摘要
风电场输出功率预测对于接入大量风电的电力系统运行具有重要意义。作者利用BP神经网络、径向基函数神经网络和支持向量机进行风电功率预测,提出了风电场输出功率的组合预测模型。采用3种方法确定权重,即等权重平均法、协方差优选组合预测法和时变权系数组合预测法。研究结果表明,不同方法的预测精度不同,整体预测精度高的方法在个别预测点也可能误差较大,组合预测模型能有效减少各预测点较大误差的出现,有利于提高预测精度。
It is of significance to forecast output power of wind farm for the operation of power grid to which large amount of wind power is connected. By use of BP neural network, radial basis function neural network and support vector machine, a combination forecasting model for output power of wind farm is built. The weights are calculated by three methods, i.e., equal weight average method, covariance optimization combination forecast and time-varying weight combination forecast. Research results show that the forecast accuracy from different methods is diverse one another; even though a method can offer high forecast accuracy in total, at individual point the forecast error of this method may be larger, however combination forecasting model can avoid larger forecast error in each point, so it is favorable to improve forecast accuracy.
出处
《电网技术》
EI
CSCD
北大核心
2009年第13期74-79,共6页
Power System Technology
关键词
风电场
功率预测
BP神经网络
径向基函数神经网络
支持向量机
wind farm
power forecast
BP neural network
radial basis function （RBF） neural network
support vector machine （SVM）
