Optimized radial basis function neural network model for wind power prediction

Abstract

In this paper an effort has been done in developing a fast and efficient Radial Basis Function (RBF) neural network model to predict the power output of a wind turbine. The performance of the RBF neural network has been improved by making use of a hybrid Particle Swarm Optimization based Fuzzy C Means (PSO-FCM) clustering algorithm. Extreme Learning Machine (ELM) algorithm has been used to improve the speed of learning. Particle Swarm Optimization (PSO) has also been used to optimize the number of centers and width of the RBF units of the developed neural network model. The simulation results show that the model developed has a compact network structure and good generalization ability with 100% accuracies on training, test and validation data sets. The novelty of the present work is the use of PSO in optimizing the RBF neural network model and use of ELM in training the same. � 2016 IEEE.