Earticle

This paper introduces a basic design for the short-term load forecasting system using a commercial data base. The proposed system uses a hybrid load forecasting method using fuzzy linear regression for forecasting of weekends and Monday and general exponential smoothing for forecasting of weekdays. The temperature sensitive is used to improve the accuracy of the load forecasting during the summer season. MS-SQL Sever has been used a commercial data base for the proposed system and the database is operated by ADO(ActiveX Data Objects) and RDO(Remote Data Object). Database has been constructed by altering the historical load data for the past 38 years. The weather iDormation is included in the database. The developed short-term load forecasting system is developed as a user friendly system based on GUI(Graphical User interface) using MFC(Microsoft Foundation Class). Test results show that the developed system efficiently performs short-term load forecasting.

This paper suggests a long-term distribution area load forecasting algorithm which offers basic data for distribution planning of power system. To build forecasting model, 4-level hierarchical spatial structure is introduced: System, Region, Area, and Substation. And, each spatial load can be decided proportional to its portion in the higher level. This paper introduces the horizon year loads to improve the forecasting results. And, this paper also introduces an effective load transfer algorithm to improve forecasting stability in case of new or stopped substations. The proposed model is applied to the load forecasting of KEPCO system composed of 16 regions, 85 areas and 761 substations, and the results are compared with those of econometrics model to verify its validity.

This paper proposes an improved algorithm of the daily peak load forecasting for Korean Thanksgiving Day and the Lunar New Year's day. So far, many studies on the short-term load forecasting have been made to improve the accuracy of the load forecasting. However, the large errors of the load forecasting occur i case of Korean Thanksgiving Day and the Lunar New Year's Day. In order to reduce the errors of the load forecasting, the fuzzy linear regression method is introduced and a good selection method of the past load pattern is presented. Test results show that the proposed algorithm improves the accuracy of the load forecasting.

서비스 품질의 향상을 기하기 위해서는 제품의 고장에 따른 부품의 적시 공급이 매우 중요하다. 이를 위해서는 항시 서비스 부품의 수요를 어느 정도 정확히 예측해야 한다. 본 논문은 주 제품(prime product)에 부분 의존적인 서비스 부품의 수요 예측 방법을 제시한다. 부분 종속적인 서비스 부품은 특정 부품이 장착되는 주 부품이 여러 개인 경우를 지칭한다. 기존의 방법은 주로 판매되는 부품의 수량에 의거하여 미래의 부품 수요량을 예측하는 단순 시계열이나 수요 확산 및 대체 모형에 근거하고 있는 실정이다. 본 논문은 주 제품의 폐기율과 부품의 고장율의 추정을 통한 부품의 수요 예측 방법을 제시한다. 부품 수요에 대한 다양한 수식 개발을 통해 부품 수요의 구간 추정식이 제시된다. 또한 주 제품에 장착되는 부품의 고장율이 주 제품에 무관하다는 가정하에 부품의 총 수요를 추정하는 절차를 제시하고 시뮬레이션을 통해 이러한 가정의 타당성을 고찰한다.

본 연구를 통하여 전력계통의 송변전 계획에 필요한 장기 지구별 수요예측 산법을 제안하였다. 소규모 지구의 수요예측을 위한 중회귀 모형 도입시 단순한 다항식 회귀모형만으로는 장기예측을 하는데 한계가 있으므로 다항회귀 과정을 변형하거나, 새로운 기능을 보완하여 예측정확도를 높이려는 시도가 수행 되어왔다. 본 논문에서는 장기 예측시 나타나는 미래의 예측 수요의 과도한 변화를 감소시켜 예측 정확도를 개선할 수 있는 수평년도수요를 도입하였으며, 종래 추세 분석에서 난점으로 지적되어 온 변전소의 신설 및 폐지에 따른 수요이전으로 야기되는 예측의 불안정성을 개선하였다. 제안한 산법을 검증하기 위하여 우리나라 실계통에 적용하였다.

This study attempts to identify the most accurate quantitative forecasting technique for measuring the future level of demand for food ＆ beverage in super deluxe hotel in Seoul, which will subsequently lead to determining the optimal level of purchasing food ＆ beverage. This study, in detail, examines the food purchasing system of H hotel, reviews three rigorous univariate time series models and identify the most accurate forecasting technique. The monthly data ranging from January 1990 to December 1997 (96 observations) were used for the empirical analysis and the 1998 data were left for the comparison with the ex post forecast results. In order to measure the accuracy, MAPE, MAD and RMSE were used as criteria. In this study, Box-Jenkins model was turned out to be the most accurate technique for forecasting hotel food ＆ beverage demand among selected models generating 3.8％ forecast error in average.

The demand prediction becomes an essential mean to utilize efficiently finite traffic facilities and to provide the optimized schedules for transportation system. The demand prediction is one of the critical complex management schemes for distibuting resources of transportation service by means of computer system. The construction of a prediction model is based on data granulization, followed by processing the raw input data and evaluating the predicted output values. A large number of economic-social parameters are also to be implemented in conventional prediction models which are only based on a sequence of past data. The proposed prediction models are classified by static and dynamic characteristics and its performances are evaluated utilizing computer simulation.

It is very important for electric utility to expand generating facilities and transmission equipments in accordance with the increase of electricity demand. Regional electricity demand forecasting is among the most important step for long-term investment and power supply planning. The main objectives of this paper are to develop the methodologies for forecasting regional load demand. The Model consists of four models, regional economy, regional electricity energy demand, areal electricity energy demand. and areal peak load demand. This paper mainly suggests regional electricity energy demand model and areal peak load demand. A case study is also presented.

A knowledge based forecasting system for special days has been developed for the economic and secure operation of electric power system. If-then production rules has been adopted in this system to be used in various environmental conditions. Graphic user interfaces enables a user to access easily to the system. The simulation based on the historical data have shown that the forecasting result was improved remarkably when compared to the results from the conventional statistical methods. The forecasting results can be used for power system operational planning to improve security and economy of the power system.

The purpose of this study is to identify demand and methods of projection, including to prove the variables affecting the fluctuation of visitors and to analyze the relationship between these variables in National Park. Statistical analysis method (Multiple Linear Regression Analysis, ANOVA, and Model diagnostics) was carried out by computer program SAS/pc. 13 variables (1. Total Population, 2. Per Capita PDI, 3. Employment Ratio of S.O.C. & others, 4. NO. of Passenger Car, 5. Length of Roads, 6. Leisure Expenditure of Farm Household, 7. Leisure Expenditure of Urban Household, 8. Price Index, 9. NO. of Bus, 10. Exchange on Dollars, 11. Export, 12. Import, and 13. Visitors in National Park) had been used to this study. The scope of time period is during the last 17 years (1970-1986). The results were as follows; 1) Participation depends only on the specific characteristics of the economic factors (Price Index and Leisure Expenditure of Urban Household). These factors are the importance factors directly affecting the participation of visitors. The statistical Model for projecting the visitors in National Parks is the function of "Visitors in National Parks (thousand)=14915+0.210311*Leisure Expenditure of Urband Household (won)-157.835619*Price Index(1985=100)" 2) The external factors affecting the participation depends upon the interelated features of availability and accessibility (NO. of Passenger Car, Length of Roads, and NO. of Bus) of recreation resources or sites, and the economic factors (Per Capita PDI, Export, and Import). These factors are the factors indirectly affecting the participation of visitors. 3) The participation depends on the specific characteristics of demographic factors (Total Population and Employment Ratio of S.O.C. & others). These factors are the factors indirectly affecting the participation of visitors. 4) The unexpected fluctuation of yearly visitors depends on oil shock or inflation (1971, 1973-1974, 1979-1980), promulgation of national emergency decrees (1971-1972, 1974-1975, 1979-1980), and national events (assassination of president Park's wife, Madame Yuk in 1974 and president Park I 1979).