Earticle

본 연구는 서울 특1급 호텔의 외국인 이용객 객실수요에 대한 예측모형으로 계절 ATIMA모형과 개입 ARIMA모형을 제시하였다. 가장 적합한 예측모형이 어느 모형인지 MAPE를 기준으로 분석되었는데, 두 모형 모두 예측오차 10% 내에서 예측을 하는 것으로 나타났다 (계절 ARIMA: 5.84%, 개입 ARIMA: 5.70%). 2003년 3월에 발생한 SARS와 2003년 12월에 발생한 조류독감의 개입을 반영한 개입 ARIMA모형이 개입 이후를 예측하는데 있어서 계절 ARIMA모형에 비하여 조금 더 우수한 것으로 나타났다. 또한, 1997년 1월부터 2005년 12월까지의 전체 자료를 적용한 예측에 있어서도 계절 ARIMA모형에 비하여 개입 ARIMA모형이 큰 차이는 없지만, 더 우수한 것으로 나타났다. 따라서, 본 연구에 있어서 전체적으로 개입 ARIMA모형이 계절 ARIMA모형에 비하여 예측력이 더 높은 것으로 나타났다.

This paper presents the use of time series seasonal ARIMA and ARIMA with intervention in forecasting room demand of foreigners. The most suitable forecasting model was investigated. With MAPE as the basis, the seasonal ARIMA and ARIMA with intervention were found to forecast at within 10% error (seasonal ARIMA: 5.84%, ARIMA with intervention: 5.70%). On one side, the ARIMA with intervention uses the March 2003 SARS (severe acute respiratory syndrome) and the December 2003 pathogenic avian influenza of interventions, and the error of forecasting for the time period afterwards, from March 2003 to December 2005, has been found to be lower by a narrow margin as compared to the seasonal ARIMA. In addition, it was founded that the error of the forecasting in the ARIMA with intervention was superior to the seasonal ARIMA for the entire sample analysis of the time period of January 1997 to December 2005. Hence, in this study, ARIMA with intervention was found to have higher accuracy.

시계열 분석법에 대한 예측은 20여년 이상 연구가 계속되어오고 있다. 본 연구는 1995년부터 2004년까지 경주지역을 방문한 관광객 수를 이용하였다. 분석방법은 관광현상의 특성인 계절성을 고려한 확률적 분석방법으로 계절아리마(seaasonal ARIMA Model)모형을 이용하여 예측에 대한 정확도를 평가 하였으며, 이에 따른 관광정책 비전을 제시하고자 한다. 그 결과, 2005년 경주를 방문할 관광객 수는 최저 3.8백만에서 최대 25백만의 관광객이 경주를 방문할 것으로 예측되었으며, 예측에 대한 정확도의 평가는 2.4%로 매우 정확하게 나타났다. 이러한 예측결과로 비추어 볼 때, 경주시는 비수기의 관광객수요를 극복하여 관광객을 증가시킬 수 있는 관광정책이 요구된다.

The demand forecasting which have been studied for about 20 years in long-term time series. This study conducts a short-term forecast of tourist demand in Gyeongju with uni-variate ARIMA model. Moreover, it suggests the vision of tourism policy. The uni-variate ARIMA model takes into consideration a possible structure change of tourist in Gyeongju. ARIMA model by using time series data from 1995 to 2004.This research focused on two issues: 1) forecasting Gyeongju's monthly inbound travel demand and 2) seasonality and seasonal ARIMA model selection for monthly tourism time-series. The result showed that it forecasts a minimum of 3.8 million to almost maximum25million tourists may visit Gyeongju in 2005. And Gyeongju needs to overcome the demand of non-season tourist with tourism polity to increase the number of tourists in Gyeongju. Moreover, a systemic perspective is proposal where Gyeongju tourism forecasting and tourism marketing are considered to be integral components of order.

본 연구는 지식행정 활동의 요구수준을 필요성 차원에서 진단하고 지식행정 활동의 수요를 예측하는 변인들을 구분, 확인하였으며 이를 위해 Answer Tree 의사결정나무분석을 실시하였다. 분석모델을 설정하기 위하여 지식행정 활동 구성요소와 기반요소에 관한 선행연구를 검토하였으며, 이를 기반으로 지식행정 활동 구성요소를 목표변수로, 지식행정 기반요소와 공무원 개인별 특성 그리고 행정기관의 특성을 예측변수로 설정하였다. 연구의 분석결과, 지식행정 활동 구성요소들은 지식행정 기반요소에 따라서 그 필요성이 다르다는 것을 알 수 있으며, 공무원 개인별 특성과 행정기관의 특성은 지식조직화와 지식축적에 대해서만 예측변수로 분류, 확인되었음을 알 수 있다. 이러한 연구결과를 토대로 볼 때, 행정기관에서 어떤 지식행정 활동이 요구되는가를 예측하기 위해서는 행정기관의 지식기반 요인들 수준을 먼저 확인해야 될 필요가 있다.

This study is checked up the needs dimension with a needs level in knowledge administration activities and is classified and identified through decision tree analysis about a variable could make a prediction about demand of knowledge administration activities. In order to draw research model, this study is based on an antecedent literature toward KM and the components and a foundation factor of knowledge administration. An analysis model of this study is constructed with and the components of knowledge administration activities in target variable and a foundation factor of knowledge administration, officials individual characteristic, and level executive agency in predictable variable. The results of research showed that the components of knowledge administration activities show up differently in accordance with a foundation factor of knowledge administration and officials?individual characteristic and level executive agency are only knowledge organizing and knowledge storing up. And results of the study suggest implication that it would make sure of the level about a foundation factor of knowledge administration to forecast knowledge administration activities in public agency.

본 연구는 마케팅 분야에서 주로 사용되는 신제품확산모델(new product diffusion model)들이 기본적인 Bass 모형에 기반하여 개별 소비자의 선호구조 및 선택 과정을 반영하지 못하고, 제품이 시장에 출시되기 이전 단계에서 시장수요를 예측하지 못하는 한계를 극복하기 위한 방법론을 제시하는데 목적이 있다. 이를 위해 본 연구에서는 진술선호(stated preference) 자료에 근거한 컨조인트(Conjoint) 모형을 활용하여 개별 소비자 단계의 선택과정을 반영하고, 시장 및 기술환경의 변화에 대한 적절한 예측자료와 결합하여 그 결과를 신제품확산모델로부터 도출된 잠재시장(market potential) 추정치와 결합함으로써 신제품의 판매량을 예측한다. 실증연구에서는 국내 30인치 이상 대형TV 시장을 중심으로 CRT TV, Projection TV, LCD TV, PDP TV에 대한 시장수요를 예측하였다. 분석 결과, 소비자들은 TV 선택시 화질과 가격에 민감한 반응을 보이는 것을 알 수 있으며, 단기적으로는 가격 경쟁력이 있는 Projection TV가 높은 시장 점유율을 보이지만, 50인치 이상 LCD TV가 상용화되어 시장에 출시될 경우, LCD TV의 점유율이 다른 종류에 비하여 크게 향상될 것으로 분석되었다. 또한 TV 크기에 따른 소비자들의 선택을 살펴본 결과 50~60인치대에 비해 40인치대 크기의 TV가 많이 판매될 것으로 예상된다.

The methodological framework proposed in this paper addresses two limitations of the basic Bass model that it does not reflect competition among products; nor does it forecast demand for products that do not exist in the marketplace. In this research, we use conjoint analysis to estimate the utility function of consumers to investigate consumer preferences for product attributes. Next we estimate the dynamic price function of each competing product to reflect technological changes and the evolving market environment. Then we derive dynamic utility function by combining the static utility function and the price function. Finally, we forecast the sales of each product using estimated market share and sales data for each period, which are derived from the dynamic utility function and from the Bass diffusion model, respectively. We apply this model to South Korea's market for large-screen televisions. The results show that (1) consumers are sensitive to picture resolution and cost and (2) in the near future, should the market see the introduction of LCD TVs with screens larger than 50 inches, the high resolution and steep price drop of LCD will lead LCD TVs to capture a larger market share than TVs with other display types. Finally, our results show that TVs with 40-inch screens are preferred over TVs with larger screens.

As concerns over climate change and greenhouse gas emissions intensify, carbon neutrality has emerged as a critical global goal. Achieving this goal requires accurate electricity demand forecasting, which plays a pivotal role in maintaining grid stability and managing the variability of renewable energy. However, traditional models that focus on the mean often fail to capture the complexity of modern electricity demand patterns. To address this issue, this study proposes the Composite Variation Method(CVM) based on quantile regression. This approach reduces the bias in mean-based electricity demand forecasts and provides higher accuracy, particularly in scenarios with outliers or complex conditions. The CVM is applied to Random Forest and XGBoost models using hourly electricity demand data from Ontario, Canada. The results demonstrate a 9.75% performance improvement for Random Forest and a 1.31% improvement for XGBoost. These findings suggest that CVM enhances the precision of electricity demand forecasting, contributing to more effective energy management strategies essential for carbon neutrality.

에너지인터넷 기술의 발전과 다양한 전자기기의 보급으로 에너지소비량이 패턴이 다양해짐에 따라 수요예측에 대한 신뢰도가 감소하고 있어 발전량 최적화 및 전력공급 안정화에 문제를 야기하고 있다. 본 연구에서는 고신뢰성을 갖는 수요예측을 위해 딥러닝 기법인 Convolution neural network(CNN)과 Bidirectional Long Short-Term Memory(BLSTM)을 융합한 1Dimention-Convolution and Bidirectional LSTM(1D-ConvBLSTM)을 제안하고, 제안한 기법을 활용하여 시계열 에너지소비량대한 소비패턴을 효과적으로 추출한다. 실험 결과에서는 다양한 반복학습 횟수와 feature map에 대해서 수요를 예측하고 적은 반복학습 횟수로도 테스트 데이터의 그래프 개형을 예측하는 것을 검증한다.

As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversified patterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization of power generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neural network (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting by effectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposed deep learning method for various number of learning iterations and feature maps, and it is verified that the test data is predicted with a small number of iterations.

As the equipment of the military has recently become more sophisticated and expensive, the cost of purchasing spare parts is also steadily increasing. Therefore, demand forecast accuracy is also becoming an issue for the effective execution of the spare parts budget. This study predicts the demand by using the data of spare parts consumption of the KF-16C fighter which is being operated in the Republic of Korea Air Force. In this paper, SARIMA(Seasonal Autoregressive Integrated Moving Average) is applied to seasonal data after dividing the spare parts consumptions into seasonal data and non-seasonal data. Proposing new methods, Majority Voting and Hybrid Method, to the non-seasonal data which consists of spare parts of low consumption with unclear pattern, We want to prove that the demand forecast accuracy of spare parts improves.

The temperature changes are very important in improving the accuracy of the load forecasting during the summer. It is because the cooling load in summer contribute to the increasing of the load. This paper proposes a weekend load forecasting algorithm using the temperature change characteristic in a summer of Jeju. The days before and after weekends in Jeju, when the load curves are quite different from those of normal weekdays. The temperature change characteristic are obtained by using weekends peak load and high temperature data. And load forecasted based on the sensitivity between unit temperature changes and load variations. Load forecast data with better accuracy are obtained by using the proposed temperature changes than by using the ordinary daily peak load forecasting. The method can be used to reduce the error rate of load forecast.

Spare part management is very important to products that have large number of parts and long lifecycle such as automobile and aircraft. Supply chain must support immediate procurement for repair. However, it is not easy to handle spare parts efficiently due to huge stock keeping units. Qualified forecasting is the basis for the supply chain to achieve the goal. In this paper, we propose an agent based modeling approach that can deal with various factors simultaneously without mathematical modeling. Simulation results show that the proposed method is reasonable to describe demand generation process, and consequently, to forecast demand of spare parts in long-term perspective.

Among the various statistical factors for South Korea, the population has been steadily decreased by lower birthrate. Nevertheless, the number of household is constantly increasing amid population aging and single life style. In general, residential electricity use is more the result of the number of household than the population. Therefore, residential electricity consumption is expected to be far higher for decades to come. The existing long-term load forecasting, however, do not necessarily reflect the growth of single and two-member households. In this respect, this paper proposes the long-term load forecasting for residential users considering the effect of changes of the housing type, and in the case study the changes of the residential load pattern is analyzed for accurate long-term load forecasting.

In recent years, energy supply cases to take advantage of EMS(Energy Management System) are increasing according to high interest of energy efficiency. The important factor for essential and economical EMS operation is the supply and demand plan the hourly power demand of building load using the hierarchical clustering method of variety statistical techniques, and use the real historical data of target load. Also the estimated results of study are obtained the reliability through separate tests of validity.

Accurate midterm load forecasting is essential to preventive maintenance programs and reliable demand supply programs. This paper describes a midterm load forecasting method using autoregressive integrated moving average (ARIMA) model which has been widely used in time series forecasting due to its accuracy and predictability. The various ARIMA models are examined in order to find the optimal model having minimum error of the midterm load forecasting. The proposed method is applied to forecast 104-week load pattern using the historical data in Korea. The effectiveness of the proposed method is evaluated by forecasting 104-week load from 2011 to 2012 by using historical data from 2002 to 2010.

Weekly and monthly electric load forecasting are essential for the generator maintenance plan and the systematic operation of the electric power reserve. This paper proposes the weekly maximum electric load forecasting model for 104 weeks with the multiple regression model. Input variables of the multiple regression model are temperatures and GDP that are highly correlated with electric loads. The weekly variable is added as input variable to improve the accuracy of electric load forecasting. Test results show that the proposed algorithm improves the accuracy of electric load forecasting over the seasonal autoregressive integrated moving average model. We expect that the proposed algorithm can contribute to the systematic operation of the power system by improving the accuracy of the electric load forecasting.

제조업에 있어서 판매 후 서비스 건수와 내용 등은 향후 서비스 제공을 위한 자원배분의 효율성 증진과 서비스 품질 향상을 위해서도 매우 중요한 정보이다. 따라서 기업들은 향후 발생하는 판매 후 서비스에 대해 정확히 예측하고 그에 따라 적절히 대처하는 능력을 확보할 필요성이 제조업을 중심으로 증가하고 있다. 그러나 실제로 이들 기업들이 활용하고 있는 서비스 수요예측 방법들은 전통적인 통계적인 예측기법이거나, 시뮬레이션을 기반한 기법들이다. 예를 들면, 전통적인 통계적인 예측기법으로는 회귀분석(regression analysis)의 경우, 다양한 제품모델에 대한 판매 후 서비스 발생 패턴이 선형적인 관계가 매우 적음에도 불구하고 선형으로 가정하여 추정한다는 점과 적정한 회귀식을 가정하여야 되며, 이러한 가정이 실제 경영환경에서는 매우 어렵다는 점 등이 기존의 예측기법들의 한계점으로 지적되고 있다. 본 연구에서는 디지털 TV 모델을 생산 판매 하는 A사의 사례연구를 통하여 최근 인공지능연구에서 각광을 받고 있는 사례기반추론(case-based reasoning; CBR) 기법을 활용한 서비스 수요예측 프레임워크를 제안하고자 한다. 또한, 사례기반추론에서 핵심적인 역할 중 하나인 유사 사례추출 방법에 있어서 가장 일반적인 nearest-neighbor 방법 이외의 유사 사례추출 방법을 제안하고자 한다. 특히, 본 연구에서 제안하는 유사 사례추출 방법은 인공신경망(artificial neural network)을 활용한 자기조직화지도(Self-Organizing Maps : SOM) 군집화 기법을 활용한 유사 사례추출 방식으로 이를 활용한 서비스 수요예측 프레임워크에 구현하고, 실제 기업의 판매 후 서비스 데이터를 활용하여 본 연구에서 제안하는 서비스 수요 예측 프레임워크의 유효성을 실증적으로 검증하고자 한다.

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organize

단기 전력수요예측의 오차를 줄여 불필요한 전력생산을 이전에 방지하는 것은 매우 중요하다. 본 논문에서는 오차율이 높은 연휴 전 평일의 단기 전력수요예측 정확도를 높이기 위해 이전 평일과 주말의 데이터를 이용한 새로운 예측 방법을 제안하고, 추석연휴 전 평일에 제안한 방법을 적용하여 수요예측에 대한 오차가 개선됨을 확인하였다.

Energy planning for hybrid energy system is important to increase the flexibility in the urban community and national energy systems. Expected maximum loads, load profiles and yearly energy demands are important input parameters to plan for the technical and environmental optimal energy system for a planning area. The method for energy demand prediction has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. This method can produce 10% of errors hourly load profile from individual building to urban community. As the results of this paper, energy demand prediction system has been developed based on simulink.

Currently energy use planning council system is mandatory especially for the urban development project planned on a specified scale or more. The goal of existing demand prediction was to calculate the maximum load by multiplying energy load per unit area by building size. The result of this method may be exaggerated and has a limit in the information of period load. The paper suggests a new forecasting process based on standard unit household in order to upgrade the limit in demand prediction method of multi-family housing complex. The new process was verified by comparing actual using amount of multi-family housing complex to forecasting value of energy use plan.

This study focuses on affording a material basis for rearranging the manpower supply system in port and logistics industry for Busan's international competitiveness strategies. First of all, the current state and future plan of port and logistics industry, as Busan's main strategic industry, are reviewed. Then theoretical background are introduced for the estimate of demand. As a methodology of this research, Cubic model is applied to estimate the demand of manpower by using 10 year time series data from 1993 to 2002. This paper also surveyed the supply side of port and logistics industry manpower in Busan area. The amounts of mismatched equilibrium between the demand and the supply are measured in this study. The concluding remarks shows some suggestions for the problem of mismatch and the relating policy planning.

과학기술위성2호는 2007년 12월에 발사될 예정이다. 이 위성의 주관측기는 DREAM으로서, 주요 임무는 지구 또는 대기로부터 발생되는 복사에너지를 라디오파 대역에서 관측하는 것이다. 이 외 과학기술위성2호는 과학기술 실험용기기로서 정밀디지탈 태양센서, 2중헤드 별센서 등이 탑재되며, 과학기술위성2호의 자세제어 및 모멘텀 덤핑용 과학기술실험용 탑재체로서 펄스형 플라즈마 추력기가 실린다. 본 논문에서는 관측기기 및 과학기술실험용 탑재체 등의 운용모드를 고려한 과학기술위성2호의 운용모드에 따른 전력의 수요예측에 대하여 연구하였고, 임무를 수행하는 동안 안정적인 전력을 공급하기위한 필요전력에 대하여 분석하였다.

STSAT-2 will be launched on December 2007 by the first Korean launch vehicle KSLV-1, and its one of the main instruments is DREAM (Dual Channel Radio Frequency and Environment Atmosphere Monitoring) which detects a signal for atmosphere from the Earth by using micro-wave signal. The STSAT-2 has many units for technology demonstration such as FDSS (Fine Digital Sun Sensor) and DHST (Dual Head Star Tracker) including PPT (Pulsed Plasma Thruster) for attitude control and momentum dumping in the space. In this paper, the power budget analysis for STSAT-2 will be studied and provided for supporting the whole mission life time during the mission of its spacecraft.

Demand estimates in electric power systems have traditionally consisted of time-series analyses over long time periods. The resulting database consisted of huge amounts of data that were then analyzed to create the various coefficients used to forecast power demand. In this research, we take advantage of universally used analysis techniques analysis, but we also use easily available data-mining techniques to analyze patterns of days and special days(holidays, etc.). We then present a new method for estimating and forecasting power flow using decision tree analysis. And because analyzing the relationship between the estimate and power system ceiling Trices currently set by the Korea Power Exchange. We included power system ceiling prices in our estimate coefficients and estimate method.