Earticle

태국은 한국관광시장에서 가장 인기있는 해외방문지국가 중의 하나였으나 성공적인 마케팅성과만이 있었던 것은 아니다. SARS, 조류독감, 정치적 불안정, 쯔나미 등 시장에 많은 장애물도 발생하였다. 이러한 부정적 사건의 발생으로 시장수요는 감소하였고 시장수요를 유지하기 위한 취약마케팅전략이 수립되고 실행될 필요가 제기된다. 적절하고 효율적인 관광마켕 정책과 전략을 수립하기 위해 수요예측은 향후 한국관광시자에 마케팅활동의 투입수질의 양을 결정하는데 필수 불가결한 요소이다.이러한 점에서 본 연구는 시계열모델의 하나인 ARIMA 개입모형을 사용하여 태국으로의 한국인 관광객 수요예측을 하여 국가관광기구로서의 실질적인 마케팅 정책과 전략을 수립하는 시사점을 제시하였다.

Thailand have long been one of the most popular overseas destination for Korean travelers. But even though Thailand have showed the successful market achievement, there were also many obstacles to promote Thailand's tourism such as SARS, the bird flu, the political unrest and Tsunami.Due to the outbreak of the negative incidents, market demand was dropped and the vulnerable marketing strategy to maintain the market demand should be adopted and implemented to Korean market. To establish the proper and effective tourism marketing policy or strategies, the demand forecast is prerequisite especially to help the decision making of the quantity of input level for the marketing activity to Korean tourism market in the future.In this respect, the purpose of this research is to forecast the travel demand of Korean travelers to Thailand using an ARIMA　intervention model, one of time series model. The results of this research may help TAT as NTO to establish the practical marketing policy and strategy to Korean tourism market.

As the equipment of the military has become more advanced and expensive, the cost of securing spare parts is also constantly increasing along with the increase in equipment assets. In particular, forecasting demand for spare parts one of the important management tasks in the military, and the accuracy of these predictions is directly related to military operations and cost management. However, because the demand for spare parts is intermittent and irregular, it is often difficult to make accurate predictions using traditional statistical methods or a single statistical or machine learning model. In this paper, we propose a model that can increase the accuracy of demand forecasting for irregular patterns of spare parts demanding by using a combination of statistical and machine learning algorithm, and through experiments on Cheonma spare parts demanding data.

Purpose: Compared to the rapid growth rate of the domestic automotive LED industry so far, the predictive analysis method for demand forecasting or market outlook was insufficient. Accordingly, product characteristics are analyzed through the life trend of LEDs for automotive exterior lamps and the relative strengths of p and q using the Bass model. Also, future demands are predicted. Methods: We used sales data of a leading company in domestic market of automotive LEDs. Considering the autocorrelation error term of this data, parameters m, p, and q were estimated through the modified estimation method of OLS and the NLS(Nonlinear Least Squares) method, and the optimal method was selected by comparing prediction error performance such as RMSE. Future annual demands and cumulative demands were predicted through the growth curve obtained from Bass-NLS model. In addition, various nonlinear growth curve models were applied to the data to compare the Bass-NLS model with potential market demand, and an optimal model was derived. Results: From the analysis, the parameter estimation results by Bass-NLS obtained m=1338.13, p=0.0026, q=0.3003. If the current trend continues, domestic automotive LED market is predicted to reach its maximum peak in 2021 and the maximum demand is $102.23M. Potential market demand was $1338.13M. In the nonlinear growth curve model analysis, the Gompertz model was selected as the optimal model, and the potential market size was $2864.018M. Conclusion: It is expected that the Bass-NLS method will be applied to LED sales data for automotive to find out the characteristics of the relative strength of q/p of products and to be used to predict current demand and future cumulative demand.

The accuracy of load forecast is very important from the viewpoint of economical power system operation. In general, the weekdays' load demand pattern has the continuous time series characteristics. Therefore, the conventional methods expose stable performance for weekdays. In case of special days or weekends, the load demand pattern has the discontinuous time series characteristics, so forecasting error is relatively high. Especially, weekdays near the thanksgiving day and lunar new year's day have the mixed load profile characteristics of both weekdays and weekends. Therefore, it is difficult to forecast these days by using the existing algorithms. In this study, a new load forecasting method is proposed in order to enhance the accuracy of the forecast result considering the characteristics of weekdays and weekends. The proposed method was tested with these days during last decades, which shows that the suggested method considerably improves the accuracy of the load forecast results.

This paper analyzed the characteristics of the demand of electric power in Jeju by year, day. For this analysis, this research used the correlation between the changes in the temperature and the demand of electric power in summer, and cleaned the data of the characteristics of the temperatures, using the coefficient of correlation as the standard. And it proposed the algorithm of forecasting the short-term electric power demand in Jeju, Therefore, in the case of summer, the data by each cleaned temperature section were used. Based on the data, this paper forecasted the short-term electric power demand in the exponential smoothing method. Through the forecast of the electric power demand, this paper verified the excellence of the proposed technique by comparing with the monthly report of Jeju power system operation result made by Korea Power Exchange-Jeju.

In order to improve the operation of energy systems, it is necessary for the urban communities to have reliable optimization routines, both computerized and manual, implemented in their organizations. However, before a production plan for the energy system units can be constructed, a prediction of the energy systems first needs to be determined. So, several methodologies have been proposed for energy demand prediction, but due to uncertainties in urban community, many of them will fail in practice. The main topic of this paper has been the development of a method for energy demand prediction at urban community. Energy demand prediction is important input parameters to plan for the energy planing. This paper presents a energy demand prediction method which estimates heat and electricity for various building categories. The method 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. Also, the ANN can extract the relationships among these variables by means of learning with training data. In this paper, the ANN have been applied in oder to correlate weather conditions, calendar data, schedules, etc. Space heating, cooling, hot water and HVAC electricity can be predicted using this method. This method can produce 10% of errors hourly load profile from individual building to urban community.

본 논문은 전력계통에서의 전력수요예측을 하기 위한 네가지중 하나의 방법으로 특수일에 적용기법인 수요예측 담당자의 Know-How를 기반으로 예측하는 전문가법에 대하여 과거 15년간의 수요실적과 기상실적을 근거로 년중 모든 특수일에 대해서 수요패턴 분석을 실시하고 패턴의 변화되는 과정을 살펴보았다. 과거 전력수요 실적의 근거로 산출된 시간대별 상대계수의 수요패턴을 가지고 모의 운영한 결과 설 연휴 수요예측 오차율은 과거 5일간(2002, 2003년도)의 평균값 $3.23{[\%]}$에서 2005년도 설 연휴 실적대비 $1.78{[\%]}$로 상당한 개선효과가 있었다. 본 논문의 내용을 바탕으로 전력수요 예측 편람을 작성하는 것도 입문교재로서 수요예측 전문가 양성에 큰 도움을 줄 수 있을 것으로 기대된다.

This paper performs analysis the load patterns for the all the special days and studies the change of the load patterns for the last 15 years using Expert system based on the load record and the weather condition record. The Expert system is one of the four major load forecasting methods of the power system And it is used for forecasting. loads of the special days based on the Know-how of the load forecasting Experts. After the author simulates the load forecasting using hourly relative factors of the load patterns based on the past load records, there is considerable improved effect. The average errors of past 5 days load forecasting of lunar New Year's Day (year 2002 and 2003) is $3.23{[\%]}$. Using the new method the author forecast loads of the lunar new year's days (the year 2005) and it shows only $1.78{[\%]}$ error. A field manual for the load forecast can be made using proposed method. The authors expect this article could give a guidance to those who wish to be load forecast expert.

As the Kyoto Protocol will come into effect starting February 2005, 55 member countries of the Conference of Parties of the Framework Convention on Climate Change (FCCC) will be under obligation to reduce the emissions of Carbon Dioxide $(CO_2)$ by 5.2 Percent from the 1990 levels before the year 2012. Hence the development of technology to prepare for this has been accelerated in Korea. The effect of technology varies with market size of technology, and it is necessary to control technology development period, according to the size and trend of technology market. Moreover it is essential that market analysis be finished before technology development, because market on the $(CO_2)$ Reduction and Sequestration Technology expands internationally. For that reason, it is needed to analyze domestic market and to consider technology development strategy according to analysis results. In this paper, we analyzed the domestic industry and forecasted the market size, both related to the Reduction and Sequestration Technology on $(CO_2)$ emission, which is the major component of global Green House Gas(GHG).

In this paper, we have developed a technology-service relationship model which describes the diffusion process of a group of services and relevant technologies, and have applied the developed model to the prediction of the number of subscribers to the next generation mobile service. The technology-service relationship model developed in this paper incorporates the developing process of relevant technologies, a supply-side factor, into the diffusion process of specific services, while many diffusion models and multi-generation diffusion models in previous researches are mainly reflect the demand-side factors. So, the proposed model could effectively applied to the telecommunication services where the developing of the relevant technologies are very essential to the service Penetration. In our application, the Proposed model provides a competitive substitution between the next generation mobile service and the traditional mobile service.

This study was performed to estimate mid and long term demands of a tractor, a rice transplanter, a combine and a grain dryer by using logistic curve function and Markov chain model. Field survey was done to decide some parameters far logistic curve function and Markov chain model. Ceiling values of tractor and combine fer logistic curve function analysis were 209,280 and 85,607 respectively. Based on logistic curve function analysis, total number of tractors increased slightly during the period analysed. New demand for combine was found to be zero. Markov chain analysis was carried out with 2 scenarios. With the scenario 1(rice price $10\%$ down and current supporting policy by government), new demand for tractor was decreased gradually up to 700 unit in the year 2012. For combine, new demand was zero. Regardless of scenarios, the replacement demand was increased slightly after 2003. After then, the replacement demand is decreased after the certain time. Two analysis of logistic owe function and Markov chain model showed the similar trend in increase and decrease for total number of tractors and combines. However, the difference in numbers of tractors and combines between the results from 2 analysis got bigger as the time passed.

Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. The percentage errors of 24 hourly load forecasting for holidays is relatively large. In this paper, we propose the maximum and minimum load forecasting method for holidays using a fuzz linear regression algorithm. 24 hourly loads are forecasted from the maximum and minimum loads and the 24 hourly normalized values. The proposed algorithm is tested for 24 hourly load forecasting in 1996. The test results show the proposed algorithm improves the accuracy of the load forecasting.

In this paper, we have developed a technology-service relationship model which describes the diffusion process of a group of services and relevant technologies, and have applied the developed model to the prediction of the number of subscribers to the next generation mobile service. The technology-service relationship model developed in this paper incorporates the developing process of relevant technologies, a supply-side factor, into the diffusion process of specific services, while many diffusion models and multi-generation diffusion models in previous researches are mainly reflect the demand-side factors. So, the proposed model could effectively applied to the telecommunication services where the developing of the relevant technologies are very essential to the service penetration. In our application, the proposed model provides a competitive substitution between the next generation mobile service and the traditional mobile service.

본 논문에서는 광역상수도 시스템의 취·송수 설비의 최적운영계획에 필수적으로 요구되는 시간 단위 용수 수요량 예측을 위하여 칼만 필터에 의한 수요 예측 모델 구축 및 배수패턴 해석 기법을 제안하고, 기존 시스템의 실 데이터를 이용하여 시뮬레이션 수행 결과 제안된 기법의 유용성이 검증되었다. 광역상수도 시스템에서 취·송수 설비의 최적운영계획 수립을 위해서는 예측 시간 범위를 최소 하루 단위 이상으로 유지해야 한다. 따라서, 제안된 기법에서는 기존의 시간별 실적데이터의 시계열에 의한 예측을 이용하는 것이 아니라 모델로부터 예측된 일 수요량에 배수패턴을 곱하여 24시간의 시간별 용수 수요량을 예측한다. 일 수요량 예측을 위한 칼만 필터 모델은 입력변수의 통계적 분석에 의해 모델 구조 최적화가 효과적으로 구현되고 배수패턴은 데이터 Granulization에 의해 얻어진다.

Kalman Filter model of demand for residental water and consumption pattern wore tested for their ability to explain the hourly residental demand for water in metro-politan distribution system. The daily residental demand can be obtained from Kalman Filter model which is optimized by statistical analysis of input variables. The hourly residental demand for water is calculated from the daily residental demand and consumption pattern. The consumption pattern which has 24 time rates is characterized by data granulization in accordance with season kind, weather and holiday. The proposed approach is applied to water distribution system of metropolitan areas in Korea and its effectiveness is checked.

In this study, we suggest a multi-generation Lotka-Volterra model, which is a competition model using game theory and complex system theory. The suggested model shows many improvements to weakness of a well known Bass model to forecast new technology in competitive markets. We show that the Lotka-Volterra model has strong power to forecast mobile communication services when it is used for competition of 1st generation mobile phone service and 2nd generation phone service in Korea. We finally use the model to forecast IMT-2000 service, the 3rd generation mobile communication service.

In case of power demand forecasting the most important problem is to deal with the load of special-days, Accordingly, this paper presents a method that forecasting special-days load with regression models and neural networks. Special-days load in summer season was forecasted by the multiple regression models using weekday change ratio Neural networks models uses pattern conversion ratio, and orthogonal polynomial models was directly forecasted using past special-days load data. forecasting result obtains % forecast error of about $1{\sim}2[%]$. Therefore, it is possible to forecast long and short special-days load.

본 연구는 SARIMA모형을 이용하여 김해국제공항의 여객수요를 예측하고, 이를 통해 항공산업에 정책적, 실무적 시사점을 제공하고자 하였다. 본 연구목적을 수행하기 위해 국가통계포털 통계청에서 김해국제공항 월별 여객 데이터를 수집하였다. 계절성이 뚜렷한 시계열 데이터에 적합한 모형인 SARIMA모형을 이용하여 ARIMA(1,1,2)(0,1,0)[12]를 가장 적합한 모형으로 선정하였다. 모형의 정확성 검증을 위해 MAPE를 확인한 결과, 0%≤ MAPE ≺10% 사이의 값으로 나타나 김해국제공항 여객수요를 예측하기 위한 ARIMA(1,1,2)(0,1,0)[12] 모형은 매우 신뢰할 수 있다. 분석결과 예측 기간인 2030년 12월까지 시간이 흐름에 따라 김해국제공항 여객수요는 증가하는 추세임으로 나타났다. 2023년 전년 대비 13.2% 증가를 시작으로 2030년까지 지속적으로 증가할 것으로 보인다. 코로나-19가 종식되고, 특별한 이벤트가 없다는 가정하에 회복 추이만 놓고 살펴본다면 김해공항 여객수요는 2030년까지 꾸준히 증가할 것이다. 2030 월드 엑스포가 부산에서 개최 가능성이 거론되고 있는 현시점에서 본 연구의 결과는 가덕도신공항 건설 조기 착공 및 김해공항 확장에 대한 필요성을 제기할 수 있을 것으로 사료된다. 본 연구 결과를 바탕으로 향후 김해국제공항의 시설 보완 및 확충, 가덕도신공항 조기 건설, 국내 항공산업의 영업 및 사업 계획을 하는데 기초적인 자료가 되고자 한다. 또한 항공산업의 수요예측에 대한 필요성과 중요성을 인식할 수 있는 계기를 마련할 것으로 사료된다.

This study attempted to forecast the passenger demand of Gimhae International Airport using the SARIMA model and provide policy and practical implications for the aviation industry through this. In order to carry out the purpose of this study, monthly passenger data at Gimhae International Airport were collected by the National Statistical Portal Statistics Office. ARIMA (1,1,2) (0,1,0) [12] was selected as the most suitable model using the SARIMA model, which is suitable for time series data with clear seasonality. As a result of checking the MAPE to verify the accuracy of the model, the ARIMA (1,1,2) (0,1,0)[12] model for forecasting passenger demand at Gimhae International Airport is very reliable. As a result of the analysis, it can be seen that the demand for passengers at Gimhae International Airport is increasing over time until December 2030, the forecast period. Starting with a 13.2% increase from the previous year in 2023, it is expected to continue to increase until 2030. Assuming that COVID-19 is over and there are no special events, the demand for passengers at Gimhae International Airport will steadily increase by 2030. At a time when the possibility of the 2030 World Expo being held in Busan is being discussed, the results of this study are believed to raise the need for early construction of the new Gadeokdo Airport and expansion of Gimhae Airport. Based on the results of this study, we intend to serve as basic data for supplementing and expanding facilities at Gimhae International Airport, early construction of Gadeokdo New Airport, and business and business planning of the domestic aviation industry. It is also expected to provide an opportunity to recognize the necessity and importance of forecasting demand in the aviation industry.

이 연구는 날씨가 외식에 어떤 영향을 주는지에 대해 확인하기 위해 만들어졌다. 외식에 영향을 주는 매출요인은 주말 과 평균온도, 일교차는 양의 상관관계를 풍속, 강우는 음의 상관관계를 가졌다. 따라서 본 외식업체는 주말 평균기온의 상승에 따른 매출증대의 기회를 놓치지 말아야 하고 평일에 풍속, 일교차에 따른 매출감소에 대비해야 한다. 이를 위해 지속적으로 Bigdata를 구축하여 데이터의 정확성을 높일 뿐만 아니라 매장관련 이해관계자들에게 데이터기록, 이용에 대한 교육을 강화해야 한다.

This study was created to see how weather affects eating out. Sales factors influencing eating out were positively correlated with weekends, average temperatures, and daily differences, while wind speeds and rainfall had negative correlations. Therefore, this catering company should not miss the opportunity to increase sales due to the rise in average temperature on the weekend and prepare for the decrease in sales due to wind speed on weekdays. To this end, it is necessary to continuously build big data to increase data accuracy, as well as strengthen training on data recording and use for store-related stakeholders.

Korea is expected to become a super-aged society by 2050. Given an aging population and the increasing pressure for the early retirement, a sufficient social safety net for elderly population becomes important. The Korean government introduced public reverse mortgage program in 2007, which is a product for aging seniors and the elderly, The number of reverse mortgage subscribers has also steadily grown. The demand continues to grow, but the reverse mortgage over a long period of time is a highly uncertain and risky product in the position of guarantee or lending institution. Thus, suitable demand prediction of the reverse mortgage subscribers is necessary for stable and sustainable operation. This study uses a Bass diffusion model to forecast the long-term demand for reverse mortgage and provides insight into reverse mortgage by forecasting demand for stability and substantiality of the loan product. We represent the projections of new subscribers on the basis of the data obtained from Korea Housing Finance Corporation. Results show that potential market size of Korean reverse mortgage reaches approximately 760,000-1,160,000 households by 2020. We validate the results by comparing the estimate of the cumulative number of subscribers with that found in literature.

Load forecasting is very important for power system analysis and planning. This paper suggests yearly load forecasting of considering weekly normalization and seasonal load characteristics. Each weekly peak load is normalized and the average value is calculated. The new hourly peak load is seasonally collected. This method was used for yearly load forecasting. The results of the actual data and forecast data were calculated error rate by comparing.

본 연구는 최근에 활발히 연구가 진행 중인 항공수요 예측을 위하여 계절형 다변량 시계열 모형을 기반으로 하고 다른 모형과의 비교를 RMSE(Root Mean Square Error)를 기준으로 비교한 것이다. 여기서 싱가폴 국제항공유가, 수출액을 추가하여 예측성능을 좋게 하고자 한다.

Forecasting for air demand such as international passengers and freight has been one of the main interests for air industries. This research has mainly focus on the comparison of the performances of the multivariate time series models. In this paper, we used real data such as exchange rates, oil prices and export amounts to predict the future demand on international passenger and freight.