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    ESTIMATION OF WIND SPEED USING ARTIFICIAL NEURAL NETWORKS CASE STUDY – LIBYA
    (2021-02-07) Abuzawaida, Shaker Salem A.
    Worldwide, electrical power is recognized as the main factor behind improving living standards. Therefore, the safe operation of electrical energy is required for national energy management. For this purpose, accurate estimates are needed to assess electricity demand. Before the wind power plant installation, it is necessary to determine the appropriate locations for the turbine location in feasibility studies and to measure the wind speed in the relevant region. These studies can be done with simulation and wind speed estimation. Within the scope of this study, wind speed estimation has been made for Tajora city in Libya using ANN (artificial neural network) with Levenberg-Marquardt (LM) learning algorithm. A portion of the total one-year data consisting of hourly data obtained from Libya Meteorology Center has been used for the training of ANN, test and validation. ANN structure has been tested using 10, 20, 30, 40 and 50 neurons, and the number of neurons required for the best prediction has been determined. The accuracy analysis of the estimation made by using the estimation results obtained by the Levenberg-Marquardt algorithm (LMA), mean square error (MSE), and determination coefficient (R2) have been performed. According to the obtained results, the model with the best performance is the Levenberg-Marquardt algorithm with 10 neurons and the R2 and MSE values of the model are 0.99980 and 0.000243, respectively. Therefore, the wind velocity estimation values made at the specified location with the limited meteorological data used can be obtained very close to the measured values and it has been shown that the wind speed can be estimated within acceptable limits.