• 상하수도학회지
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• 제19권4호
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• pp.395-403
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• 2005

It is very important to forecast water supply for reasonal operation and management of water utilities. In this paper, water supply forecasting models using artificial intelligence are developed. Artificial intelligence models shows better results by using Temperature(t), water supply discharge (t-1) and water supply discharge (t-2), which are expressed by neural network(LMNNWS; Levenberg-Marquardt Neural Network for Water Supply, MDNNWS; MoDular Neural Network for Water Supply) and neuro fuzzy(ANASWS; Adaptive Neuro-Fuzzy Inference Systems for Water Supply). ANFISWS model which is applied for water supply forecasting shows stable application to the variable water supply data. As results, MDNNWS model shows the highest overall accuracy among proposed water supply forecasting models and the lowest estimation error with the order of ANFISWS, LMNNWS model.

• 한국시스템다이내믹스연구
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• 제10권1호
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• pp.61-76
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• 2009

We try to use system dynamics to forecast the demand/supply and price, also transportation fare for iron ore. Iron ore is very important mineral resource for industrial production. The structure for this system dynamics shows non-linear pattern and we anticipated the system dynamic method will catch this non-linear reality better than the regression analysis. Our model is calibrated and tested for the past 6 year monthly data (2003-2008) and used for next 6 year monthly data(2008-2013) forecasting. The test results show that our system dynamics approach fits the real data with higher accuracy than the regression one. And we have run the simulations for scenarios made by possible future changes in demand or supply and fare related variables. This simulations imply some meaningful price and fare change patterns.

• 한국산학기술학회논문지
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• 제16권12호
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• pp.8576-8584
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• 2015

전력수요의 예측은 안정적인 전력공급을 위한 수급계획수립을 위해서 그리고 전력계통의 최적운영계획수립을 위해서도 필요하다. 특히 안정적인 전력수급확보를 위해서는 중장기 전력수요예측이 중요하고 공급안정성 강화를 위해서는 지역별 전력수요예측이 중요하다. 지역별 전력수요예측은 지역에 소요되는 부하를 충족시킬 수 있도록 송전선로 및 변전소 등의 계통망의 최적상태 구성 및 유지를 위한 필수적인 과정으로 알려져 있다. 따라서 본 논문은 12개월(중장기)동안 대한민국 시도별 16개 지역의 전력사용량을 SARIMA 모형을 이용하여 예측하는 방법을 제안한다.

• 상하수도학회지
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• 제25권4호
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• pp.581-589
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• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

• 한국시스템다이내믹스연구
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• 제9권1호
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• pp.125-141
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• 2008

It is very difficult to predict future demand/supply, price for resources with acceptable accuracy using regression analysis. We try to use system dynamics to forecast the demand/supply and price for nickel. Nickel is very expensive mineral resource used for stainless production or other industrial production like battery, alloy making. Recent nickel price trend showed non-linear pattern and we anticipated the system dynamic method will catch this non-linear pattern better than the regression analysis. Our model has been calibrated for the past 6 year quarterly data (2002-2007) and tested for next 5 year quarterly data(2008-2012). The results were acceptable and showed higher accuracy than the results obtained from the regression analysis. And we ran the simulations for scenarios made by possible future changes in demand or supply related variables. This simulations implied some meaningful price change patterns.

• 제어로봇시스템학회논문지
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• 제15권10호
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• pp.1056-1061
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• 2009

This paper proposes a short-term water demand forecasting algorithm based on kalman filtering with data mining for sustainable water supply and effective energy saving. The proposed algorithm utilizes a mining method of water supply data and a decision tree method with special days like Chuseok. And the parameters of MLAR (Multi Linear Auto Regression) model are estimated by Kalman filtering algorithm. Thus, we can achieve the practicality of the proposed forecasting algorithm through the good results applied to actual operation data.

• 산업공학
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• 제18권3호
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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.

• 농업과학연구
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• 제48권4호
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• pp.797-806
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• 2021

As the number of scaled-up ranches increased and agile responses to market changes became possible, decision-making by Hanwoo cattle farms also began to affect short-term shipments. Considering the changing environment of the Hanwoo supply market and the response speed of producers, it is necessary quickly to grasp the forecast ahead of time and to respond accordingly in an effort to stabilize supply and demand in the Hanwoo market. In this study, short-term forecasting model centered on the supply of Hanwoo was established. The analysis conducted here indicates that the slaughter of Hanwoo males increases by 0.248 as the number of beef cattle raised over 29 months of age in the previous month increases by one, and 0.764 Hanwoo females were slaughtered under average conditions for every Hanwoo male slaughtered. With regard to time, the slaughtering of Hanwoo was higher in January and August, which are months known for holiday food preparation activities for the New Year and Chuseok in Korea, respectively. Simulations indicated that errors were within 10% in all simulations performed through the Hanwoo supply model. Accordingly, it is considered that the estimation results from the supply model devised in this study are reliable and that the model has good structural stability.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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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.

• 한국조명전기설비학회지:조명전기설비
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• 제6권1호
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• pp.29-39
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• 1992

In this paper, the basic idea of optimum electric system design by means of the forecasting of maximum demand is presented, and the load characteristics and practical operating conditions are based on the technical data. After reconstruction of th model plant by use of above method, power supply reliability, future extention, initial cost, and running cost saving effects are analyzed. As a result, it is verified that the systems wherein the power is supply to each load frm main transformer whose capacity is calculated by forecasting are economic rather than the systems wherein the power is supply to each electric feeders from each corresponding transformer.