Аннотация
This paper introduces neural networks technique for wind speed prediction
and compares its performance with an autoregressive model. First,
we studied the statistical characteristics of mean monthly and daily
wind speed in Jeddah, Saudi Arabia. The autocorrelation coefficients
are computed and the correlogram is found compatible with the real
diurnal variation of mean wind speed. The stochastic time series
analysis is found to be suitable for the description of autoregressive
model that 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 testing data indicate that the neural network approach
outperforms the AR model as indicated by the prediction graph and
by the root mean square errors.
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