• Journal of the Korean Operations Research and Management Science Society
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• v.8 no.2
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• pp.45-56
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• 1983

This paper presents a generalized fuel choice model in which restrictive constraints on cross-price coefficients as Baughman-Joskow-FEA Logit Model need not be imposed, but all demand elasticities are uniquely determined. The model is applied to estimating aggregate energy demand and fuel choices for the residential and commercial sector. The structural equations are estimated by a generalized least squares procedure using national-level EPB, KDI, BK, KRIS, MOER data for 1965 and 1980, and other related reports. The econometric results support the argument that “third-price” and “fourth-price” coefficients should not be constrained in estimating relative market share models. Furthermore, by using this fuel choice model, it has forecasted energy demands by fuel sources in, the residential and commercial sector until 1991. The results are turned out good estimates to compare with existing demands forecasted from other institutes.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.289-291
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• 2000

In a competitive electricity power market, the price of electricity changes instantly, that of conventional market is predetermined and hardly changes. In such a new environment, customers' behaviors change instantly according to the changing electricity prices. If we develop a electricity load model that well describes the behavior of electricity consumers, we can utilize that model in forecasting the amount of future load, solving the load flow problem and finding the weak point of the system. In this paper new electricity model that considers the price of electricity and power factor of the load is presented. While conventional load model, which is demand function of electricity, uses the price of real and reactive power as the independent variable of the demand function. this new load model uses price of real power and penalty factor according to the power factor for the calculation of amount of electricity demand.

• Journal of the Korea Safety Management & Science
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• v.8 no.5
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• pp.139-150
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• 2006

The aim of this study is to establish an efficient distribution planning for a capacitated multi-stage supply chain. We assume that the demand information during planning horizon is given a deterministic form using a certain forecasting method. Under such a condition, we present a cost effective heuristic method for minimizing chain-wide supply chain inventory cost that is the sum of holding and backorder costs by using look-ahead technique. We cope with the capacity restriction constraints through look-ahead technique that considers not only the current demand information but also future demand information. To evaluate performance of the proposed heuristic method, we compared it with the extant research that utilizes echelon stock concept, under various supply chain settings.

• Electronics and Telecommunications Trends
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• v.34 no.3
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• pp.106-113
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• 2019

Mobile traffic is one of the most important indexes of the growth of the mobile communications market, and it has a close relationship with subscribers' service usage patterns, frequency demand and supply, network management, and information communication policy. The purpose of this paper is to understand mobile data usage in Korea and to suggest the optimal steps for establishing the frequency supply and demand system by researching the traffic trends that reflect the characteristics of radio resources in the mobile communications field. To achieve this goal, attempts were made to increase the possibility of policy use by analyzing and forecasting mobile traffic trends, and to improve the accuracy of the research through the verification of the existing prediction results. The paper ends with a discussion of the necessity of a frequency management system based on data science.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.439-441
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• 2022

This study compares the monitoring application for monitoring data predicted by the demand prediction algorithm and the web page of the construction site safety management system used by the power demand management application 'Hajumon' and U&E Communications. This study is two representative examples above, and it is possible to identify an appropriate application or web by comparing the difference between the web and the application's UI, advantages and disadvantages, and data supplementation.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.465-475
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• 2011

In this paper, we have considered the problems of the estimation of the gross areas of a factory site relating to the areas of industrial complex lands based on a statistical forecasting model and the results of a sampling survey. In respect to the data of a gross areas of a factory site, we have only the sizes from 1981-2003. In 2009, the Korea Industrial Complex Corp. conducted a sampling survey to estimate its bulk size, and investigate the demands of its sizes for the next five years. In this study, we have adopted the sampling survey results, and have created a statistical growth model for the gross areas of a factory site to improve the prediction for the areas of a factory site. The three-different parts of data: the results of areas of a factory site by Korea National Statistical Office, imputation results by the statistical forecasting model, and sampling survey results have used as the basis for analysis. The combination of the three-different parts of data has created a new forecasting value of the areas of a factory site through the spline smoothing method.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.1-6
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• 2018

In smart grid an accurate load forecasting is crucial in planning resources, which aids in improving its operation efficiency and reducing the dynamic uncertainties of energy systems. Research in this area has included the use of shallow neural networks and other machine learning techniques to solve this problem. Recent researches in the field of computer vision and speech recognition, have shown great promise for Deep Neural Networks (DNN). To improve the performance of daily electric peak load forecasting the paper presents a new deep neural network model which has the architecture of two multi-layer neural networks being serially connected. The proposed network model is progressively pre-learned layer by layer ahead of learning the whole network. For both one day and two day ahead peak load forecasting the proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange (KPX).

• Journal of the Society of Naval Architects of Korea
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• v.56 no.6
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• pp.523-531
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• 2019

The low birthrate and the need for national defense reform in Korea drive the Navy to develop efficient human resource planning such as a manpower forecasting model. However, to our knowledge, there is no study exploring the manpower forecasting model for naval ships in Korea. The purpose of this paper is to develop a model for forecasting manpower demand in naval ships. Data for analyses were drawn from 19 ships in the Korean Navy. Results indicate that mission type is significantly related to the number of manpower. Specifically, battleships need the more manpower than the battle support ships. The results also showed that the weight of hull structure-engine and the weight of the weapons system significantly increased the number of manpower. However, the weight of the combat system was not significant. In addition, whereas the automation level of hull structure-engine and the automation level of weapon system was found to be negatively related to the number of manpower, the automation level of combat system was positively related to it. The model developed here contributes to an advanced human resource planning of the Korean Navy. Implications, limitations, and directions for future research are discussed.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.57-72
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• 2020

The price of iron ore has continued to fluctuate with high demand and supply from many countries and companies. In this business environment, forecasting the price of iron ore has become important. This study developed the machine learning model forecasting the price of iron ore a one month after the trading events. The forecasting model used distributed lag model and deep learning models such as MLP (Multi-layer perceptron), RNN (Recurrent neural network) and LSTM (Long short-term memory). According to the results of comparing individual models through metrics, LSTM showed the lowest predictive error. Also, as a result of comparing the models using the ensemble technique, the distributed lag and LSTM ensemble model showed the lowest prediction.

• Journal of the Korean Wood Science and Technology
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• v.15 no.4
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• pp.32-44
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• 1987

This study was carried out to analyze and furecast the domestic demand for plywood in Korea by regression models with time-series data for 16 years(1970-85). The results obtained were summarized as follows. 1. To analyze domestic demand for plywood, GNP, PWI and CWI were used as independant variables. The domestic demand equation was computed as follows: $^{in}DDP$=0.65186+1.29412 $^{in}GNP$-0.28385 $^{in}PWI$-1.05011 $^{in}CWI$ Where DDP : Domestic demand for plywood(1000 S/F) GNP: Gross national product (Billion won) PWI : Real wholesale price index of plywood CWI: Real wholesale price index of construction materials. 2. Among independant variables reflecting on the production activity of plywood industry, GNP was the most decisive in forecasting the domestic demand for plywood. 3. The significance can be recognized highly because the decision coefficient of the forecasting model which is obtained by using time series data is 0.9. 4. According to the estimated regression coefficients for GNP, PWI and CWI, GNP shows positive relation while PWI and CWI show negative relation. 5. An annual average increase rate of demand for plywood was 9.4 percent during expect period. Therefore, it was decreased slightly than that of 10.2 percent during sample period.