds of samples to seasonal periodicity and time-continuity are selected separately. The two kinds of samples are used respectively to train two back propagation （BP） neural network models to obtain the lateral and vertical wind speed forecasting values. By importing the two values to a BP neural network again,the wind speed value is finally predicted. Forecasting results of a domestic wind farm indicate that the proposed combination forecasting method is valid and effective in short-term wind speed forecasting.

关 键 词：风速预测 时间连续性 季节周期性 模式识别 反向传播(BP)神经网络 

分 类 号：TM614[电气工程—电力系统及自动化]