改进PSO的WNN模型在短期负荷预测中的应用

针对电力系统负荷预测中实际的负荷数据往往具有极大的波动性,模型呈现出极大的非线性,提出一种改进粒子群优化的小波神经网络模型,将其应用于电力系统的负荷预测研究.首先,分析和介绍了小波神经网络和改进的粒子群算法的基本原理和优点;其次,将改进的PSO算法用于优化小波神经网络的参数优化;最后对改进的PSO-WNN负荷预测模型进行仿真分析.实验结果与传统PSO-WNN的实验结果进行对比,证明改进的PSO能够提高模型的运算效率和负荷预测精度.

On Application of Improved PSO-WNN to Model Load Forecasting

An improved Particle Swarm Optimization algorithm has been proposed in this paper to model the wavelet neuron network, and this model been applied to the application of forecasting load in power system.Firstly, the basic principle of improved PSO-WNN model is presented.Secondly, the improved PSO algorithm is used to optimize the parameters of WNN load forecasting model.Then, the experiment simulations of improved PSO-WNN model are conducted and analyzed.The improved PSO-WNN model is compared with conventional PSO-WNN model.The experiment results show that the proposed model is efficiency in model computing and the precision of the load forecasting.