• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• 한국수자원학회논문집
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• 제45권6호
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• pp.607-616
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• 2012

본 연구는 AWS 관측강우정보를 이용하여 실시간 유량예측을 수행할 경우 적용가능한 예측선행시간 및 정확도를 평가하고자 하는데 그 목적이 있다. 이를 위해 남한강 상류유역을 대상유역으로 선정하였으며 2006~2009 홍수기간에 대해 SURF 모형을 구축하였다. 관측유량 자료동화 수행 유무에 따른 모의유량은 관측유량을 잘 모의하며 유효성지수를 이용하여 자료동화 효과를 분석한 결과에서 충주댐 32.08%, 달천 51.53%, 횡성 39.70%, 여주 18.23%가 개선된 것으로 나타났다. 첨두유량 발생시간 이전 가상의 현재시점까지의 AWS 관측강우정보를 이용하여 유량예측 적용성을 평가한 결과 허용오차 20% 범위 내에서 첨두유량은 충주 11시간, 달천 2시간, 횡성 3시간, 여주 5시간, 유출용적은 충주 13시간, 달천 2시간, 횡성 4시간, 여주 9시간 이내에서 예측이 가능한 것으로 나타났다. 따라서 유역의 지체효과로 인해 관측강우만을 이용하여 적정 예측시간에 대해서 실시간 첨두유량 예측이 가능할 것으로 판단된다.

• 한국게임학회 논문지
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• 제5권2호
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• pp.3-9
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• 2005

인터렉티브 리얼 타임 3D(Interactive real-time 3D)는 유저가 가상의 3차원 세계를 탐색하고 또한 몰입하는 폼의 컨텐츠를 경험할 수 있게 해준다. 다른 매체와는 다르게, 인터렉티브 리얼 타임 3D의 사용자(user)는, 디지털 3D의 구조에서의 작용과 반작용이 즉시 일어나는 "실시간"에서 진행되는 프로세스에서 능동적인 역할을 수행한다.

• International journal of advanced smart convergence
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• 제7권2호
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• pp.86-94
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• 2018

This study focuses on presenting the IT program module provided by real - time forecasting and database of the voluntary disclosure quality measure in order to solve the problem of capital cost due to information asymmetry of external investors and corporate executives. This study suggests a model of the algorithm that the quality of real - time voluntary disclosure can be provided to all investors immediately by IT program in order to deliver the meaningful value in the domestic capital market. This is a method of generating and analyzing real-time or non-real-time prediction models by transferring the predicted estimates delivered to the Big Data Log Analysis System through the statistical DB to the statistical forecasting engine.

• 한국경영과학회지
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• 제24권1호
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• pp.79-98
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• 1999

There have been a number of empirical studios on the effectiveness of Judgmental adjustment to statistical forecasts Generally the results have been mixed. This study examined the impact of the reliability and the source of the additionally presented reference forecast upon the revision process in a longitudinal time series forecasting task with forecast support systems. A 2-between(reliability ＆ source). 2-within(seasonality ＆ block) factorial experiment was conducted with post-graduate students using real time series. Judgmental adjustment was found to improve the accuracy of initial eyeballing irrespective of the reliability of an additionally presented forecast. But it did not outperform the dampened reference forecast. No effect was found of the way the source of the reference forecast was framed. Overall the subjects anchored heavily on their Initial forecast and relied too little on the reference forecast irrespective of its reliability. Moreover they did not improve at the task over time, despite immediate outcome feedback.

• International journal of advanced smart convergence
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• 제7권3호
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• pp.73-78
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• 2018

This study focuses on presenting the IT program module provided by BKLS measure in order to solve the problem of capital cost due to information asymmetry of external investors and corporate executives. Barron at al(1998) set up a BKLS measure to guide the market by intermediate analysts. The BKLS measure was measured by using the changes in the analyst forecast dispersion and analyst mean forecast error squared. This study suggests a model of the algorithm that the BKLS measure can be provided to all investors immediately by IT program in order to deliver the meaningful value in the domestic capital market as measured. This is a method of generating and analyzing real-time or non-real-time prediction models by transferring the predicted estimates delivered to the Big Data Log Analysis System through the statistical DB to the statistical forecasting engine. Because BKLS measure is not carried out in a concrete method, it is practically very difficult to estimate the BKLS measure. It is expected that the BKLS measure of Barron at al(1998) introduced in this study and the model of IT module provided in real time will be the starting point for the follow-up study for the introduction and realization of IT technology in the future.

• 산업경영시스템학회지
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• 제43권2호
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• pp.1-13
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• 2020

There are several methods of peak-shaving, which reduces grid power demand, electricity bought from electricity utility, through lowering "demand spike" during On-Peak period. An optimization method using linear programming is proposed, which can be used to perform peak-shaving of grid power demand for grid-connected PV+ system. Proposed peak shaving method is based on the forecast data for electricity load and photovoltaic power generation. Results from proposed method are compared with those from On-Off and Real Time methods which do not need forecast data. The results also compared to those from ideal case, an optimization method which use measured data for forecast data, that is, error-free forecast data. To see the effects of forecast error 36 error scenarios are developed, which consider error types of forecast, nMAE (normalizes Mean Absolute Error) for photovoltaic power forecast and MAPE (Mean Absolute Percentage Error) for load demand forecast. And the effects of forecast error are investigated including critical error scenarios which provide worse results compared to those of other scenarios. It is shown that proposed peak shaving method are much better than On-Off and Real Time methods under almost all the scenario of forecast error. And it is also shown that the results from our method are not so bad compared to the ideal case using error-free forecast.

• Journal of Electrical Engineering and Technology
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• 제2권2호
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• pp.152-156
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• 2007

This paper proposes a method for real-time control of both capacitors and ULTC in a distribution system to reduce the total power loss and to improve the voltage profile over the course of a day. The multi-stage consists of the off-line stage to determine dispatch schedule based on a load forecast and the on-line stage generates the time and control sequences at each sampling time. It is then determined whether one of the control actions in the control sequence is performed at the present sampling time. The proposed method is presented for a typical radial distribution system with a single ULTC and capacitors.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• 한국태양에너지학회 논문집
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• 제33권6호
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• pp.62-69
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• 2013

In this paper, Korea Institute of Energy Research, building integrated renewable energy monitoring system that utilizes solar power generation forecast data forecast model is proposed. Renewable energy integration of real-time monitoring system based on monitoring data were building a database and the database of the weather conditions and to study the correlation structure was tailoring. The weather forecast cloud cover data, generation data, and solar radiation data, a data mining and time series analysis using the method developed models to forecast solar power. The development of solar power in order to forecast model of weather forecast data it is important to secure. To this end, in three hours, including a three-day forecast today Meteorological data were used from the KMA(korea Meteorological Administration) site offers. In order to verify the accuracy of the predicted solar circle for each prediction and the actual environment can be applied to generation and were analyzed.