• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.150-150
• /
• 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.

• Journal of Korea Water Resources Association
• /
• v.45 no.6
• /
• pp.607-616
• /
• 2012

The objective of this study is to evaluate the valid forecast lead time and the accuracy when AWS observed rainfall data are used for real-time river flow forecast. For this, Namhan river basin is selected as study area and SURF model is constructed during flood seasons in 2006~2009. The simulated flow with and without the assimilation of the observed flow data are well fitted. Effectiveness index (EI) is used to evaluate amount of improvement for the assimilation. EI at Chungju, Dalcheon, Hoengsung and Yeoju sites as evaluation points show 32.08%, 51.53%, 39.70% and 18.23% improved, respectively. In the results of the forecasted values using the limited observed rainfall data in each forecast time before peak flow occur, the peak flow under the 20% tolerance range of relative error at Chungju, Dalcheon, Hoengsung and Yeoju sites can be simulated in forecast time-11h, 2h, 3h and 5h and the flow volume in the same condition at those sites can be simulated in forecast time-13h, 2h, 4h and 9h, respectively. From this results, observed rainfall data can be used for real-time peak flow forecast because of basin lag time.

• Journal of Korea Game Society
• /
• v.5 no.2
• /
• pp.3-9
• /
• 2005

Interactive real-time 3D enables users to explore virtual three dimensional environments and also experience contents in an absorbing fashion. Unlike other media, Interactive real-time 3D users take an active role in the process of "real-time fashion" where action and reaction occur instantly in a digital 3D structure. Once the components and origins of interactive real-time 3D is made, it is possible making principles of the beauty that help decide success or failure of real-time 3D in two way system. Substantial real-time 3D has not yet passed 10 years so it was unable to make sufficient precedents of fundamental artistic value based upon the credibility of the media. The goal is to explain the new form of design in relation to general principles of arts at the same time to understand the technical definition better. Concepts of historical documentation are explained with an example of categorization of recent video game and recent technology. This thesis concludes with rough forecast on the future interactive real time 3D. Since the medium began relatively recently and is developing in the rapid pace, recent analyses, though clear forecast is difficult, tend to investigate potential directions to some level the field allows.

• International journal of advanced smart convergence
• /
• v.7 no.2
• /
• pp.86-94
• /
• 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.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.24 no.1
• /
• pp.79-98
• /
• 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
• /
• v.7 no.3
• /
• pp.73-78
• /
• 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.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.43 no.2
• /
• pp.1-13
• /
• 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.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.147-147
• /
• 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.

• Journal of Electrical Engineering and Technology
• /
• v.2 no.2
• /
• pp.152-156
• /
• 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.

• Journal of the Korean Solar Energy Society
• /
• v.33 no.6
• /
• pp.62-69
• /
• 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.