Earticle

1일 최대전력 부하 예측 자료는 계통의 경제적 운용과 전력 감시에 필수적이므로 정확한 예측기법이 요구된다. 신경회로망이나 퍼지이론을 한 예측비법의 장점은 정도(精度)가 높고 운용하기가 편리한 점은 있으나 학습시간이 길고, 부하가 급변할 때는 예측오차가 크게 발생한다. 본 연구에서는 이러한 단점을 개선하기 위하여 새로운 예측 기법을 제시하였으며 예측결과에서 타당성이 입증되었다.

Since daily peak load forecasted data are essential to economic operation and power monitor, the technique of accurate forecasting is needled. The chief advantage of forecasting technique using neural network and fuzzy theory is high accuracy and operative implicity but the loaming time is long, and it makes large forecasting error when the load changes rapidly. This paper has resented a new forecasting technique to improve those faults and the forecasting technique prove to be valid by forcasted results.

The study on forecasting tourism demand is a crucial step that must be undertaken before initiating the development of a tourist destination. Tourism development requires significant financial investment, and the capital recovery process extends over a long operational period following project completion. Therefore, demand analysis is a critical process that involves estimating the number of tourists who will visit the developed tourism facilities. This estimation is essential for designing facilities of an appropriate scale based on the anticipated visitor numbers. Previous studies on tourism demand analysis have predominantly focused on evaluating tourist satisfaction after the completion of tourist destinations, often limiting the scope to operational resorts. In contrast, this study conducts demand analysis prior to the development of the tourism destination. The methodology employed in this research integrates various approaches. Specifically, it involves analyzing time-series data from similar resorts in the vicinity of the target tourism destination, along with the number of domestic and international tourists visiting the target area and the province to which it belongs. After a detailed regional analysis, a specific adjustment rate is applied to forecast demand. The predicted number of visitors is then used to calculate the required facility space to accommodate them. The gravity model was utilized to verify the predicted annual number of visitors. The findings indicate that the annual number of tourists visiting Chuncheon Wido Tourism Resort is estimated to be 870,007. This figure is 13,297 fewer than the 883,304 visitors predicted using the mixed model, resulting in a margin of error of 1.5%. This suggests minimal variance between the two models. resulting in a margin of error of 1.5%, suggesting minimal variance between the two models. Consequently, the estimated number of visitors derived from this research has been applied to determine the necessary facility space, and the architectural permits for Chuncheon Wido Tourism Resort are currently being processed based on these findings.

실제로 관찰되는 전력수요 통계는 관찰되지 않는 전력수요관리부분이 포함되어 있다. 그러므로 미래의 전력수요를 예측하는데 수요관리량을 어떻게 감안할 것인가에 대해서는 많은 어려움과 논란이 있다. 이에 본 연구는 미래의 전력수요를 예측하는데 사전적으로 수요관리량을 감안하는 경우와 감안하지 않는 경우를 비교 분석하였다. 이 두 방식을 통계적 방법을 활용하여 비교, 검토하였으나 두 방식간의 유의적 차이를 찾지 못했다. 이러한 점은 향후 수요관리를 감안한 전력수요를 합리적으로 예측하는데 활용될 수 있다.

Actual electricity demand data contains demand management effects. Therefore there is a lot of difficulties and debates how to estimate electricity demand taking demand management effects into account. This study analyzes the differences between forecasts of peak demand without demand management and those of peak demand after demand management using statistical inferences. The result is that there is nothing different statistically between them. We can use this statistical inferences to forecast future electricity demand more accurately under continuous demand management scheme.

Demand side management (DSM) program has been frequently used for reducing the system peak load because it gives utilities and independent system operator (ISO) a convenient way to control and change amount of electric usage of end-use customer. Planning and operating methods are needed to efficiently manage a DSM program. This paper presents a planning method for DSM program. A planning method for DSM program should include an electric load forecasting, because this is the most important factor in determining how much to reduce electric load. In this paper, load forecasting with the temperature stochastic modeling and the sensitivity to temperature of the electric load is used for improving load forecasting accuracy. The proposed planning method can also estimate the required day, hour and total capacity of DSM program using Monte-Carlo simulation. The results of case studies are presented to show the effectiveness of the proposed planning method.

디지털 컨버전스 신규사업은 IT를 통한 경제 활성화에 기여도가 높은 분야이다. 이러한 분야의 신규 제품 및 서비스의 성공을 위한 수요 분석은 경영전략 수립에 핵심 요소이나 유사 사례가 희귀하고 성장 단계별로 고객 수용의 특성이 상이하여 그 예측에 많은 어려움이 있다. 이와 관련하여 본 논문에서는 학계와 산업계의 심층 분석을 위한 관련 이론과 위성DMB 사업의 고객 수용도 파악 시장 조사 결과를 체계적으로 제시하였다. 본 논문에서 정리된 내용은 전국의 소비자 1,000명에 대한 설문 결과를 바탕으로 하였으며 Bass의 기술확산 모형과 Rogers의 확산수용자모형을 이용하였다. 본 논문에서 제시된 위성DMB 수요예측, 성장단계 분석, 성장단계별 표적고객, 콘텐츠 성향, 단말기, 가격, 사업자 채택 요소 등의 시사점은 위성DMB 서비스 및 유사 디지털 컨버전스 사업의 정책 수립 및 고객관계전략 등 향후 연구에 유용성 있게 활용 될 수 있다.

The importance of new business establishment in the area of digital convergence has been emphasized not just because of telecommunication firm's diversification, but for it's impact to national economy growth and technological innovation. Though market analysis for this new products and services are so critical to developing effective managerial strategy and policy making, the lack of similar experiences and dynamics of customer behavior makes it hard to correctly estimate them. In this paper, using the market survey data for satellite DMB services, we illustrate various findings analyzed with reference to Bass model, Innovation diffusion theory by Rogers, and so on. The findings include demand forecast, innovation diffusion pattern, customer characteristics, customer preferences such as contents, devices, service fees, and supplier selections. Data analysis results and theoretical illustrations presented in this paper have significant implications for further in-depth strategy researches and policy making for digital convergence related new businesses.

Fuzzy linear regression method has been used for short-term load forecasting of the special day in the previous researches. However, considerable load forecasting errors would be occurring if a special day is located on Saturday or Monday. In this paper, a new load forecasting method for the consecutive holidays is proposed with the consideration of the power demand variation rate. In the proposed method, a exponential smoothing model reflecting temperature is used to short-term load forecasting for Sunday during the consecutive holidays and then the loads of the special day during the consecutive holidays is calculated using the hourly power demand variation rate between the previous similar consecutive holidays. The proposed method is tested with 10 cases of the consecutive holidays from 2009 to 2012. Test results show that the average accuracy of the proposed method is improved about 2.96% by comparison with the fuzzy linear regression method.

본 연구에서는 공장용지 면적을 예측하기 위한 통계적 추정을 다루었다. 공장용지에 대해서는 1981년부터 2003년까지 자료가 존재하며, 2004년 이후에는 공장용지보다 좁은 개념인 산업단지 면적에 대한 조사 자료만 존재한다. 한국산업단지공단에서는 2009년 10월 표본조사를 실시하여 당해의 공장용지 면적을 추정하였으며, 동 조사 시 향후 5개년의 공장용지면적에 대한 수요를 조사한 바 있다. 본 연구에서는 과거 절단된 자료를 여러 통계모형을 사용하여 적절히 대체할 수 있는 수요예측모형을 도출하고, 표본조사에 의한 추정치와 통계적 모형에 의한 대체값들을 융합하는 평활기법으로 향후 공장용지 수요를 예측하는 방법을 다루었다.

In this paper, we have considered the problems of the estimation of the gross areas of a factory site relating to the areas of industrial complex lands based on a statistical forecasting model and the results of a sampling survey. In respect to the data of a gross areas of a factory site, we have only the sizes from 1981-2003. In 2009, the Korea Industrial Complex Corp. conducted a sampling survey to estimate its bulk size, and investigate the demands of its sizes for the next five years. In this study, we have adopted the sampling survey results, and have created a statistical growth model for the gross areas of a factory site to improve the prediction for the areas of a factory site. The three-different parts of data: the results of areas of a factory site by Korea National Statistical Office, imputation results by the statistical forecasting model, and sampling survey results have used as the basis for analysis. The combination of the three-different parts of data has created a new forecasting value of the areas of a factory site through the spline smoothing method.

The purpose of this study was to prove research assumption that the econometric model will minimize the forecast error rather than applying time series analysis for the long-run tourism demand forecast in Jeju Special Self-Governing Province. Results showed that the prediction error could be minimized when applying the econometric model as an independent variable with only one GDP per capita, rather than applying a time series analysis. In Jeju Special Self-Governing Province, tourism demand fluctuated sharply due to the increase in low-cost airlines, high popularity of Jeju Olle trail, diplomatic conflicts with China, and the COVID-19 Pandemic. This study suggests to use an econometric model using per capita GDP to accurately predict long-run tourism demand of Jeju Special Self-Governing Province.

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

The accurate short-term load forecasting is essential for the efficient power system operation and the system marginal price decision of the electricity market. So far, errors of load forecasting for Chuseok Holiday are very big compared with forecasting errors for the other special days. In order to improve the accuracy of load forecasting for Chuseok Holiday, selection of input data, the daily normalized load patterns and load forecasting model are investigated. The efficient data selection and daily normalized load pattern based on fuzzy linear regression model is proposed. The proposed load forecasting method for Chuseok Holiday is tested in recent 5 years from 2006 to 2010, and improved the accuracy of the load forecasting compared with the former research.

저가항공이 2005년 8월 국내 첫 취항한 이후 여객수요는 괄목한 성장을 이루었다. 특히 저가요금을 제공하는 저가항공은 급변하는 환경에 대처하기 위하여 단기 수요예측이 필요하다. 이를 토대로 항공노선, 스케줄, 직원수, 공항시설 및 항공기 공급계획 등을 수립ㆍ조정하기 때문이다. 국내 5개 저가항공 시장을 제주권과 내륙권으로 구분하여 여객운송을 2005년 8월-2012년 7월까지 총 84개의 시계열 자료를 분석하였다. 항공수요의 계절 변동을 고려하여 Box and Jenkins(1976)의 계절형 ARIMA (Autoregressive Integrated Moving Average) 모형을 이용하여 항공수요를 예측하였다. 분석결과 제주권 노선은 일정한 계절성 패턴을 보여 항공수요가 점차적으로 증가할 것으로 예측되지만 내륙권 노선인 경우 정체, 포화된 상태로 항공수요가 감소할 것으로 예측된다. 본 연구는 처음으로 계절형 ARIMA 모형을 활용하여 국내 저가항공 수요예측을 체계적이고 과학적인 방법으로 시도하였다는 데에 학문적 공헌도가 있다 하겠다. 또한 모수절약의 법칙에 근거하여 가장 단순화한 모형으로써 수요예측에 대한 방향성을 제시하였다는 것에 또 다른 공헌도를 찾을 수 있다.

Since inauguration of domestic Low Cost Carrier(LCC) on Aug. 2005, passenger air traffic demand of LCC has been remarkably increased. Especially, LCC selling low fare needs short-term demand forecasting in order to meet rapid change of situation. On the basis of it, that’s why air route, schedule, staff, airport facilities and aircraft supply plan etc. will be established and adjusted. After Five Domestic LCC passenger market was divided into Jeju route and Inland route, passenger transportation was analyzed with 84 time-series data per month from Aug. 2005 to Jul. 2012. In view of seasonal change, the air traffic demand was forecasted through Seasonal ARIMA model of BOX and Jenkins(1976). As a result of an analysis, the air traffic demand in Jeju route is expected to increase gradually : the air traffic demand in Inland route, on the other hand, is forecasted to decrease as stagnation or saturation. increase of air passenger from now on. Finally, this study seems to make a great academic contribution which is first attempt to identify the domestic air traffic demand of LCC by systematic and scientific methods with Seasonal ARIMA model. In addition, we can also find another contribution to show the direction of demand forecasting with the most simplified model based on principle of parsimony.

The purpose of this study is to select the most appropriate quantitative forecasting model to predict the Japanese travel demand to Korea in the near future. And also, This research progressed from the results of previous study that the forecasting accuracy of model could be changed according to data apply to forecasting model. The models to evaluate consisted of exponential smoothing model, ARIMA model and regression model from among the models confirmed in previous studies on accuracy. The data for analysis has applied the number of Japanese tourist in 15 years. Each model was assessed based on MAPE(mean absolute percentage error) and the probability of improvement of forecasting accuracy was additionally evaluated by combined techniques. The results of model estimation indicates that Winter's exponential smoothing model was measured as the best model(MAPE=8.72%) in individual model, and also the combination model consisted of Winters exponential smoothing model and cubic regression model was relatively outperformed(MAPE=8.63%) the other combined techniques.

Purpose ? This study examines the impact of data length and demand characteristics on forecast accuracy in short-term operational planning. Design/Methodology/Approach ? Using daily demand data from a manufacturing company, we compare the performance of exponential smoothing (ETS) and the Hidden Markov Model (HMM) across different data lengths ranging from two to 24 months. Forecast accuracy is evaluated over a 21-day horizon, focusing on the second and third weeks. In addition, a rolling forecasting approach is employed to validate out-of-sample performance. Findings ? The results indicate that the optimal data length varies depending on demand characteristics. For stable demand, ETS shows superior performance, particularly with 12 months of data. In contrast, for highly variable demand, HMM demonstrates improved performance, especially with shorter data lengths, such as six months. However, its performance is not consistent across all conditions. Furthermore, forecast accuracy decreases as the forecast horizon increases, with significantly higher errors observed in the third week. Research Implications ? These findings highlight the importance of selecting appropriate data length and forecasting models based on demand characteristics and planning horizons. The study contributes to forecasting research by incorporating data length as a key analytical factor and provides practical insights to improve demand forecasting strategies in short-term operational settings.

In this study, we explore the long-term demand forecasting of high-price medical equipments based on logistic and Bass diffusion model. We analyze the specific pattern of each equipment's diffusion curve by interpreting the parameter estimates of Bass diffusion model. Our findings are as follows. First, ultrasonic imaging system, CT are in the stage of maturity and so, the future demands of them are not too large. Second, medical image processing unit is between growth stage and maturity stage and so, the demand is expected to increase considerably for two or three years. Third, MRI is in the stage of take-off and Mammmography X-ray system is in the stage of maturity but, estimates of the potential number of adopters based on logistic model is considerably different to that based on Bass diffusion model. It means that additional data for these two equipments should be collected and analyzed to obtain the reliable estimates of their demands. Fourth, medical image processing unit have the largest q value. It means that the word-of-mouth effect is important in the diffusion of this equipment. Fifth, for MRI and Ultrasonic system, q/p values have the relatively large value. It means that collective power has an important role in adopting these two equipments.

Objectives: In this study, we explore the demand forecasting of Advanced Diagnostic imaging Equipment. Methods: The analysis was based on logistic diffusion model. We analyze the specific pattern of each equipment’s diffusion curve by interpreting the parameter estimates of logistic diffusion model. Results: Our findings are follows. First, Computed tomography is in the stage of saturation and so, the future demands of that is not too large. Second, Magnetic resonance imaging (MRI) is expected that it will take about 5 years to reach saturation, and further growth is expected to continue. Third, Positron emission tomography (PET) has been shown to be saturated, and therefore, it is not expected that there will be a rapid increase in demand in the future. However, since demand data has been declining since 2000, it is said that additional data collection is required to reliably predict future demand. Conclusions: As a result of analyzing the demand for three major advanced diagnostic imaging equipment, it was found that the domestic market is generally in saturation. Therefore, a future research task will be to predict and analyze the demand for advanced diagnostic imaging equipment in consideration of the government’s policy changes.

The fourth industrial revolution encourages manufacturing industry to pursue a new paradigm shift to meet customers' diverse demands by managing the production process efficiently. However, it is not easy to manage efficiently a variety of tasks of all the processes including materials management, production management, process control, sales management, and inventory management. Especially, to set up an efficient production schedule and maintain appropriate inventory is crucial for tailored response to customers' needs. This paper deals with the optimized inventory policy in a steel company that produces granule products under supply contracts of three targeted on-time delivery rates. For efficient inventory management, products are classified into three groups A, B and C, and three differentiated production cycles and safety factors are assumed for the targeted on-time delivery rates of the groups. To derive the optimized inventory policy, we experimented eight cases of combined safety stock and data analysis methods in terms of key performance metrics such as mean inventory level and sold-out rate. Through simulation experiments based on real data we find that the proposed optimized inventory policy reduces inventory level by about 9%, and increases surplus production capacity rate, which is usually used for the production of products in Group C, from 43.4% to 46.3%, compared with the existing inventory policy.

Many organizations are currently adjusting the statistical forecasts with qualitative factors. However, so for a few forecasting system with adjustment process have been developed. They have difficulties in managing knowledge and estimating the quantity of adjustment. In this study, the forecasting support system adopting the frame based knowledge representation and containing the decision making scheme for adjustment is proposed to overcome these difficulties. According to the experiments, the proposed system improves the forecasting performance on gasoline case.

Forecasting the future values of an observed time series is an important problem in many areas, including economics, traffic engineering, production planning, sales forecasting, and stock control. The purpose of this paper is aimed to discover the more efficient forecasting model through the parameter estimation and residual analysis among the quantitative method such as Winters' exponential smoothing model, Box-Jenkins' model, and Kalman filtering model. The mean of the time series is assumed to be a linear combination of known functions. For a parameter estimation and residual analysis, Winters', Box-Jenkins' model use Statgrap and Timeslab software, and Kalman filtering utilizes Fortran language. Therefore, this paper can be used in real fields to obtain the most effective forecasting model.

This paper proposes a new algorithm for maintenance scheduling with considering load forecast uncertainty. The proposed algorithm is based on the equivalent load of effective load carrying capability(ELCC) of generators. The uncertainty of forecasted load is considered as a normal distribution probability density function. For maintenance scheduling, reserve levelization method and risk levelization method are used in this study. To test the algorithm, we applied the proposed method to IEEE reliability test system(IEEE RTS). As a result, we verified the validity of the proposed method.

The traffic forecast is one of the most important analysis objects in the urban transportation planning process. The results of traffic forecast are the most widely used informations and give a critical influence on the major decision makings in the transportation planning process. Thus, they should be as much accurate and credible data, and evaluated to determine whether they are enough reliable to directly use in the planning process. However, the evaluation process is usually overlooked or abbreviated with a few exceptions according to the size and character of the project. Even though a planner or engineer tries to evaluate the assignment results, he/she is usually faced with certain difficulties since there are no established criteria and methods for the accuracy evaluation. Accordingly, the main purpose of this research placed on establishing the criteria and methods for the accuracy evaluation of the assignment results. The secondary purpose was to evaluate which assignment technique produces the most accurate assignment results by applying the established evaluation criteria and methods to an actual network. The research found that the proposed evaluation methods well operated in testing the accuracy of assignment results with few limits on application. Also, the incremental assignment was found to provide the best assignment results of existing assignment techniques (Stochastic, Iterative, Incremental, Equilibrium assignment) for the Seoul city network applied.