OPTIMAL NEURAL NETWORK MODELS FOR WIND SPEED PREDICTION

The accuracy of wind speed forecasting is important to control, and optimizes wind power generation. The nonlinearity patterns of wind speed data is the reason of inaccurate wind speed forecasting. The artificial neural networks handle the nonlinearities and provide accurate wind speed. This paper presents multilayer feed forward network and radial basis functions for wind speed forecasting performance analysis of real time data collected from Coimbatore wind farms, utilizing daily wind speed data collected over a period of one year. Experimental results show that this model is an optimal model which can improve the prediction precision of wind speed compared with other approaches according to the statistical analysis involving the coefficient of determination (R2), mean absolute error (MAE), the root mean square error (RMSE) and the mean bias error (MBE) a re conducted.