9 research outputs found
āļāļēāļĢāļāļĢāļ°āļĄāļēāļāļāđāļēāđāļāļĨāļĩāđāļĒāļāļĢāļ°āļāļēāļāļĢāļāđāļ§āļĒāļ§āļīāļāļĩāļāļāļāđāļāļīāļĨāļĨāđEstimating the Population Mean Using Searls Approach
āđāļāļ§āļāļīāļāļ§āļīāļāļĩāļāļāļāđāļāļīāļĨāļĨāđāļāļđāļāļāļģāļĄāļēāđāļāđāđāļāļ·āđāļāļāļąāļāļāļēāļāļąāļ§āļāļĢāļ°āļĄāļēāļāļāđāļēāđāļāļĨāļĩāđāļĒāļāļĢāļ°āļāļēāļāļĢ āđāļāļĒāļāļēāļĻāļąāļĒāđāļāļ§āļāļīāļāļāļĩāđāļāđāļāļāļāļĢāļēāļāļāđāļēāļŠāļąāļĄāļāļĢāļ°āļŠāļīāļāļāļīāđāļāļēāļĢāđāļāļĢāļāļąāļāļāļāļāļāļĢāļ°āļāļēāļāļĢ āđāļāļ·āđāļāļāļģāđāļŦāđāļāļąāļ§āļāļĢāļ°āļĄāļēāļāļāđāļēāđāļāļĨāļĩāđāļĒāļāļĢāļ°āļāļēāļāļĢāļāļĩāđāļāļąāļāļāļēāļĄāļēāļāļēāļāļ§āļīāļāļĩāļāļāļāđāļāļīāļĨāļĨāđāļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļĄāļēāļāļāļ§āđāļēāļāļąāļ§āļāļĢāļ°āļĄāļēāļāļāđāļēāđāļāļĨāļĩāđāļĒāļāļĢāļ°āļāļēāļāļĢāđāļāļāļāļąāđāļāđāļāļīāļĄ āđāļĨāļ°āđāļāļāļāļāļ§āļēāļĄāļāļĩāđāļĄāļĩāļāļļāļāļāļĢāļ°āļŠāļāļāđāđāļāļ·āđāļāļāļģāđāļŠāļāļāđāļŦāđāđāļŦāđāļāļāļķāļāļ§āļīāļāļĩāļāļēāļĢāļāļāļāđāļāļīāļĨāļĨāđ āđāļĨāļ°āļĒāļāļāļąāļ§āļāļĒāđāļēāļāļāļąāļ§āļāļĢāļ°āļĄāļēāļāļāđāļēāđāļāļĨāļĩāđāļĒāļāļĢāļ°āļāļēāļāļĢāļāļĩāđāđāļāđāļ§āļīāļāļĩāļāļēāļĢāļāļāļāđāļāļīāļĨāļĨāđāļĄāļēāļāļąāļāļāļēāļāļąāļ§āļāļĢāļ°āļĄāļēāļāļāđāļē āđāļāđāđāļāđ āļāļąāļ§āļāļĢāļ°āļĄāļēāļāļāđāļēāđāļāļĨāļĩāđāļĒāļāļĢāļ°āļāļēāļāļĢāļāđāļ§āļĒāļ§āļīāļāļĩāļāļāļāđāļāļīāļĨāļĨāđāđāļāļāļēāļĢāđāļĨāļ·āļāļāļāļąāļ§āļāļĒāđāļēāļāļŠāļļāđāļĄāđāļāļāļāđāļēāļĒ āđāļĨāļ°āļāļąāļ§āļāļĢāļ°āļĄāļēāļāļāđāļēāđāļāļĨāļĩāđāļĒāļāļĢāļ°āļāļēāļāļĢāļāđāļ§āļĒāļ§āļīāļāļĩāļāļāļāđāļāļīāļĨāļĨāđāđāļāļāļēāļĢāđāļĨāļ·āļāļāļāļąāļ§āļāļĒāđāļēāļāđāļāļāļāļĨāļļāđāļĄāļāļąāđāļāđāļāļĩāļĒāļ§āđāļāļāļāđāļēāļĒThe Searls approach was employed to develop the estimation of population mean based on the coefficient of variation of the known population. This approach leads to a higher efficiency of the Searls estimator than that of the traditional one. This article presents the Searls approachâs methods and provides examples of the population mean estimators using the Searls approach: the estimator using the Searls approach for simple random sampling without replacement and the estimator using the Searls approach for single-stage cluster sampling with simple random sampling without replacemen
Decision analysis on generation capacity of a wind park
The investment decision on generation capacity of a wind park is difficult when wind studies or data are neither available nor sufficient to provide adequate information for developing a wind power project. Although new measurement is possible but it is definitely time consuming. To determine the optimum capacity, decision analysis techniques are proposed in this paper to cope with uncertainties arising from wind speed distribution and power-speed characteristics. The wind speed distribution is modeled from the measured data, the Rayleigh distribution, and the Weibull distribution. The power-speed curve of a wind turbine from cut-in speed to rated speed is modeled by using linear, parabolic, cubic, and quadratic characteristics. The optimization model is formulated as a mixed-integer nonlinear programming problem. The constraints are considered as interval bounds so that a set of feasible solutions is obtained. The optimum solution can be determined by using the profit-to-cost and profit-to-area ratios as performance metrics of investment. Decision analysis rules are then applied to overcome the uncertainty problem and to refine the investment plan. The proposed procedure has been tested with the wind power project of the Electricity Generating Authority of Thailand.Decision analysis Mixed-integer nonlinear programming Optimization Uncertainty Wind park