• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.360-365
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• 2003

In this paper, GMDH(Croup Method of Data Handling) algorithm which is proved to be more excellent in efficiency and accuracy of practical use of data is applied to electric power demand forecasting. As a result, it became much easier to make a choice of input data and make an exact prediction based on a lot of data. Also, we considered both economy factors(GDP, export, import, number of employee, number of economically active population and consumption of oil) and climate factors(average temperature) when forecasting. We assumed target forecast period from first quarter 1999 to first quarter 2001, and suggested more accurate forecasting method of electric power demand by using 3-step computer simulation processes(first process for selecting optimum input period, second for analyzing time relation of input data and forecast value, and third for optimizing input data) for improvement of forecast precision. The proposed method can get 0.96 percent of mean error rate at target forecast period.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1019-1023
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• 2018

In this paper sensitivity analysis of temperature on special day electricity demand of land and Jeju Island is performed. The basic electricity demand per 3 hours is defined as electricity demand that reflects the GDP effect without the temperature influence. The temperature sensitivity per 3 hours is calculated through the relationship between special day electricity demand normalized to basic electricity demand and temperature. In the future, forecast error will be improved if the temperature sensitivity per 3 hours is applied to the special day load forecasting.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.247-252
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• 2013

Korea gas corporation (KOGAS) is responsible for the whole sale of natural gas in the domestic market. It is important to forecast the daily demand of city gas for supply and demand control, and delivery management. Since there is the autoregressive characteristic in the daily gas demand, we introduce a modified autoregressive model as the first step. The daily gas demand also has a close connection with the outdoor temperature. Accordingly, our second proposed model is a temperature-based model. Those two models, however, do not meet the requirement for forecasting performances. To produce acceptable forecasting performances, we develop a weighted average model which compounds the autoregressive model and the temperature model. To examine our proposed methods, the forecasting results are provided. We confirm that our method can forecast the daily city gas demand accurately with reasonable performances.

• Journal of Korean Academy of Nursing
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• v.30 no.2
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• pp.437-451
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• 2000

The purpose of this study was to estimate the forecast of bed demand for institutional long-term care for the elderly persons in Taegu Metropolitan City. The study subject was the total 1,877 elderly persons over age 65 living in Taegu. Among them 1,441 elderly persons were sampled from community and 436 were from the elderly admitted 5 general hospitals. Data collection was carried out by interview from 25 August to 25 December 1997. The measuring instrument of this study was the modified tool of CARE, MAI, PCTC, and ADL which were examined for validity and reliability. In order to forecast bed demand of Nursing Home, this study revised prediction techniques suggested by Robin. The results were as follows : 1. OLDi of Taegu City were 122,202 by the year 1998 and number of Low-Income Elderly Persons were 3,210. 2. The Level I : Senior Citizen Home $ADEMi=\frac{AQi * ASTAYi}{365 * AOCUi}$. AQi = OLDi * LADLi * NASi * ALONi * LIADLi * AUTILi. Predicted number of bed demand for Home Based. Elderly Persons were 4,210 and Low-Income Elderly Persons were 1,081 and Total Elderly Persons were 5,291 by the year 1998, 6,343 by the year 2000 and 8,351 by the 2005. 3. The Level II : Nursing Home $BDEMi=\frac{(BQ1i+BQ2i) * BSTAYi}{365 * BOCUi}$. BQ1i = OLDi * HADLi * ALONi * HIADLi BQ2i = OLDi * HADLi * FAMi * OBEDi Predicted number of demand for Total Elderly Persons were 668 by the year 1998, 802 by the year 2000 and 1,055 by the 2005. 4. The Level III : Nursing Home $CDEMi=\frac{COLDi * HDISi * CUTILi * CSTAYi}{365 * COCUi}+OQi/10$ Predicted number of demand for Total Elderly Persons were 1,899 by the year 1998, 2,311 by the year 2000 and 3,003 by the 2005. 5. Predicted number of bed demand of long-term care facilities in the year 1998 according to Levels were 4.3% among elderly persons in Taegu by Level I, 0.5% by Level II and 1.5% by Level III. Number of elderly persons in current long-term care facilities were 458 in LevelI I,284 in Level II. 6. Deficit number of bed demand of long-term care facilities were 4,833 in Level I, 384 in Level II, 1,899 in Level III for the elderly persons in Taegu Metropolitan City.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.292-294
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• 2000

This paper is presented the method peak load forecast based on multiple regression Model. Forecasting model was composed with the temperature-humidity and the discomfort index. Also the week periodicity was excluded from weekday change coefficient of two types. Forecasting result was good with about 3[%]. And, utility of presented forecast model using statistical tests has been proved. Therefore, This results establish appropriateness and fitness of forecast models using peak power demand forecasting.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.169-178
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• 2011

In this study, a forecasting engine from the user perspective is studied and developed. Characteristics of forecasting engine can be divided into a few categories, an algorithms for predicting variety of situations and the depth of algorithms based on the number and the types of data. Then applying a variety of algorithms that most closely match the predicted values for the actual value that deduce criteria for selecting an appropriate forecasting algorithm is to organize. Through the forecast quality assessment, the suggested distribution algorithm compared to the existing demand forecast algorithms is good indicators for its accuracy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.50-58
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• 2018

Intermittent demand is a demand with a pattern in which zero demands occur frequently and non-zero demands occur sporadically. This type of demand mainly appears in spare parts with very low demand. Croston's method, which is an initiative intermittent demand forecasting method, estimates the average demand by separately estimating the size of non-zero demands and the interval between non-zero demands. Such smoothing type of forecasting methods can be suitable for mid-term or long-term demand forecasting because those provides the same demand forecasts during the forecasting horizon. However, the smoothing type of forecasting methods aims at short-term forecasting, so the estimated average forecast is a factor to decrease accuracy. In this paper, we propose a forecasting method to improve short-term accuracy by improving Croston's method for intermittent demand forecasting. The proposed forecasting method estimates both the non-zero demand size and the zero demands' interval separately, as in Croston's method, but the forecast at a future period adjusted by binomial weight according to occurrence probability. This serves to improve the accuracy of short-term forecasts. In this paper, we first prove the unbiasedness of the proposed method as an important attribute in forecasting. The performance of the proposed method is compared with those of five existing forecasting methods via eight evaluation criteria. The simulation results show that the proposed forecasting method is superior to other methods in terms of all evaluation criteria in short-term forecasting regardless of average size and dispersion parameter of demands. However, the larger the average demand size and dispersion are, that is, the closer to continuous demand, the less the performance gap with other forecasting methods.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.658-663
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• IE interfaces
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• v.18 no.3
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• pp.343-349
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• Culinary science and hospitality research
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• v.5 no.1
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• pp.89-101
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• 1999

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.