SHORT TERM WIND SPEED PREDICTION USING A NEW HYBRID MODEL WITH PASSIVE CONGREGATION

Abstract

Short term wind speed predicting is essential in using wind energy as an alternative source of electrical power generation, thus the improvement of wind speed prediction accuracy becomes an important issue. Although many prediction models have been developed during the last few years, they suffer a poor performance because their dependency on performing only the local search without the capability in performing the global search in the whole search space. To overcome this problem, we propose a new passive congregation term to the standard hybrid Genetic Algorithm / Particle Swarm Optimization (GA/ PSO) model in training Neural Network (NN) wind speed predictor. This term is based on the mutual cooperation between different particles in determining new positions rather than their selfish thinking. Experiment study shows significantly the influence of the passive congregation   term in improving the performance accuracy compared to the standard model.