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    ESTIMATING WIND ENERGY POTENTIAL WITH PREDICTING BURR LSM PARAMETERS: A DIFFERENT APPROACH
    (Yildiz Technical Univ, 2018) Kose, Bayram; Duz, Murat; Guneser, M. Tahir; Recebli, Ziyaddin
    Estimating wind energy potential and wind speed frequency are important for planning wind energy conversion plants. Probability distribution functions are utilized to model wind speed distributions. In this study, an estimation was model designed by using the least squares method to predict the wind speed density with the Burr distribution, which has not been studied before. To confirm this model, the annual data of eight different weather stations were analysed, and the results were compared with the Weibull distribution model, which is the most popular one in the literature. For predicting the parameters of both models least square method and maximum likely methods were used. Regarding the comparison results, the performance of designed estimation model (Burr LSM) is higher than the Weibull distribution models, especially for the locations with higher average wind speeds. The results show that the Burr LSM is better than the others for seven of eight weather stations in terms of the power density.
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    Parameter estimation of the wind speed distribution model by dragonfly algorithm
    (Gazi Univ, Fac Engineering Architecture, 2023) Kose, Bayram; Aygun, Hilmi; Pak, Semih
    Purpose: In modelling wind speed by Weibull probability distribution function (Wpdf) for potential calculation of wind energy and wind speed characterization, the purpose of the study is to estimate the distribution parameters.Theory and Methods: The performance of the proposed method has been evaluated by comparing not only the classical methods which are the moment method (MM) and the least squares method (LSM) but also metaheuristic optimization algorithms which are Dragonfly Algorithm (DA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Determination coefficient (R2) and root mean square error (RMSE) were used to evaluate the performance of these parameter estimation methods. Results: Dat obtaine from 6 measurement stations were used in the study. The results show that while the DA method gives the best performance according to the determination coefficient (R2) criterion in all stations, it provides the best performance in 2 stations according to the root mean square error (RMSE) criterion. In addition, it was observed that the DA method showed better performance in all stations compared to the LSM method.Conclusion: It is seen that the proposed DA algorithm for wind energy potential calculation can be used to estimate Weibull and two-component mixed Weibull distribution parameters.