###### Abstract  
In recent years, wind energy has become remarkably popular among renewable energy sources due to its low installation costs and easy maintenance. Having high energy potential is of great importance in the selection of regions where wind energy investments will be made. In this study, the wind power potential in Canakkale Province, located in the northwest of Turkiye, is examined, and the wind speed is estimated using hourly and daily data over a one-year period. The data, including 12 different meteorological parameters, were taken from the Turkish State Meteorological Service. The two-parameter Weibull and Rayleigh distributions, which are the most widely preferred models in wind energy studies, are employed to estimate the wind power potential using hourly wind speed data. The graphical method is implemented to calculate the shape (k) and scale (c) parameters of the Weibull distribution function. Daily average wind speed estimation is performed with artificial neural network-genetic algorithm (ANN-GA) and ANN-particle swarm optimization (ANN-PSO) hybrid approaches. The proposed hybrid ANN-GA and ANN-PSO algorithms provide correlation coefficient values of 0.94839 and 0.94042, respectively, indicating that the predicted and measured wind speed values are notably close. Statistical error indices reveal that the ANN-GA model outperforms the ANN-PSO model. wind speed estimation; artificial neural network; genetic algorithm; particle swarm optimization; weibull distribution; rayleigh distribution +
Footnote †: journal: Applied  
0000-0001-6500-799X/00/0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-0  
Physical models, which are based on the use of weather data such as temperature, humidity, pressure, terrain, and obstacles, are preferred to achieve more accurate results for long-term prediction. It should be noted here that [PERSON] et al. [3] divided the wind speed forecasting process into four classes according to time horizons: very short-term, short-term, medium-term, and long-term. If the prediction is made for less than 30 min, it is called very short-term prediction; if it is 30 min to 6 h, it is called short-term prediction; if it is 6 h to 24 h, it is called medium-term prediction; and if it is 1 day to 7 days, it is called long-term prediction. These models require large computational resources, since complicated mathematical equations are implemented. Numerical Weather Prediction (NWP) is one of the best-known examples of this type of model in the literature. Most of the early studies performed in the literature on wind speed estimation are based on physical models. A review of the use of NWP models for wind energy assessments is presented by [PERSON] et al. [4]. Statistical methods are generally appropriate for the short-term prediction of wind data. In statistical methods, large amounts of data are analyzed to determine the relationship between historical wind data and weather, and then this relationship is used to forecast future wind values. Unlike physical methods, there is no requirement to use complex mathematical equations in statistical methods. The Weibull and Rayleigh distribution models are the two most widely used types of this approach. Several studies have been conducted on the estimation of wind speed and wind power by using Weibull and Rayleigh probability density functions (PDFs) [5]. It has been emphasized in [6; 7] that the Weibull function gave more accurate results in the estimation of wind energy potential when compared with the Rayleigh function. To determine the shape (k) and scale (c) parameters of the Weibull function, different techniques existing in the literature were reviewed, and the most frequently used techniques in the literature are the Maximum Likelihood Method (MLM) [8; 9; 10; 11; 12; 13; 14; 15; 16; 17], the Graphical Method (GM) [8; 10; 11; 12; 14; 15; 16; 18; 19], the Energy Pattern Factor Method (EPFM) [9; 15; 16; 18; 20] and the Least-Squares Regression (LSR) method [11; 12; 13; 16].