论文部分内容阅读
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 problem of power system load forecasting, historical load data are complemented by radial basis function (RBF) neural network. Based on principal component analysis (PCA) and RBF neural network theory, combined with PCA and RBF neural network Method for load forecasting. The example shows that this method can effectively reduce the dimensionality of input variables and has higher accuracy.