Wind Power Generation Prediction by Particle Swarm Optimization Algorithm and RBF Neural Network

Abstract:

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 output layer, the centers of radial basis function in hidden layer 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 layer. It is indicated that the hybrid model of particle swarm optimization algorithm and RBF neural network has better prediction ability than BP neural network.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 2099-2102
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.2099
• Online since: January 2012
• Authors: Rui Fang Liu
• Keywords: Neural Network (NN), Particle Swarm Optimization Algorithm (PSO), Prediction, Wind Power Generation

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