Modeling of Wind Speed for Palestine Using Artificial Neural Network

Abstract

This study presents a wind speed prediction using Feedback forward artificial neural networks for two sites in Palestine which are Ramallah and Nablus. 1.1ATLAB is used to develop and train the proposed network using weather records for Palestine. However, three statistical values are used to evaluate the proposed networks. These statistical values are mean absolute percentage error, MAPE, mean bias error, 1.1BE and root mean square error, RMSE. Based on results, the proposed network predicts an accurate daily wind speed values. The 1.1APE, RJ\.1SE and 1.1BE values for the predicted daily wind speed values for Ramallah city are 8%, 0.5305 (12.15%) and-0.0192 (-0.441%). Meanwhile, the MAPE, RMSE and MBE values for predicted daily wind speed values for Nablus city are 9.25%, 0.8407 (I 4.94%) and 0.09 (I .6%), respectively. Such proposed approach helps in weather forecasting and estimating the output power of a wind turbine.