Prediction model of wind speed using hybrid artificial neural network based on Levenberg-Marquardt algorithm

Anas Elmejdki, Khalid Hati, Hilal Essaouini


In this paper, a new method is developed to model the wind speed data that is considered as a function of seasonal wind variations. A hybrid artificial neural network (HANN) is investigated based on the Weibull distribution model. The presented HANN model predicts wind speed data with seasonal and chronological characteristics similar to real wind data. The design of the wind farm was implemented using MATLAB software. The suggested model has been applied and validated with wind data collected from the site of Tangier-MED in Morocco over two years, 2015 and 2016. The errors in terms of mean absolute percentage error MAPE and root mean square error RMSE are respectively 0.011 and 0.067 in 2015.


Artificial neural network; Levenberg-Marquardt; Optimization; Weibull distribution; Wind energy; Wind speed

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