• Atmosphere
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• v.18 no.3
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• pp.199-206
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• 2008

This study employs a structural time series method in order to model and estimate stochastic trend of surface temperatures of the globe, Northern Hemisphere, and Northeast Asia ($20^{\circ}N{\sim}60^{\circ}N$, $100^{\circ}E{\sim}150^{\circ}E$). For this study the reanalysis data CRUTEM3 (CRU/Hadley Centre gridded land-surface air temperature Version 3) is used. The results show that in these three regions range from $0.268^{\circ}C$ to $0.336^{\circ}C$ in 1997, whereas these vary from $0.423^{\circ}C$ to $0.583^{\circ}C$ in 2007. The annual mean temperature over Northeast Asia has increased by $0.031^{\circ}C$ in 2007 compared to 1997. The climate change in surface temperatures over Northeast Asia is slightly higher than that over the Northern Hemisphere.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.7
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• pp.54-61
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• 2015

This paper presents short-term wind farm power forecasting method using multivariate analysis and time series. Based on factor analysis, the proposed method makes new independent variables which newly composed by raw independent variables such as wind speed, ramp rate, wind power. Newly created variables are used in the time series model for forecasting wind farm power. To demonstrate the improved accuracy, the proposed method is compared with persistence model commonly used as reference in wind power forecasting using data from Jeju Island. The results of case studies are presented to show the effectiveness of the proposed forecasting method.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.161-168
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• 2004

A single-index model is useful in fields which employ multidimensional regression models. Many methods have been developed in parametric and nonparametric approaches. In this paper, posterior inference is considered and a wavelet series is thought of as a function approximated to a true function in the single-index model. The posterior inference needs a prior distribution for each parameter estimated. A prior distribution of each coefficient of the wavelet series is proposed as a hierarchical distribution. A direction $\beta$ is assumed with a unit vector and affects estimate of the true function. Because of the constraint of the direction, a transformation, a spherical polar coordinate $\theta$, of the direction is required. Since the posterior distribution of the direction is unknown, we apply a Metropolis-Hastings algorithm to generate random samples of the direction. Through a Monte Carlo simulation we investigate estimates of the true function and the direction.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.955-969
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• 2003

The classical Fourier analysis, which is the typical frequency domain approach, is used to detect periodic trends that are of the sinusoidal shape in time series data. In this article, using a sequence of periodic step functions, describes an adaptive Fourier series where the patterns may take general periodic shapes that include sinusoidal as a special case. The results, which extend both Fourier analysis and Walsh-Fourier analysis, are applies to investigate the shape of the periodic component. Through the real data, compare the goodness-of-fit of the model using two methods, the adaptive Fourier method which is proposed method in this paper and classical Fourier method.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.17-29
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• 2007

The major purpose of this article is the comparison of estimation method with Newton-Raphson, Kalman-filter, and prediction method with Kalman prediction. Conditional expectation in space time bilinear(STBL) model, which is a very powerful and parsimonious nonlinear time-series model for the space time series data can be viewed as a set of time series collected simultaneously at a number of spatial locations and time points, and which have appeared in a important applications areas: geography, geology, natural resources, ecology, epidemiology, etc.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1309-1317
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• 2012

Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.496-498
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• 2005

This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent the complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated linear operators to be matched. Also by using the power series, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.918-922
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• 2016

As the number of wind farms operating has increased, the interest of the central unit commitment and dispatch for wind power has increased as well. Wind power forecast is necessary for effective power system management and operation with high wind power penetrations. This paper presents the centralized wind power forecasting method, which is a forecast to combine all wind farms in the area into one, using time series models. Also, this paper proposes a prediction model modified with wind forecast error compensation. To demonstrate the improvement of wind power forecasting accuracy, the proposed method is compared with persistence model and new reference model which are commonly used as reference in wind power forecasting using Jeju Island data. The results of case studies are presented to show the effectiveness of the proposed wind power forecasting method.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.889-898
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• 2017

In this research multiple change-points estimation for South Korean electricity generation data is considered. We analyze the South Korean electricity data via deterministically trending dynamic time series model with multiple structural changes in trends in a Bayesian approach. The number of change-points and the timing are unknown. The goal is to find the best model with the appropriate number of change-points and the length of the segments. A genetic algorithm is implemented to solve this optimization problem with a variable dimension of parameters. We estimate the structural change-points for South Korean electricity generation data and Nile River flow data additionally.

• Journal of IKEEE
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• v.24 no.4
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• pp.1011-1016
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• 2020

We propose a framework called Stacked Gated Recurrent Unit - Infrequent Residual Analysis (SG-IRA) that detects anomalies in time-series data that can be trained on streams of raw sensor data without any pre-labeled dataset. To enable such unsupervised learning, SG-IRA includes an estimation model that uses a stacked Gated Recurrent Unit (GRU) structure and an analysis method that detects anomalies based on the difference between the estimated value and the actual measurement (residual). SG-IRA's residual analysis method dynamically adapts the detection threshold from the population using frequency analysis, unlike the baseline model that relies on a constant threshold. In this paper, SG-IRA is evaluated using the industrial control systems (ICS) datasets. SG-IRA improves the detection performance (F1 score) by 5.9% compared to the baseline model.