Wind Power Generation Prediction by Particle Swarm Optimization Algorithm and RBF Neural Network
wind power generation trend prediction is the important to make the plan on the development of wind power generation．Wind power generation prediction by particle swarm optimization algorithm and RBF neural network in the paper．As the connection weights between the hidden layer and ontput layer,the centers of radial basis function in hidden laver and the widths of radial basis function in hidden layer have a great influence on the prediction results of RBF neural network,particle swarm optimization which has a great global optimization ability is used to optimize the three parameters including the connection weights between the hidden layer and output layer,the centers of radial basis function in hidden layer and the widths of radial basis function in hidden laver．It is indicated that the hybrid model of particle swarm optimization algorithm and RBF neuraI network has better prediction ability than BP neural network.
wind power generation particle swarm optimization prediction neural network
Liu Rui-fang
Dept.of applied mathematics Taiyuan University of Science and Technology Taiyuan 030024,China
国际会议
海口
英文
216-219
2011-02-22（万方平台首次上网日期，不代表论文的发表时间）