• 산업경영시스템학회지
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• 제38권1호
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• pp.110-117
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• 2015

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.

• 상하수도학회지
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• 제20권2호
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• pp.187-196
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• 2006

Forecasting the long-term water demand is important in the plan of water supply system because the location and capacity of water facilities are decided according to it. To forecast the long-term water demand, the existing method based on lpcd and population has been usually used. But, these days the trend among the variation of water demand has been disappeared, so expressing other variation of it is needed to forecast correct water demand. To accomplish it, we introduced the System Dynamics method to consider total connections of water demand factor. Firstly, the factors connected with water demand were divided into three sectors(water demand, industry, and population sectors), and the connections of factors were set with multiple regression model. And it was compared to existing method. The results are as followings. The correlation efficients are 0.330 in existing model and 0.960 in SD model and MAE are 3.96% in existing model and 1.68% in SD model. So, it is proved that SD model is superior to the existing model. To forecast the long-term water demand, scenarios were made with variations of employment condition, economic condition and consumer price indexes and forecasted water demands in 2012. After all scenarios were performed, the results showed that it was not needed to increase the water supply ability in Seoul.

• 대한전기학회논문지:전력기술부문A
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• 제53권11호
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• pp.604-609
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• 2004

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.

• Journal of Information Technology Applications and Management
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• 제24권2호
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• pp.81-96
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• 2017

Forecasting long-term mobile service demand is inevitable to establish an effective frequency management policy despite the lack of reliability of forecast results. The statistical forecasting method has limitations in analyzing how the forecasting result changes when the scenario for various drivers such as consumer usage pattern or market structure for mobile communication service is changed. In this study, we propose a dynamic model of the mobile communication service market using system dynamics technique and forecast the future demand for long-term mobile communication subscriber based on the dynamic model, and also experiment on the change pattern of subscriber demand under various scenarios.

• 한국경영과학회지
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• 제38권1호
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• pp.45-59
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• 2013

A long-term forecasting method for a new product in early stage of diffusion is proposed. The method includes a constrained non-linear least square estimation with the logistic diffusion model. The constraints would be critical market informations such as market potential, peak point, and take-off. Findings on 20 cases having almost full life cycle are that (i) combining any market information improves the forecasting accuracy, (ii) market potential is the most stable information, and (iii) peak point and take-off information have negative effect in case of overestimation.

• 한국수자원학회논문집
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• 제42권10호
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• pp.809-824
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• 2009

한정된 기간의 짧은 유출량 기록을 갖는 댐 유역에서의 수자원 시스템 거동예측은 수문학적 지속성여부에 대한 판단이 선행 되어야 하며 가용한 시계열자료에 대한 추계학적 분석을 통하여 실시하여야 한다. 본 연구에서는 계절형 ARIMA모형을 통하여 안동댐 유역의 강우량, 증발량 및 유출량 시계열자료로 월별 수문시스템 거동을 예측하였으며, 예측된 결과를 토대로 TANK모형과 ARIMA+TANK결합모형에 의한 장기유출모의를 실시하였다. 분석결과 관측자료의 특성을 비교적 잘 반영 하였으며, 댐 유입량 예측을 위한 추계학적 결합모형의 적용가능성을 검토하였다. 이는 상대적으로 유출량자료의 보유년한이 짧은 대상유역의 시계열 수문인자 예측을 통한 유출모의의 적용으로 수자원의 중 장기 전략수립에 도움이 되리라 사료된다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1447-1449
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• 1999

The KEPCO is developing the load forecasting system using land-use simulation method and distribution planning system. A distribution planning needs the data of present loads, forecasted loads and substations. distribution lines information. By the distribution planning system, the distribution line designer determines the substations and feeder lines plan. This paper presents the method of formulation process for the long term load forecasting and optimal distribution planning, and describes the case study of long term distribution planning of Suwon-city according to the newly applied method.

• 산업경영시스템학회지
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• 제41권1호
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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.

• 한국수자원학회논문집
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• 제44권10호
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• pp.843-851
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• 2011

수문학 분야에서 중장기 유출량 예측은 입력변수의 불확실성 등으로 인하여 확률론적 방법을 사용하는 것이 바람직한 것으로 알려져 왔다. 본 연구에서는 금강유역을 대상으로 구성된 바 있는 RRFS-ESP 시스템에 PDF-ratio 방법을 기반으로한 사전처리기능을 장착하여 보다 효율적인 중장기 예측시스템으로의 확장을 시도하여 보았다. 이를 위하여 기상청에서 제공하는 확률기상정보를 이용하여 가중치를 산정하고 이를 기반으로 시나리오별 예측확률을 갱신하였다. 예측결과에 대하여 각 기법별 예측점수를 산정하여 본 결과 우선 ESP 기법에 의한 예측점수의 평균이 초보예측 점수를 상회하여 본 연구에서 구성한 RRFS-ESP 시스템의 적용성을 확인할 수 있었다. 또한 확률기상전망을 이용하여 갱신한 유입량 시나리오의 예측점수가 ESP 기법에 의한 예측점수를 상회하고 있음을 확인할 수 있어 ESP 기법에 의한 예측결과를 확률기상전망을 이용하여 갱신할 경우 예측 정확도를 보다 개선시킬 수 있음을 확인할 수 있었다.

• Communications for Statistical Applications and Methods
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• 제28권5호
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• pp.521-536
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• 2021

As life expectancies increase continuously over the world, the accuracy of forecasting mortality is more and more important to maintain social systems in the aging era. Currently, the most popular model used is the Lee-Carter model but various studies have been conducted to improve this model with one of them being 6-parametric factor model (6-PFM) which is introduced in this paper. To this new model, long short-term memory (LSTM) and regularized LSTM are applied in addition to vector autoregression (VAR), which is a traditional time-series method. Forecasting accuracies of several models, including the LC model, 4-PFM, 5-PFM, and 3 6-PFM's, are compared by using the U.S. and Korea life-tables. The results show that 6-PFM forecasts better than the other models (LC model, 4-PFM, and 5-PFM). Among the three 6-PFMs studied, regularized LSTM performs better than the other two methods for most of the tests.