• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1225-1234
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• 2013

According to the importance of greenhouse gas emissions, it grows day by day, the goverment is promoting to prepare the specific policy implementation to enhance building energy-saving design standars as the development agenda. In this study, the statistical analysis was performed by Descriptive statistics, Regression analysis, and Hypothesis testing to collect to generate and storage energy usage data in real time to settle parameter setting to affect energy consumption under energy-guzzling apartment not single building. This study is expected to be utilized as the basis for the optimum energy-saving design of the future of the building or facility energy costs rise and the demand for energy-efficient and stable management.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.903-908
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• 2006

Building energy simulation has become a useful tool for predicting cooling, heating and air-conditioning loads for facilities. It is important to provide building energy performances feed back to the mechanical and electrical system operator and engineer for energy conservation and maintenance of building. From this research, we set up the typical weather data of location, basic description of building, geometric modelling data and the specification of Installed primary HVAC system for establishing the simulation model about energy consuming that take place in multipurpose building complex. The simulation tool of building energy - EnergyPlus (DOE and BLAST based simulation S/W), it has been used and accomplished calculations and analyses for evaluating the effect of the system types and operating condition of central HVAC plant on the building energy consumption. In this paper, we offer comparison and simultaneous results those involve electricity consumption pattern and amount between actual operation versus EnergyPlus simulation to the object building during summer season.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1511-1514
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• 2007

KORAIL is one of the biggest consumer in electric power market so it is necessary to investigate power consumption pattern and optimize power rates. This paper presents simulation program for electric power rates define program based on billing system database. Base on the substation annual power usage DB data, the characteristic of the substation power consumption are investigated and effective electrical billing system are compared each other. Through this program it is verified that we can save more then several hundred million won for a year. Also electric train air condition power consumption was measured to figure the method how to reduce peak energy consumption.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.1185-1190
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• 2009

Korea is an industrial country that overspends energy and has a policy that is more focused on a supply side. When an urban developmental program is to set up, surveys are carried out only with the respect to electricity, telecommunication, gas, and heating sources. Based on the existing survey results, the problems related to the supply side are being dealt with more importantly and the quantities of those supplies are estimated only by each energy source. The aim of this study is to provide basic information on energy consumption patterns of a diverse comsumer groups including industry, transportation, commerce, public and household to plan diverse energy policies. Through this basic information, it may be possible to analyze the energy consumption pattern by each consumer group and provide data for setting up efficient energy policies by the government. The energy consumption map that are analyzed and developed by the data obtained from Busan municipal area will be deposited and used as a part of the national energy statistics.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.

• Journal of Convergence for Information Technology
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• v.9 no.1
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• pp.19-27
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• 2019

In recent years, with the advancement of IT technology, we expect data size of the world to increase 10 times in 2025. The rapid development of this Internet technology leads to the downsizing of the server system of the data center which manages and operates the data, the capacity of the storage medium, and the power consumption of the data center. In this paper, we propose an energy conservation policy and analyze it in real time by analyzing the power consumption pattern of the server system of the data center. The proposed system can monitor and analyze the power consumption pattern of the individual server system in the data center, and it can be expected that about 10% of the total power consumption of the data center will be saved by efficiently managing the actual operation time of the server system.

• Proceedings of the KIEE Conference
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• 2005.10c
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• pp.238-240
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• 2005

The Optimization has been performed to search an economical running pattern in the view point of trip time and energy consumption. Fuzzy control model have been applied to build the meta-model. To identify the structure and its parameters of a fuzzy model, fuzzy c-means clustering method and differential evolutionary scheme are utilized, respectively. As a result, two meta-models for trip time and energy consumption were constructed. The optimization to search an economical running pattern was achieved by differential evolutionary scheme. The result shows that the proposed methodology is very efficient and conveniently applicable to the operation of railway system.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.594-600
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• 2006

The optimization has been performed to search an economical running pattern in the view point of trip time and energy consumption. Fuzzy control model has been applied to build the meta-model. To identify the structure and its parameters of a fuzzy model, fuzzy c-means clustering method and differential evolutionary scheme ate utilized, respectively. As a result, two meta-models for trip time and energy consumption are constructed. The optimization to search an economical running pattern is achieved by differential evolutionary scheme. The result shows that the proposed methodology is very efficient and conveniently applicable to the operation of railway system.

• Proceedings of the KIEE Conference
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• summer
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• pp.1385-1387
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• 2004

This paper presents a modelling methodology using fuzzy logic and train performance simulation for determining an economical running pattern for a high speed train which minimizes energy consumption under an given trip margin. The economical running pattern is defined with an economical maximum speed in traction phase, a speed at the end of coasting. As a case study, the simulation is carried out for an economical run of korea high speed train prototype, and the results of fuzzy model described.

• Journal of the Korean Society for Railway
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• v.9 no.1 s.32
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• pp.76-80
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• 2006

The Optimization has been performed to search an economical running pattern in the view point of trip time and energy consumption. Fuzzy control model have been applied to build the meta-model. To identify the structure and its parameters of a fuzzy model, fuzzy c-means clustering method and differential evolutionary scheme are utilized, respectively. As a result, two meta-models for trip time and energy consumption were constructed. The optimization to search an economical running pattern was achieved by differential evolutionary scheme. The result shows that the proposed methodology is very efficient and conveniently applicable to the operation of railway system.