• Journal of Wind Energy
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• v.15 no.2
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• pp.10-22
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• 2024

This research is a comprehensive analysis of wind power prediction sensitivity using a Long Short-Term Memory (LSTM) deep learning neural network model, accounting for the inherent uncertainties in wind speed estimation. Utilizing a year's worth of operational data from an operational wind farm, the study forecasts the power output of both individual wind turbines and the farm collectively. Predictions were made daily at intervals of 10 minutes and 1 hour over a span of three months. The model's forecast accuracy was evaluated by comparing the root mean square error (RMSE), normalized RMSE (NRMSE), and correlation coefficients with actual power output data. Moreover, the research investigated how inaccuracies in wind speed inputs affect the power prediction sensitivity of the model. By simulating wind speed errors within a normal distribution range of 1% to 15%, the study analyzed their influence on the accuracy of power predictions. This investigation provided insights into the required wind speed prediction error rate to achieve an 8% power prediction error threshold, meeting the incentive standards for forecasting systems in renewable energy generation.

• Communications for Statistical Applications and Methods
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• v.21 no.1
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• pp.105-114
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• 2014

In this paper we suggest an efficient method to estimate the distribution function using the Bezier curve, and compare it with existing methods by simulation studies. In addition, we suggest a robust version of cross-validation criterion to estimate the number of Bezier points, and showed that the proposed method is better than the existing methods based on simulation studies.

• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.75-86
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• 1993

This paper deals with the problem of estimating a reliability function for the Rayleigh distribution. Using the priors about a reliabity of real interest some Bayes estimators and Bayes credible sets are proposed and studied under squared error loss and Harris loss.

• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.523-532
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• 1997

We propose several estimators of the reliability function R of the two-parameter exponential distribution, and then compare those estimator in terms of the mean square error (MSE) through Monte Carlo method. We also consider the parametric bootstrap estimation. Using the parametric bootstrap estimator, we obtain the bootstrap confidence intervals for reliability function and compare the proposed bootstrap confidence intervals in terms of the length and the coverage probability through Monte Carlo method.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.103-114
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• 1994

For a $2 \times 2$ factorial design without the restriction of a linear model or without regard to error terms having homoscedasticity, under the null hypothesis of no interaction we can have the rank transformed F statistic for interaction converge in distribution to a chi-squared random variable with one degree of random if and only if there is only main effect.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.423-432
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• 1999

Accelerated life testing of product is commonly used to reduced test time and costs. In this papers is considered when the product is a two component system with lifetimes following the bivariate exponential distribution of Sarkar(1987) using inverse power rule model. Also we derived the maximum likelihood estimators of parameters for asymptotic normality. We compare the mean square error of the proposed estimator for the life distribution under use conditions stree through Monte Carlo simulation.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.59-70
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• 1997

We introduce several estimators of the location and the scale parameters of the two-parameter exponential distribution, and then compare these estimators by the mean square error (MSE). Using the parametric bootstrap estimators and the delete-d jackknife, we obtain the bootstrap and the delete-d jackknife confidence intervals for the location and the scale parameters and compare the bootstrap confidence intervals with the delete-d jackknife confidence intervals by length and coverage probability through Monte Carlo method.

• Journal for History of Mathematics
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• v.12 no.1
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• pp.1-12
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• 1999

The first part of this thesis discusses the types and the properties of errors, one of which makes up systematic errors of measurements, removable by detecting their causes, the other errors of accidental causes which can not be removed. The final part of this thesis deals with the historical background of the Gaussian distribution by Hershel, Hagen, Laplace and Gauss from the late 18th century to the early 19th century. It can be concluded that the accidental idea and the treatment of accidental error distribution by Gauss Is the best one based on the assumption that the most probable value of true value is the arithmetic mean of data, obtained by repeated measurements of a given quantity.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.225-230
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• 1998

By assuming a singly right cenosred sample, we propose the approximate maximum likelihood estimator (AMLE) of the scale parameter of the p-dimensional Rayleigh distribution. We compare the proposed estimator in ·terms of the mean squared error through Monte Carlo methods.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.257-265
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• 1996

We consider nonstationary autoregressive autoregressive process with infinite variance of error. In the case of infinite cariance, the limiting distribution of the estimated coefficient is different from that under the finite cariance assumption. In this paper we show that the bootstrap method can be used to approximate the distribution of ordinary least squares estimator of the coefficient in the first order random walk process with infinite variance through some empirical studies and we suggest a test procedure based on bootstrap method for the unit root test.