ESTIMATION OF FAST VARIED WIND SPEED BASED ON NARX NEURAL NETWORK BY USING CURVE FITTING

In this study, a Nonlinear AutoRegressive eXogenous insert ignore into journalissuearticles values(NARX); neural network is used to estimate the wind speed on three monthly data sets taken from the wind central in Zonguldak province in Turkey. In the estimation study, the first and second order curve fitting coefficients of the measured temperature, pressure, humidity and solar radiation parameters together with the wind speed are used. In the estimation process, before these coefficients are applied directly to the NARX network structure, the most suitable features are selected with ReliefF method to minimize the MSE value. The number of delay steps in the NARX network structure is varied from 3 to 15 and the number of hidden neurons is varied from 3 to 15 to obtain model parameters that give the least estimation error. In order to determine the performance of the obtained model, the model is evaluated in terms of statistical error criteria such as MAE, MSE and RMSE. The model parameters and features matrix giving the least estimation error for the wind speed estimation of the NARX network structure are determined. It has been observed that this approach provides a high performance for estimating the wind speed with related to other measured parameters.