Earticle

Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. A wide variety of techniques/algorithms for load forecasting has been reported in many literatures. These techniques are as follows: multiple linear regression, stochastic time series, general exponential smoothing, state space and Kalman filter, knowledge-based expert system approach (fuzzy method and artificial neural network). These techniques have improved the accuracy of the load forecasting. In recent 10 years, many researchers have focused on artificial neural network and fuzzy method for the load forecasting. In this paper, we propose an algorithm of a hybrid load forecasting method using fuzzy linear regression and general exponential smoothing and considering the sensitivities of the temperature. In order to consider the lower load of weekends and Monday than weekdays, fuzzy linear regression method is proposed. The temperature sensitivity is used to improve the accuracy of the load forecasting through the relation of the daily load and temperature. And the normal load of weekdays is easily forecasted by general exponential smoothing method. Test results show that the proposed algorithm improves the accuracy of the load forecasting in 1996.

Load Forecasting is essential in the electricity market to the participants to manage the market efficiently and stably. Therefore, we review the past researches of load forecasting and investigate various characteristics of load variation. Finally, future studies are discussed.

In this paper, the design of WDM network using the traffic estimation modeling is implemented. Because of the lack of data of real traffic volumes, the information of statistic data is used. using the modeling results, the WDM channels is assigned for each node, and the network is simulated using OPNET simulation tools. As a result, the realistic WDM network design for Korea topology is proposed.

This paper describes the Weekly Load Forecasting Expert System(Named WLoFy) which was developed and implemented for Korea Electric Power Corporation(KEPCO). WLoFy was designed to provide user oriented features with a graphical user interface to improve the user interaction. The various forecasting models such as exponential smoothing, multiple regression, artificial nerual networks, rult-based model, and relative coefficient model also have been included in WLofy to increase the forecasting accuracy. The simulation based on historical data shows that the weekly forecasting results form WLoFy is an improvement when compared to the results from the conventional methods. Especially the forecasting accuracy on special days has been improved remakably.

The yearly load forecasting system has been developed for the economic and secure operation of electric power system. It forecasts yearly peak load and thereafter deduces hourly load using the top-down approach. Relative coefficient model has been applied to estimate peak load of a specific date or a specific day of the week. It is equipped with graphic user interface which enables a user to easily access to the system. Yearly average forecasting error may be reduced to $2{\sim}3$(%) only if we can forecast summer-time temperature correctly.

It is commonly accepted that the demand forecasting data play a vital role in deciding strategic variables such as the optimal market entry time, the price structure and the production capacity etc. In case of the new product, however, it is hard to apply the well known regression-type methodologies. In this study, we survey the characteristics of various types of new product demand forecasting(NPDF) methodologies which are useful in case the historical data are not available. Further, we explore the possibility of incorporating the NPDF methodologies and develope the unified infra-structure of the NPDF methodologies. Finally we propose an integrated prototype of the NPDF model.

All most factories producing briquets in Seoul are currently located at residential districts. The coal dusts scattered from these factories are making serious problems in living. To get rid of this environmental pollution, moving thesis factories to places of mining coals is considered now. For doing this project, first, annual briquet demands are forecasted using regression analysis and then, optimal factory capacities to be built are obtained based on forecast demands using LP. To validate factory capacities obtained above simulation is conducted using SLAM II.

IPO 관련 연구는 주로 공모가격 결정에 초점을 두고 다양하게 진행되어 왔다. 본 연구는 공모가격 결정 이전 단계인 수요예측 과정을 중심으로 기관투자자의 투자 의사결정 요인을 분석하였다. 2017년 1월부터 2023년 12월까지 수요예측에 참여한 542개 기업을 대상으로 기업특성변수, 재무특성변수, 공모특성변수의 독립변수를 사용하여 기관투자자의 투자의사결정의 직접적 결과물인 기관경쟁율, 참여강도, 기관확약, 공모밴드 선택을 종속변수로 하여 기관투자자의 투자의사 결정요인에 대한 실증연구를 진행하였다. 분석결과 종속변수별로 유의한 변수는 차별적으로 나타났으나, IT섹터여부, PER적용배수의 크기, 코스닥지수와의 이격도는 공통적으로 중요한 요인임을 확인할 수 있었다. 한편, 증권신고서상에 기재되어 있는 기업특성, 전년도 실적을 기반으로 산출된 재무자료는 기관투자자의 의사결정에 유의하지 않았다. 분석을 위해 SUR(Seemingly Unrelated Regression) 모형(기관경쟁율, 참여강도, 기관확약)과 다항로짓 모형(공모밴드 선택)을 사용하였다.

Various studies on IPOs have been conducted and have mainly focused on determinants of the public offering price. In this study, we analyzed the investment decision-making factors of institutional investors by focusing on the book-building process (a step before the public offering price decision). We conducted an empirical study on the determinants of institutional investors’ investment decisions based on data from 542 companies that participated in the book-building process from January 2017 to December 2023. As a result of the analysis, the significant variables were differentiated by the dependent variables. We found that the IT sector, size of the applied multiple, and disparity in the KOSDAQ index were essential factors in common. However, company characteristics written in the prospectus and financial data calculated based on the previous year’s performance were not significant in terms of institutional investors' decision-making. For the analysis, we used a seemingly unrelated regression (SUR) model (institutional competition rate, participation intensity, and mandatory holding pledge) and a multinomial logit model (selection of price band).

The purpose of this study is to estimate and forecast the tourism demand function,using panel data analysis method. In this study, both inbound and outbound demandare estimated. To be more specific number of tourists from four major countries,USA, Japan, China, and UK to Korea(inbound demand) or from Korea to fourcountries(outbound demand) is estimated using panel data analysis. The empiricalresults show that foreign exchange rate(FX), income level(GDP: gross domesticproduct), and price level(CPI: consumer price index) are important factors having astrong influence on tourism demand. In result, the time-serial and cross-sectionalrelationship between four big countries and Korea is well explained byinbound/outbound tourism demand model estimated by panel data analysis method.

Many countries has been deploying the various policy for international business herb. Korea government has been pursued the free economic zone policy for fostering international business competency in the Northeast Asian. The Yellow Sea Free Economic Zone has been designated in 2007, vision is the high-tech hub of international cooperation, the main purpose are contained creating high-tech industry cluster development, fostering cutting-edge industry, export to China, and advanced value-added logistics. National Industrial Complex 3 and 4 Chungnam province's industrial parks is located in the Yellow Sea Free Economic Zone within 15 ㎞ Radius, and industrial integration of the northwest region is increasing. However, the key infrastructure to support the logistics facility in the Yellow Sea Free Economic Zone is very insufficient and the logistics hub development in the Chungnam western region is very weak. The purpose of this study, first, we will review needs about the west coast logistics hub. Second, we will suggest the size and nature granted yards of logistic park. Third, we will set up the introduction feature of logistics park. To achieve the purpose, we will analyze the transportation demand forecasting, environment of the surrounding area, and linkages the Yellow Sea Free Economic Zone and other area. We will propose the necessity of logistic park composition through this analysing.