基于PCA和RBF神经网络的中长期负荷预测方法

Medium and Long-Term Load Forecasting Method Based on PCA and RBF Network

针对电力系统负荷预测问题,利用径向基函数(RBF)神经网络补全历史负荷数据,然后在主成分分析(Principal Component Analysis,PCA)和RBF神经网络原理的基础上,结合PCA和RBF神经网络方法进行负荷预测。实例表明该方法能有效降低输入变量的维数,且具有较高的精度。

According to the load characteristics of electric power system in medium and long term and the nonlinear identification function of radial basis function （RBF） neural network, RBF neural network is applied to complement the vacant data, and principal component analysis （PCA） is applied to decrease the dimension of the input space. The RBF neural network is used to set up the model of medium and long-term load forecasting. The feasibility and validity of the method presented in this paper are proved by practical examples.