9 research outputs found

    āļāļēāļĢāļ›āļĢāļ°āļĄāļēāļ“āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļ›āļĢāļ°āļŠāļēāļāļĢāļ”āđ‰āļ§āļĒāļ§āļīāļ˜āļĩāļ‚āļ­āļ‡āđ€āļ‹āļīāļĨāļĨāđŒEstimating the Population Mean Using Searls Approach

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    āđāļ™āļ§āļ„āļīāļ”āļ§āļīāļ˜āļĩāļ‚āļ­āļ‡āđ€āļ‹āļīāļĨāļĨāđŒāļ–āļđāļāļ™āļģāļĄāļēāđƒāļŠāđ‰āđ€āļžāļ·āđˆāļ­āļžāļąāļ’āļ™āļēāļ•āļąāļ§āļ›āļĢāļ°āļĄāļēāļ“āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļ›āļĢāļ°āļŠāļēāļāļĢ āđ‚āļ”āļĒāļ­āļēāļĻāļąāļĒāđāļ™āļ§āļ„āļīāļ”āļ—āļĩāđˆāļ•āđ‰āļ­āļ‡āļ—āļĢāļēāļšāļ„āđˆāļēāļŠāļąāļĄāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāđŒāļāļēāļĢāđāļ›āļĢāļœāļąāļ™āļ‚āļ­āļ‡āļ›āļĢāļ°āļŠāļēāļāļĢ āđ€āļžāļ·āđˆāļ­āļ—āļģāđƒāļŦāđ‰āļ•āļąāļ§āļ›āļĢāļ°āļĄāļēāļ“āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļ›āļĢāļ°āļŠāļēāļāļĢāļ—āļĩāđˆāļžāļąāļ’āļ™āļēāļĄāļēāļˆāļēāļāļ§āļīāļ˜āļĩāļ‚āļ­āļ‡āđ€āļ‹āļīāļĨāļĨāđŒāļĄāļĩāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāļ āļēāļžāļĄāļēāļāļāļ§āđˆāļēāļ•āļąāļ§āļ›āļĢāļ°āļĄāļēāļ“āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļ›āļĢāļ°āļŠāļēāļāļĢāđāļšāļšāļ”āļąāđ‰āļ‡āđ€āļ”āļīāļĄ āđāļĨāļ°āđƒāļ™āļšāļ—āļ„āļ§āļēāļĄāļ™āļĩāđ‰āļĄāļĩāļˆāļļāļ”āļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļ™āļģāđ€āļŠāļ™āļ­āđƒāļŦāđ‰āđ€āļŦāđ‡āļ™āļ–āļķāļ‡āļ§āļīāļ˜āļĩāļāļēāļĢāļ‚āļ­āļ‡āđ€āļ‹āļīāļĨāļĨāđŒ āđāļĨāļ°āļĒāļāļ•āļąāļ§āļ­āļĒāđˆāļēāļ‡āļ•āļąāļ§āļ›āļĢāļ°āļĄāļēāļ“āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļ›āļĢāļ°āļŠāļēāļāļĢāļ—āļĩāđˆāđƒāļŠāđ‰āļ§āļīāļ˜āļĩāļāļēāļĢāļ‚āļ­āļ‡āđ€āļ‹āļīāļĨāļĨāđŒāļĄāļēāļžāļąāļ’āļ™āļēāļ•āļąāļ§āļ›āļĢāļ°āļĄāļēāļ“āļ„āđˆāļē āđ„āļ”āđ‰āđāļāđˆ āļ•āļąāļ§āļ›āļĢāļ°āļĄāļēāļ“āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļ›āļĢāļ°āļŠāļēāļāļĢāļ”āđ‰āļ§āļĒāļ§āļīāļ˜āļĩāļ‚āļ­āļ‡āđ€āļ‹āļīāļĨāļĨāđŒāđƒāļ™āļāļēāļĢāđ€āļĨāļ·āļ­āļāļ•āļąāļ§āļ­āļĒāđˆāļēāļ‡āļŠāļļāđˆāļĄāđāļšāļšāļ‡āđˆāļēāļĒ āđāļĨāļ°āļ•āļąāļ§āļ›āļĢāļ°āļĄāļēāļ“āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļ›āļĢāļ°āļŠāļēāļāļĢāļ”āđ‰āļ§āļĒāļ§āļīāļ˜āļĩāļ‚āļ­āļ‡āđ€āļ‹āļīāļĨāļĨāđŒāđƒāļ™āļāļēāļĢāđ€āļĨāļ·āļ­āļāļ•āļąāļ§āļ­āļĒāđˆāļēāļ‡āđāļšāļšāļāļĨāļļāđˆāļĄāļ‚āļąāđ‰āļ™āđ€āļ”āļĩāļĒāļ§āđāļšāļšāļ‡āđˆāļēāļĒThe 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 approach’s 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

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    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

    A review of uncertainty characterisation approaches for the optimal design of distributed energy systems

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