Validation of fixed speed wind turbine dynamic models with measured data
A neural networks approach for wind speed prediction
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A NEURAL NETWORKS APPROACH FOR WIND SPEED PREDICTION MOHAMED A. MOHANDES,* SHAFIQUR REHMAN and TALAL O. HALAWAN1 Development and Manufacturing Center, Research Institute. King Fahd University of Petroleum and Minerals. Dhahran 31261, Saudi Arabia (Received 10 April 1997: accepted20 October L997) Abstract This paper introduces neural networks technique for wind speed prediction and compares its performance with an autoregressive model. First, we studied the statistical characteristics of mean monthly and daily wind speed in Jcddah, Saudi Arabia. The autocorrelation coefficients are computed and the correlogram is found compatible with the real diurnal variation of mean wind speed. The stochastic time series analysis is found to be suitable for the description of autoregressive model that involves a time lag of one month for the mean monthly prediction and one day for the mean daily wind speed prediction. The results on a testing data indicate that the neural network approach outperforms the AR model as indicated by the prediction graph and by the root mean square errors. © 1998 Elsevier Science Ltd. All rights reserved.
Transcript of A neural networks approach for wind speed prediction
A NEURAL NETWORKS APPROACH FOR WIND SPEED PREDICTION
MOHAMED A. MOHANDES,* SHAFIQUR REHMAN and TALAL O. HALAWAN1
Development and Manufacturing Center, Research Institute. King Fahd University of Petroleum and Minerals. Dhahran 31261, Saudi Arabia
(Received 10 April 1997: accepted20 October L997)
Abstract
This paper introduces neural networks technique for wind speed prediction and compares its performance with an autoregressive model. First, we studied the statistical characteristics of mean monthly and daily wind speed in Jcddah, Saudi Arabia. The autocorrelation coefficients are computed and the correlogram is found compatible with the real diurnal variation of mean wind speed. The stochastic time series analysis is found to be suitable for the description of autoregressive model that involves a time lag of one month for the mean monthly prediction and one day for the mean daily wind speed prediction. The results on a testing data indicate that the neural network approach outperforms the AR model as indicated by the prediction graph and by the root mean square errors. © 1998 Elsevier Science Ltd. All rights reserved.
