A Self-Adaptive Approach for Particle Swarm Optimization Applied to Wind Speed Forecasting

Abstract

Operational research has made meaningful contributions to practical forecasting in organizations. An area of substantial activity has been in nonlinear modeling. Based on Particle Swarm Optimization, we discuss a nonlinear method where a self-adaptive approach, named as Particle Swarm Optimization with aging and weakening factors, was applied to training a Focused Time Delay Neural Network. Three freely available benchmark datasets were used to demonstrate the features of the proposed approach compared to the Backpropagation algorithm, Differential Evolution and the Particle Swarm Optimization method when applied for training the artificial neural network. Even acknowledging that the effort in comparing methods across multiple empirical datasets is certainly substantial, the proposed algorithm was used to produce 30 min, 1, 3 and 6 h ahead predictions of wind speed at one site in Brazil. The use of the proposed algorithm goes further than only training the artificial neural network, but also searching the best number of hidden neurons and number of lags. The results have shown that the modified Particle Swarm Optimization algorithm obtained better results in all predictions horizons, and the use of it has remarkably reduced the training time.