• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.353-358
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• 2001

Since the financial crisis broke out, liquidity has become the critical issue in housing construction industry. In order to secure liquidity, it is prerequisite to precisely forecast cash flow. However, construction companies have failed to come up with a systematic process to manage and forecast cash flow. Until now, companies have solely relied on the prediction of profits and losses, which is carried out as they review business feasibility. To obtain more accurate cash flow forecast model, practical pattern of payments should be taken into account. In this theory, basic model that analyzes practical housing payment collection pattern resulting from prepayments and arrears is described. This model is to complement conventional cash flow forecast scheme in the phase of business feasibility review. Analysis result on final losses in cash that occur as a result of prepayment and arrears is considered in this model. Additionally, in the estimation of construction cost in the phase of business feasibility review, real construction prices instead of official prices are applied to enhance accuracy of cash outflow forecast. The proportion of payment made by a bill and changes in payment date caused by rescheduling of a bill are also factored in to estimate cash outflow. This model would contribute to achieving accurate cash flow forecast that better reflect real situation and to enhancing efficiency in capital management by giving a clear picture with regard to the demand and supply timing of capital.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.3
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• pp.593-601
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• 2017

With the rapid industrial development and national economic advance since 1970, the national income of Korea has sharply increased. As a result, issues regarding city expansion, urban concentration, increase in the number of registered motor vehicles, and increase in traffic have caused transportation issues such as traffic congestion and problems with parking. Especially, enforcement ordinances and rules have been established on installation and management of parking lots to solve problems with parking which are raised as social problems such as conflict with neighbors but the flexible calculation of legal parking space has the limitations because of the diversity and complex functionality of purposes of facilities. Accordingly, this study attempted to supplement such demerit of the parking space demand forecast method based on the legally required number of parking spaces and average unit requirement in the parking space supply. This study estimated the required number of parking spaces by analyzing existing literature, collecting field research data, and analyzing the factors that have an impact on the parking demand. Also, it compared the required number of parking spaces based on the average unit requirement as well as the required number of parking spaces by the forecast model based on the cumulative number of motor vehicles parked. The result was that the required number of parking space based on average unit requirement was less than the cumulative number of motor vehicles parked by 9.99%. Meanwhile, the required number of parking spaces by the forecast model was more than the cumulative number of motor vehicles parked by 4.37%. Therefore, it is believed that the parking space forecast model is more efficient than the others in estimating there quired parking space. The parking space forecast model of this study consider different environmental factors to enable practical parking demand forecast considering the local characteristics and thus supply the parking space in an efficient way.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1495-1504
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• 2013

Renewable energy integration and increased system complexities make system operator maintain supply and demand balance harder than before. To keep the grid frequency in a stable range, an appropriate spinning reserve margin should be procured with consideration of ever-changing system situation, such as demand, wind power output and generator failure. This paper propose a novel concept of dynamic reserve, which arrange different spinning reserve margin depending on time. To investigate the effectiveness of the proposed dynamic reserve, we developed a new short-term reliability criterion that estimates the probability of a spinning reserve shortage events, thus indicating grid frequency stability. Uncertainties of demand forecast error, wind generation forecast error and generator failure have been modeled in probabilistic terms, and the proposed spinning reserve has been applied to generation scheduling. This approach has been tested on the modified IEEE 118-bus system with a wind farm. The results show that the required spinning reserve margin changes depending on the system situation of demand, wind generation and generator failure. Moreover the proposed approach could be utilized even in case of system configuration change, such as wind generation extension.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1373-1383
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• 2016

The wind energy industry and wind power generation have increased; consequently, the stable supply of the wind power has become an important issue. It is important to accurately predict the wind power with short-term basis in order to make a reliable planning for the power supply and demand of wind power. In this paper, we first analyzed the speed, power and the directions of the wind. The neural network and the time series models (ARMA, ARMAX, ARMA-GARCH, Holt Winters) for wind power generation forecasting were compared based on mean absolute error (MAE). For one to three hour-ahead forecast, ARMA-GARCH model was outperformed, and the neural network method showed a better performance in the six hour-ahead forecast.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1488-1494
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• 2011

In this paper, we propose an algorithm using linear regression model that forecasts the demand of heated water in winter. To supply heated water to apartments, stores and office buildings, Korea District Heating Corp.(KDHC) operates boilers including electric power generators. In order to operate facilities generating heated water economically, it is essential to forecast daily demand of heated water with accuracy. Analysis of history data of Kangnam Branch of KDHC in 2006 and 2007 reveals that heated water supply on previous day as well as temperature are the most important factors to forecast the daily demand of heated water. When calculated by the proposed regression model, mean absolute percentage error for the demand of heated water in winter of the year 2006 through 2009 does not exceed 3.87%.

• Korean System Dynamics Review
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• v.3 no.1
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• pp.79-104
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• 2002

A system dynamics project is going on for forecasting automobile market in Korea. The project is made up of three stages, and the first stage has been wrapped up. As the first attempt, most efforts have been focused on the sound foundation rather than the exact forecast. The model consists of three sectors; the supply sector, the demand sector, and the population sector. The supply sector is a simple stock and flow diagrams representing the supply capacities of all automobile types. The major effort is made on the demand sector and the population sector. The demands are divided into three categories; replacement demands, new demands, and additional demands. The model applies “one car per person" concept, and assumes there will be no additional demands for a while. The replacement demands are calculated based on a simple stock and flow diagram. The new demands are calculated via Bass models; each bass model represents a diffusion for each age group. The population is divided into 101 age groups (age 0 to age 100). The model has been calibrated with past 10 year data (1990 - 1999), and tested for the next two years (2000-2001). The results ware acceptable, although a fine tuning is required. Now the second stage is going on, and most of efforts are made how to incorporate the economic and cultural factors.

• Journal of Society of Preventive Korean Medicine
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• v.13 no.1
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• pp.1-11
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• 2009

To strengthen the R&D capability and the competitiveness of the Korean oriental medicine industry, an adequate supply of qualified R&D personnel including medical doctors of Korean oriental medicine is an important precondition. This study analyze current and future R&D manpower requirements including medical doctors in the Korean oriental medicine research area. Our analyses can be utilized for developing the government R&D manpower planning including the adequate supply of medical doctors for the Korean oriental medicine research. For the study, we conducted and analyzed a delphi survey of the experts, the principal investigators, with expertise in Korean oriental medicine research areas. The results of this study can be summarized as follows; First, in 2007 the Korean oriental medicine R&D personnel is currently under-supplied as many as 302 people including 111 medical doctors of Korean oriental medicine. The rate of under-supplied is 28.2%. Second, in 2017 the forecast shows that the R&D personnel in this area will be more severely under-supplied as many as 539 people including 185 medical doctor of Korean oriental medicine. The rate of under-supplied will be 32.6%. As a result, the confrontation of demand and supply forecasts shows that, in general, severe shortages of R&D manpower in the areas of Korean oriental medicine will result if there are not adequate manpower policy adjustment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.385-391
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• 2018

Abalone aquacultural industry has been growing rapidly in a short period of time, however, there has been just a few researches related to the forecast of the supply, demand and price. Even the models developed by these researches have problems of low compatibility and reliability. To resolve these problem, a biological supply model needs to be developed that maintains time difference and linkage among the quantity of juvenile abalone into the plots, quantity of cultivation, quantity of shipment, and at the same time juvenile abalone is transplanted into the plot, matured and shipped by the expected market price. This study focus on the development of the model for determining quantity of the abalone cultivation, which is the core part of the entire abalone demand and supply model. Key factors that affect cultivation quantity were identified and verified the causal relationship among these variables and cultivation quantity. It turned out that the quantity of juvenile abalone transplanted and the relative price(the abalone price of the place of produce divided by the brown seaweed price) have a great influence on the cultivation quantity. Also, the similarity of the variation for the cultivation quantity of the observed value and the forecasted value implies that the model developed in this study has a high compatibility.

• Journal of Korean Society of Water and Wastewater
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• v.11 no.1
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• pp.109-118
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• 1997

In this study, we examined the structural analysis of water demand fluctuation for water distribution control of water supply network. In order to analyze for the length of stationary time series, we calculate autocorrelation coefficient of each case equally divided data size. As a result, it was found that, with the data size of around three months, any case could be used as stationary time series. we analyze cross-correlation coefficient between the daily water consumption's data and primary influence factors. As a result, we have decided to use weather conditions and maximum temperature as natural primary factors and holidays as a social factor. Applying the multiple ARIMA model, we obtains an effective model to describe the daily water demand prediction. From the forecasting result, even though we forecast water distribution quantity of the following year, estimated values well express the flctuations of measurements. Thus, the suitability of the model for practical use can be confirmed. When this model is used for practical water distribution control, water distribution quantity for the following day should be found by inputting maximum temperature and weather conditions obtained from weather forecast, and water purification plants and service reservoirs should be operated based on this information while operation of pumps and valves should be set up. Consequently, we will be able to devise a rational water management system.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.