Comparison of different neural network training algorithms for wind velocity forecasting

In this paper the wind speed is predicted by the use of data provided from the Mehrabad
meteorological station located in Tehran, Iran, Collected between 2003 and 2008. A comprehensive
analogy study is presented on Comparison of various Back Propagation neural networks methods in
wind velocity forecasting. Four types of activation functions, namely, BFGS quasi-Newton,
Bayesian regularized, Levenberg -Marquardt, and conjugate gradient algorithm, were studied. The
data was investigated by correlation coefficient and characterizing the amount of dependency
between the wind speed and other input data. The meteorological parameters (pressure, direction,
temperature and humidity) were used as input data, while the wind velocity is used as the output of
the network. The results demonstrate that for the similar wind dataset, Bayesian Regularized
algorithm can accurately predict compared with other method. In addition, choosing the type of
activation function is dependent on the amount of input data, which should be acceptably large.