A NEURAL NETWORKS APPROACH FOR WIND-SPEED PREDICTION

This paper introduces neural networks technique for wind speed predict ion and compares its performance with an autoregressive model. First, we studied the statistical characteristics of mean monthly and daily w ind speed in Jeddah, Saudi Arabia. The autocorrelation coefficients ar e computed and the correlogram is found compatible with the real diurn al variation of mean wind speed. The stochastic time series analysis i s found to be suitable for the description of autoregressive model tha t involves a time lag of one month for the mean monthly prediction and one day for the mean daily wind speed prediction. The results on a te sting data indicate that the neural network approach outperforms the A R model as indicated by the prediction graph and by the root mean squa re errors. (C) 1998 Elsevier Science Ltd. All rights reserved.