Aşır Genç

Estimation of Wind Power Potential Using Weibull Distribution

The main objective of the present study is to estimate wind power potential using the two Weibull parameters of the wind speed distribution function, the shape parameter k (dimensionless) and the scale parameter c (m/s). In this regard, a methodology that uses three various techniques (maximum likelihood, least squares, and method of moments) for estimating the Weibull parameters was given first. The methodology was then applied to a region in Turkey. Finally, the parameter techniques were compared to Monte-Carlo simulation in different sample sizes, and the best parameter estimation techniques belonging to the sample sizes were also determined.

Assessment of wind characteristics for energy generation

Wind technology in Turkey has gained considerable maturity over the last five years, and wind energy projects are becoming commercially attractive in the country. In practice, it is essential to describe the variation of wind speeds for optimizing the design of the systems resulting in less energy generating costs. The wind variation for a typical site is usually described using the so-called Weibull distribution. In this study, the two Weibull parameters of the wind speed distribution function, the shape parameter k (dimensionless) and the scale parameter c (m/s), were computed from the wind speed data for Aksehir in Konya, located in Central Anatolia in Turkey (latitude: 38.35° and longitude: 31.42°). Wind data, consisting of hourly wind speed records over a 6 year period, 1997–2002, were obtained from the Aksehir State Meteorological Station. Based on the experimental data, it was found that the numerical values of both Weibull parameters (k and c) for Aksehir vary over a wide range. The yearly values of k range from 1.756 to 2.076, while those of c are in the range of 2.956 to 3.444. Average seasonal Weibull distributions for Aksehir are given. The wind speed distributions are represented by Weibull distribution and also by Rayleigh distribution with a special case of the Weibull distribution for k = 2. The Rayleigh distribution is found to be suitable to represent the actual probability of wind speed data for the site studied.

New Shrinkage Parameters for the Liu-type Logistic Estimators

The binary logistic regression is a widely used statistical method when the dependent variable has two categories. In most of the situations of logistic regression, independent variables are collinear which is called the multicollinearity problem. It is known that multicollinearity affects the variance of maximum likelihood estimator (MLE) negatively. Therefore, this article introduces new shrinkage parameters for the Liu-type estimators in the Liu (2003) in the logistic regression model defined by Huang (2012) in order to decrease the variance and overcome the problem of multicollinearity. A Monte Carlo study is designed to show the goodness of the proposed estimators over MLE in the sense of mean squared error (MSE) and mean absolute error (MAE). Moreover, a real data case is given to demonstrate the advantages of the new shrinkage parameters.

Simple Correlations for Estimating the Energy Production of Turkey

Industrialization and population increases make the availability of potential energy in most areas a problem of great importance. Energy is considered to be a prime agent in the generation of wealth and also a significant factor in economic development. Most of the locations in Turkey have abundant energy resources, and energy utilization technologies can be profitably applied to these regions. In this study, we analyze the current status of Turkeys energy resources in terms of energy production and present simple corrections with high correlation coefficients for future projections. It is expected that this study will he helpful in developing highly applicable and productive planning for energy policies.

Statistical Analysis of Solar Radiation Data

Solar radiation is the most important parameter in the design and study of solar energy conversion devices. In this study, statistical methods were used to analyze the solar radiation data for the city of Konya in the Mediterranean region of Turkey. Experimental data were obtained from the State Meteorological Station in Konya over a 9 year period from 1990 to 1998. The empirical coefficients a and b of the modified Angström-type regression equation were determined, while the values of the monthly average-daily clearness index (KT ) were calculated. A nonlinear model was also developed between the monthly average daily global radiation (H) and ambient temperature. The values of the monthly average-daily hours of bright sunshine (S) varied between 9.41 and 14.58. The values of KT ranged from 0.54 to 0.60, averaged for the period studied. It may be concluded that the present model estimates the values of H for Konya reasonably well.

Statistical Analysis of Solar Radiation Data Using Cubic Spline Functions

A cubic spline-type model has been developed to analyze the avarage daily total solar radiation data. This model has been found to perform significantly better than the other regression-type models. In this study, cubic spline functions have been used to analyze the solar radiation data of 5 years from 1994 to 1998 for G zmir. The reliability of the model has also been tested with a statistical hypothesis.

Some new modifications of Kibria's and Dorugade's methods: An application to Turkish GDP data

Abstract In multiple linear regression analysis, multicollinearity is an important problem. Ridge regression is one of the most commonly used methods to overcome this problem. There are many proposed ridge parameters in the literature. In this paper, we propose some new modifications to choose the ridge parameter. A Monte Carlo simulation is used to evaluate parameters. Also, biases of the estimators are considered. The mean squared error is used to compare the performance of the proposed estimators with others in the literature. According to the results, all the proposed estimators are superior to ordinary least squared estimator (OLS).

Constitution of random intercept and slope model (RISM) as a special case of linear mixed models (LMMs) for repeated measurements data

Abstract In this paper, for the aim of modeling variance–covariance structure matrix of the response variables vector in random intercept and slope model (RISM) from linear mixed models (LMMs) for repeated measurements data, 13 different homogeneous and heterogeneous variance–covariance structure models are investigated comparatively in an application from a clinical trial.

An investigation of export–import ratios in Turkey using spline regression models

Abstract This paper examines the use of spline functions in linear, squared, and cubic spline regression models and exhibits the estimation of spline parameters from data by ordinary least squares. Determination of the number and the location of knots is central to spline regression. In this paper, we initially propose a method based on the coefficient of determination R2 related to the estimation of knots in spline regression. This proposed method as applied to export–import ratio distributions in Turkey for the years 1923–2010 determines the knots, and linear, quadratic, and cubic spline regression models are established accordingly. Results reveal that spline regression models offer better results than polynomial regression models, and that the quadratic spline regression model is the best explanatory model for export–import ratio distributions in the smoothest spline regression models.

Two-parameter ridge estimator in the binary logistic regression

ABSTRACT The binary logistic regression is a commonly used statistical method when the outcome variable is dichotomous or binary. The explanatory variables are correlated in some situations of the logit model. This problem is called multicollinearity. It is known that the variance of the maximum likelihood estimator (MLE) is inflated in the presence of multicollinearity. Therefore, in this study, we define a new two-parameter ridge estimator for the logistic regression model to decrease the variance and overcome multicollinearity problem. We compare the new estimator to the other well-known estimators by studying their mean squared error (MSE) properties. Moreover, a Monte Carlo simulation is designed to evaluate the performances of the estimators. Finally, a real data application is illustrated to show the applicability of the new method. According to the results of the simulation and real application, the new estimator outperforms the other estimators for all of the situations considered.