一种基于多方法组合的新型等值附盐密度预测模型

A New ESDD Forecasting Model Based on Multi-methods Combination

为了克服目前预测等值附盐密度的三种单一预测模型,即多元线性回归法,BP神经网络法和最小二乘支持向量机法存在的问题,以光传感器输变电设备盐密在线监测系统提供的数据为依据,建立了基于小波神经网络的一种等值附盐密度的非线性组合预测模型。该模型为单输出的3层小波神经网络,即将多元线性回归,BP神经网络及最小二乘支持向量机的预测结果作为模型的输入,实际测量值作为输出,使训练的网络具有预测能力。为了更好地反映单一模型预测值对等值附盐密度的影响及提高等值附盐密度的预测精度,选用Morlet小波构建小波神经网络,采用误差反向传播学习算法来训练网络,利用遗传算法确定网络参数的初始值。仿真结果表明本模型预测精度不仅高于任一个单一预测模型,而且高于线性组合预测模型。

To conquer the problems existed in the ESDD forecasting by multivariate linear regression （MLR） model, baek propagation （BP） neural network and least squares support vector machines （LSSVM）, a nonlinear combination forecasting model based on wavelet neural network （WNN） for ESDD is proposed. The model is a WNN with three layers, whose input layer has three neurons and output layer has one neuron, namely, regarding the forecasting results of the three ESDD forecasting models as the inputs of the combined WNN and the measured value of ESDD as the output. For the sake of better reflection of the influence of each single forecasting model on ESDD and increase of the accuracy of ESDD prediction, Morlet wavelet is used to construct WNN, error backpropagation algorithm is adopted to train the network and genetic： algorithm is used to determine the initials of the network parameters. Simulation results show that the aecuracy of the proposed combination ESDD forecasting model is higher than that of any single model and also higher than that of traditional linear combination forecasting （LCF） model. The model provides a new feasible way to real implementation of computerization of pollution distribution map of power network.