• 상하수도학회지
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• 제21권1호
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• pp.75-82
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• 2007

When one forecasts urban water demand in a long-term, multivariate model can give more benefits than per capita requirement model. However, the former has shortcomings in that statistically high explanatory power cannot be obtained well, and change in customer behavior cannot be considered. If the past water consumption effects the future water demand, dynamic model may describe real water consumption data better than static model, i.e. the existing multivariate model. On these grounds, this study built dynamic model using system dynamics. From a case study in Seoul and Busan city, dynamic model was expected to forecast water demand more descriptively and reliably.

• 품질경영학회지
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• 제36권4호
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• pp.93-101
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• 2008

The rare metals, used for semiconductors, PDP-LCS and other specialized metal areas necessarily, has been playing a key role for the Korean economic development. Rare metals are influenced by exogenous variables, such as production quantity, price and supplied areas. Nowadays the supply base of rare metals is threatened by the sudden increase in price. For the stable supply of rare metals, a rational demand outlook is needed. In this study, focusing on the domestic demand for chromium, the uncertainty and probability materializing from demand and price is analyzed, further, a demand forecast model, which takes into account various exogenous variables, is suggested, differing from the previously static model. Also, through the OOS(out-of-sampling) method, comparing to the preexistence ARIMA model, ARMAX model, multiple regression analysis model and ECM(Error Correction Mode) model, we will verify the superiority of suggested model in this study.

• 한국정보시스템학회지:정보시스템연구
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• 제31권3호
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• 에너지공학
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• 제28권4호
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• pp.82-93
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• 2019

본 연구에서는 기존 열수요 예측 시스템이 공휴일과 같은 특정 일자의 열수요 예측율이 저하되는 문제점을 개선하기 위해 새로운 모델을 제안한다. 제안된 모델은 사계절 혼합형 신경망 모델(Four Season Mixed Heat Demand Prediction Neural Network Model)로서 열수요 예측율 상승하였고, 특히 예측일 유형별(평일/주말/공휴일) 열수요 예측율이 크게 증가하였다. 제안된 모델은 다음과 같은 과정을 통해 선정되었다. 특정 계절에 예측일 유형별로 고른 오차를 갖는 모델을 선정하여 전체 예측 모델을 구성한다. 학습 시간의 단축과 과도학습을 방지하기 위해 구조적으로 단순화된 서로 다른 4개의 모델을 각각 학습한 후에 다양한 조합을 통해 최적의 예측 오차를 보여주는 모델을 선정하였다. 모델의 출력은 예측일의 24시간의 시간대별 열수요이며 총합은 일일 총열수요이다. 이 예측값을 통해 효율적인 열공급 계획을 수립 할 수 있으며, 목적에 따라 출력값을 선택하여 활용할 수 있다. 제안된 모델의 일일 열 총수요 예측의 경우, 전체 MAPE(Mean Absolute Percentage Error, 평균 절대 비율 오차)가 개별 모델의 5.3~6.1%에서 5.2%로 향상되었고, 공휴일 열수요예측은 4.9~7.9%에서 2.9%로 크게 개선되었다. 본 연구에서는 한국 지역난방공사에서 제공한 특정 아파트 단지의 34개월 분량의(2015년 1월~ 2017년10월) 시간단위 열수요 데이터를 활용하였다.

• KIEAE Journal
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• 제13권3호
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• pp.91-96
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• 2013

The purpose of this study was to analyze the annual energy demand including heating, cooling and lighting according to kind of windows with transparent thin-film a-Si Building Integrated Photovoltaic(a-Si BIPV) for office building. The analysis results of the annual energy demand indicated that the a-si BIPV window was reduced by 8.4% than the clear gazing window. The base model A was combinate with a-Si BIPV window area of 67% and clear window area of 33% among the total exterior area. The model B is to be applied with low-e clear glass instead of clear glass of the base model A. The model B was reduced to annual energy demand of 1% more than the model A. Therefore, By using a-si BIPV solar module, the cooling energy demand can be reduced by 53%(3.4MWh) and the heating energy demand can be increase by 58%(2.4MWh) than clear glazing window in office building. Also, Model C applied to the high efficient lighting device to the model B was reduced to annual energy demand of 14.4% more than the Model D applied to the high efficient lighting device to the model A. The Model E applied with daylight dimming control system to the Model C was reduced to annual energy demand of 5.9% more than Model C.

• 상하수도학회지
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• 제28권4호
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• pp.377-389
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• 2014

In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and this has led to various studies regarding energy saving and improvement of water supply reliability. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The concepts was demonstrated through application to observed from water plant (A) in the South Korea. Various statistics (e.g. the efficiency coefficient, the correlation coefficient, the root mean square error, and a maximum error rate) were evaluated to investigate model efficiency. The ensemble based model with an cross-validate prediction procedure showed better predictability for water demand forecasting at different temporal resolutions. In particular, the performance of the ensemble model on hourly water demand data showed promising results against other individual prediction schemes.

• 전기학회논문지
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• 제60권6호
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• pp.1103-1111
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• 2011

This paper is developed to Energy Balance Flow show the flow of total energy resource be used nationally. The Energy Balance Flow is applicable of demand management factor through the analysis of foreign energy model of supply and demand and energy statistic data in the country. This study is based on and developed to Energy system management model is able to appraisal efficient of energy cost cutting, CO2 emission reduction and Energy saving at the national level calculated effect reached amount of primary energy to change of energy flow followed application of demand side management factor is able to appraisal quantitatively at the total energy to model of demand and supply.

• 한국농촌생활과학회지
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• 제6권2호
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• pp.173-183
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• 1995

On the basis of intertemporal utility maximization theory and stock-adjustment hypothesis, a multivariate stock-adjustment credit demand model, which included on- and cross-adjustment effects of credit and cross-adjustment effects of assets was developed. With weighted four-year panel data from 1983 and 1986 Surveys of Consumer Finances, the theoretical model was tested using two-stage estimation method for tobit model. The results supported the hypothesis that, in general, the household demand for a certain type of credit was related to the demand for other types of credit and asset components in the portfolio. The household demand for mortgage credit, installment credit and revolving credit card debt depended not only on the disequilibrium of itself but on the disequilibrium of the other types of credit and asset components in the portfolio. The household demand for non-installment credit was related not to the disequilibrium of itself and other types of credit but to the disequilibria of asset components in the portfolio.

• 자원ㆍ환경경제연구
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• 제11권1호
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• pp.81-97
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• 2002

Unlike the existing supply-centered water policy, demand management policy of water has become an increasingly important issue in Korea. This paper attempts to analyse the demand for domestic water in Seoul. We employed Engle-Granger's error correction model(ECM) to deduced the price and income elasticities of the water demand. Particularly, we used accounted water amounts instead of supplied water amounts as representative variable of water demand. The result indicates that ECM set up is appropriate and short-run and long-run price elasticities derived by the model are -0.145 and -1.414. In contrast with other studies, we can conclude that the water demand for the water price is elastic. Besides, we can infer from this result that the water price policy with respect to a decrease of leakage ratio is more effective.

• 수산경영론집
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• 제44권1호
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• pp.59-70
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• 2013

The purpose of this paper is to integrate various models of price formation and let the data choose the most proper model. After the data choose the proper model, one can analyze the price formation process and demand structures for fishery resources under the restriction of Korean fisheries regulations. This study suggests the integrated model including quasi-linear price formation model, Translog price formation model, AIDS price formation model and Lewbel price formation model as level variables. It also suggests another integrated model including AIDS price formation model, Rotterdam price formation model, Latinen-Theil price formation model and Neves price formation model as difference variables. The empirical results show that the AIDS price formation model is the most preferred in both level and difference variables of fishery resources. The estimated parameters show that all sample species have (-) sign of price flexibilities, thus following the law of demand. The scale flexibilities of all species are estimated as (-) sign, thus being adapted to the theory. The contribution and results are summarized as follows. First, the integrated model of fishery market demand has been developed and the data can choose the proper model without arbitrary choice of the researcher. Second, the fishery market demand structure could be analyzed in a way different from the ordinary demand analysis, which is based upon price flexibility and scale flexibility. Third, the integrated model for fishery resources can be used easily when catching restrictions are imposed by policies.