Abstract:The wind energy potential of the Antakya area was statistically analyzed based 8 years of wind data sets (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009). The 4-parameter Burr, 3-parameter generalized gamma, and conventional Weibull distributions were regarded as suitable statistical models for describing wind speed profiles. The suitability of the models was tested by R 2 , RMSE, chi-squared, and Kolmogorov-Smirnov analysis. According to goodness-of-fit tests, the Burr distribution was found to be more suitable than the generalized gamma or Weibull distributions for representing the actual probability of wind speed data for Antakya. Based on the capacity factors estimated by the Burr model at a hub height, the power generation potential of a commercial 330-kW wind turbine was also determined. The results show that the available wind energy potential to generate electricity in Antakya is low; consequently, wind power would be suitable only for stand-alone electrical and mechanical applications, such as water pumps, battery charging units, and local consumption in off-grid areas.
Due to fluctuating weather conditions, estimating wind energy potential is still a significant problem. Artificial neural networks (ANNs) have been commonly used in short-term and just-in-time modeling of wind power generation systems based on main weather parameters such as wind speed, temperature, and humidity. Two different datasets called hourly main weather data (MWD) and daily sub-data (DSD) are used to estimate a wind turbine power generation in this study. MWD are based on historically observed wind speed, wind direction, air temperature, and pressure parameters. Besides, DSD created with statistical terms of MWD consist of maximum, minimum, mean, standard deviation, skewness, and kurtosis values. The main purpose of this study in particular was to develop a multilinear model representing the relationship between the DSD with the calculated minimum (P min ) and maximum (P max ) power generation values as well as the total power generation (P sum ) produced in a day by a wind turbine based on the MWD. While simulation values of the turbine, P min , P max , and P sum , were used as the separately dependent parameters, DSD were determined as independent parameters in the estimation models.Stepwise regression was used to determine efficient independent parameters on the dependent parameters and to remove the inefficient parameters in the exploratory phase of study. These efficient parameters and simulated power generation values were used for training and testing the developed ANN models. Accuracy test results show that interoperability framework models based on stepwise regression and the neural network models are more accurate and more reliable than a linear approach.
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