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Journal of Applied Sciences
Year: 2011  |  Volume: 11  |  Issue: 14  |  Page No.: 2634 - 2639

Modeling of Wind Speed for Palestine Using Artificial Neural Network

Tamer Khatib and Samer AlSadi    

Abstract: This study presents a wind speed prediction using Feedback forward artificial neural networks for two sites in Palestine which are Ramallah and Nablus. MATLAB 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, MBE and root mean square error, RMSE. Based on results, the proposed network predicts an accurate daily wind speed values. The MAPE, RMSE and MBE 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 (14.94%) and 0.09 (1.6%), respectively. Such proposed approach helps in weather forecasting and estimating the output power of a wind turbine.

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