• Journal of the Korea Safety Management & Science
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• v.13 no.2
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• pp.267-273
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• 2011

Since 'The Act on Private Investment of The Infrastructure' was established in 1994, private investment as well as government's investment in transport infrastructure has been active. However investment in transport infrastructure has more risks than others' due to uncertainty both in traffic volume and in construction cost. In the current appraisal procedure of deciding transportation infrastructure investment, instead of risk management, the sensitivity analysis considering only the changes of benefit, cost and social discount rate which are main factor affecting economic feasibility is carried out. Therefore the uncertainty of various factors affecting demand, cost and benefit are not considered in feasibility study. In this study the problems in current investment appraisal system were reviewed. Using Delphi technique the major factors which have high uncertainty in feasibility study were surveyed and then improvement plan was suggested in the respective of classic 4 step demand forecasting method. The range estimation technique was also mentioned to deal with the uncertainty of the future.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1503-1510
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• 2014

This study organizes scenarios on the power supply plans and electricity load forecasts considering their uncertainties and estimates natural gas quantity for electricity generation, total electricity supply cost and air pollutant emission of each scenario. Also the analysis is performed to check the properness of government's natural gas demand forecast and the possibility of achieving the government's CO2 emission target with the current plan and other scenarios. In result, no scenario satisfies the government's CO2 emission target and the natural gas demand could be doubled to the government's forecast. As under-forecast of natural gas demand has caused the increased natural gas procurement cost, it is required to consider uncertainties of power plant construction plan and electricity demand forecast in forecasting the natural gas demand. In addition, it is found that CO2 emission target could be achieved by enlarging natural gas use and demand-side management without big increase of total costs.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.349-371
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• 2022

Purpose: Compared to the rapid growth rate of the domestic automotive LED industry so far, the predictive analysis method for demand forecasting or market outlook was insufficient. Accordingly, product characteristics are analyzed through the life trend of LEDs for automotive exterior lamps and the relative strengths of p and q using the Bass model. Also, future demands are predicted. Methods: We used sales data of a leading company in domestic market of automotive LEDs. Considering the autocorrelation error term of this data, parameters m, p, and q were estimated through the modified estimation method of OLS and the NLS(Nonlinear Least Squares) method, and the optimal method was selected by comparing prediction error performance such as RMSE. Future annual demands and cumulative demands were predicted through the growth curve obtained from Bass-NLS model. In addition, various nonlinear growth curve models were applied to the data to compare the Bass-NLS model with potential market demand, and an optimal model was derived. Results: From the analysis, the parameter estimation results by Bass-NLS obtained m=1338.13, p=0.0026, q=0.3003. If the current trend continues, domestic automotive LED market is predicted to reach its maximum peak in 2021 and the maximum demand is $102.23M. Potential market demand was $1338.13M. In the nonlinear growth curve model analysis, the Gompertz model was selected as the optimal model, and the potential market size was $2864.018M. Conclusion: It is expected that the Bass-NLS method will be applied to LED sales data for automotive to find out the characteristics of the relative strength of q/p of products and to be used to predict current demand and future cumulative demand.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.137-143
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• 2014

Demand Controller is a load control device that monitor the current power consumption and calculate the forecast power to not exceed the power set by consumer. Accurate demand forecasting is important because of controlling the load use the way that sound a warning and then blocking the load when if forecasted demand exceed the power set by consumer. When if consumer with fluctuating power consumption use the existing forecasting method, management of demand control has the disadvantage of not stable. In this paper, load forecasting of the unit of seconds using the Exponential Smoothing Methods, ARIMA model, Kalman Filter is proposed. Also simulation of load forecasting of the unit of the seconds methods and existing forecasting methods is performed and analyzed the accuracy. As a result of simulation, the accuracy of load forecasting methods in seconds is higher.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.331-332
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• 2015

Since $20^{th}$ century, along with the rapid industrial advancement, the concentrated urban development in specific large cities have made people migrate to those cities, thus causing problems in the power system stability. In case of Korea, more than 40% of the power system demand comes from the consumers in Seoul Metropolitan area and the rate is expected to increase. With the continuous increase of power demand, in order to meet the demand for system reliability improvement, the power system was multi-looped for reliability enhancement, the problem of fault current happened. In this situation, there are several methods for fault current reduction likes current limiting reactor, replacing circuit breaker, splitting busses, etc. But these methods reached its limit, power system needs more fundamental solutions such as grid segmentation. In this paper, we assume grid segmentation already has been progressed using VSC BTB HVDC. Then, this paper discusses operating point of HVDC in metropolitan area considering loss minimization and handy flow control. The simulation is proceeded on 2027 KEPCO system.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.45-47
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• 1993

Load Management, is originated from efficiency improvement of energy use, or energy conservaion. Traditionally, electric utilities have constructed new power plants to meet the steadily increasing electricity demand. Power development planning, however, is becoming more difficult in the countries like Korea, Japan, and the United States, and increasing concerns about global environmental problems necessitate changes from existing supply-side options based on fossil-fuel to environmentally agreeable supply strategies. This paper discusses the demand side management strategy with emphasis on the concept, implementation scheme, and current practices employed in utilities.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.4
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• pp.382-391
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• 2005

Increasing of the nonlinear type power electronics equipment, power conditioning systems (PCS) have been researched and developed for many years in order to compensate for harmonic disturbances and reactive power. PCS's not only improve harmonic current and power factor in the ac grid line but also achieves energy saving used by the renewable energy source (RES). In this paper, the implementation of a current controlled voltage source inverter (CCVSI) using RES for PCS is presented. The basic principle and control algorithm is theoretically analyzed and the design methodology of the system is discussed. The proposed system could achieve power quality control (PQC) to reduce harmonic current and improve power factor, and demand side management (DSM) to supply active power simultaneously, which are both operated by the polarized ramp time (PRT) current control algorithm and the grid-interactive current control algorithm. A 1KVA test model of the CCVSI has been built using IGBT controlled by a digital signal processor (DSP). To verify the proposed system, a comprehensive evaluation with theoretical analysis, simulation and experimental results is presented.

• The Journal of Industrial Distribution & Business
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• v.7 no.3
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• pp.27-36
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• 2016

Purpose - Considering the importance of housing needs to real estate market, domestic studies on real estate prices from the perspective of demand are basically based on macro-data, but relatively few are associated with micro-data of urban real estate demand. We try to find a reliable relation of elasticity of demand and commercial housing market. Research design, data, and methodology - In this paper, we have derived housing demand theoretic method and have utilized micro-data of residential family housing survey of downtown area in Kunming City in October, 2015 to estimate income elasticity and price elasticity of housing demand respectively and make a comparative analysis. Results - The results indicate that income elasticity and price elasticity of families with owner-occupied housing are both larger than those of families with rental housing. Income elasticity of housing demand of urban residential families in Kunming is far below the foreign average and eastern coastal cities level, however, the corresponding price elasticity is far higher. Conclusions - We suggest that housing affordability of urban families in western China are constrained by the level of economic development, and the current housing price level has exceeded the economic affordability and psychological expectation of ordinary residents. Furthermore, noticing the great rigidity of housing demand, the expansion space of housing market for improvement and for commodity is limited.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.3
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• pp.75-85
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• 2021

This study evaluated variations in the paddy rice water demand based on the continuous changing in rice transplanting period and ponding depth at the four study paddy fields, which represent typical rice producing regions in Korea. Total 7 scenarios on rice transplanting periods were applied while minimum ponding depth of 0 and 20 mm were applied in accordance with maximum ponding depth ranging from 40 mm to 100 mm with 20 mm interval. The weather data from 2013 to 2019 was also considered. The results indicated that the highest rice water demand occurred at high temperature and low rainfall region. Increased rice transplanting periods showed higher rice water demand. The rice water demand for 51 transplanting days closely matched with the actual irrigation water supply. In case of ponding depth, the results showed that the minimum ponding depth had a proportional relationship with rice water demand, while maximum ponding depth showed the contrary results. Minimum ponding depth had a greater impact on rice water demand than the maximum ponding depth. Therefore, these results suggest that increasing the rice transplanting period, which reflects the current practice is desirable for a reliable estimation of rice water demand.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.3
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• pp.225-231
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• 2016

Peak load rate(i.e., maximum daily flow/average daily flow) has not been considered for industrial water demand planning in Korea to date, while area unit method based on average daily flow has been applied to decide capacity of industrial water treatment plants(WTPs). Designers of industrial WTPs has assumed that peak load would not exist if operation rate of factories in industrial sites were close to 100%. However, peak load rates were calculated as 1.10~2.53 based on daily water flow from 2009 to 2014 for 9 industrial WTPs which have been operated more than 9 years(9-38 years). Furthermore, average operation rates of 9 industrial WTPs was less than 70% which means current area unit method has tendency to overestimate water demand. Therefore, it is not reasonable to consider peak load for the calculation of water demand under current area unit method application to prevent overestimation. However, for the precise future industrial water demand calculation more precise data gathering for average daily flow and consideration of peak load rate are recommended.