The Neural Network Model for Wind Field Assessment Based on Particle Swarm Optimization Algorithm

Using the global search ability and optimize the network structure and connection power of artificial neural network at the same time by particle swarm optimization algorithm and a new training of BP neural network was going on, then a nonlinear artificial neural network model used to calculate the wind speed of wind farms was constructed, here the Mountain Darong in Guangxi wind farms would be calculated at this paper for example. The results showed that the calculation accuracy by the nonlinear ensemble model of neural network based on particle swarm optimization algorithm for wind field is significantly higher than the traditional multiple linear regression model. Thus in practical application the long time sequence data of wind could be calculate according to the short time sequence data of the observation through the new model, therefore this model is better practicability and popularize value for it provide the basis to research the exploitation wind resources.