• The Journal of Sustainable Design and Educational Environment Research
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• v.11 no.2
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• pp.19-27
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• 2012

This study was carried out in order to establish the estimation equation for school power consumption using regression analysis based on collected power consumption for two years of weather data and schools are located in Central Changwon and Masan district in Changwon city. (1) The power consumption estimation equation for Heating and cooling is calculated using power consumption per unit volume, the difference between actual power consumption and results of estimation equations is 4.1%. (2) The power consumption estimation equation for heating load is showed 2.6% difference compared to actual power consumption in Central Changwon and is expressed 2.9% difference compared to that in Masan district. Therefore, the power consumption prediction for each school using the power consumption estimation equation is possible. (3) The power consumption estimation equation for cooling load is showed 8.0% difference compared to actual power consumption in Central Changwon and is expressed 2.9% compared to that in Masan district. As the power consumption estimation equation for cooling load is expressed difference compared to heating load, it needs to investigate influence for cooling load.

• Journal of the Korean Solar Energy Society
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• v.32 no.3
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• pp.153-161
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• 2012

This study conducted a survey and field investigation on the application of the Public Obligation System for new & renewable energy in public buildings, as well as energy consumption of each building according to their uses. The findings are as follows: (1) Since the introduction of the Public Obligation System (until June 30, 2011), there was average 1.4 new & renewable energy facilities established at 1,433 places. Preference for solar energy facilities was the highest at 57.8%. (2) The revised act sets the obligatory supply percentage of new & renewable energy for each public building: it is 9.0% for a tax office, 4.2% for a dong office, 8.2% for a public health center, and 12.6% for a fire station. All the public buildings except for fire stations failed to meet 10% expected energy consumption, a revised standard. (3) Energy consumption of each public building was 120.6TOE for a tax office, 124.3TOE for a dong office, 166.4TOE for a public health center, and 174.6TOE for a fire station. The energy consumption was comprised of 80% electric power, 18% urban gas, and 1% oil. (4) Electric power consumption per person in the room was high at a dong office, and fuel consumption per person in the room was high at a public health center. In addition, electric power consumption per unit space was high at a public health center, and fuel consumption per unit space was high at a fire station. (5) In all the four public buildings, power load had the highest basic unit percentage at average 55%, being followed by heating load (21.2%), cooling load (15%), and water heating load (7%). A tax office and fire station had 2% load due to cooking facilities.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2011.05a
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• pp.193-195
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• 2011

Nowadays, climatic environment change has become a major issue in the world. This causes major emissions of carbon dioxide industries steel industry, thermal power industry, cement industry is essential in the reduction of carbon dioxide, which is based on total carbon dioxide emissions account for most of the construction industry in an effort to minimize the environmental load is needed. accordingly, through case studies, It can be induce the selection to minimize environmental load by comparing the output of quantitative energy consumption and carbon dioxide emissions per constructional methods. As a result of this study, RC Structure was less environmental load than SC structure.

• Journal of Biosystems Engineering
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• v.36 no.5
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• pp.303-313
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• 2011

This study was conducted to investigate the effects of axle weight distribution and inflation pressure of tire on the fuel economy of tractors as well as operational range of tractor engine in terms of engine speed and power when a 4WD tractor of 38.2 kW rated power at 2500 rpm is used for plowing and flooded-field rotavating in paddy fields. (1) Plowing operation required an average engine power of 9.6~13.5 kW which equals 25~35% of rated PTO power. Engine speed ranged from 1,320.4 to 1,737.4 rpm, work velocity from 3.4 to 4.8 km/h, and fuel consumption from 3.2 to 4.2 L/h, respectively. (2) Flooded-field rotavating required an average engine power of 11.5~18.5 kW which equals 30~48.4% of rated PTO power. Out of this 6.2~12.2 kW was used for PTO power. Engine speed ranged from 1,557 to 2,067 rpm, work velocity from 2.5~5.4 km/h and fuel consumption from 3.2~5.5 L/h, respectively. (3) Axle weight distribution, inflation pressure of tire and moisture content of soil did not affect significantly the specific volumetric fuel consumption but affected significantly the fuel consumption per unit area of operation. Fuel savings amounted to 65% in plowing operation and 20% in flooded-field rotavating when the axle weight distribution and inflation pressure of tire were optimally adjusted. (4) Optimal adjustment of axle weight distribution and inflation pressure of tire are expected to save fuel consumption by 10~65% per unit area of operation in plowing and 10~20% in flooded-field rotavating.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.12
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• pp.1249-1255
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• 2011

In modern large-scale semiconductor manufacturing clean rooms, the energy consumed by the outdoor air-conditioning system during heating, humidification, cooling, and dehumidification of the incoming outdoor air represents about 45% of the total air-conditioning load required to maintain a clean-room environment. In particular, the energy required for humidification of the outdoor air in winter is very high. Therefore, evaluation and comparison of the energy consumption in key humidification systems, viz., steam-humidification and water-spray-humidification systems, used in outdoor air-conditioning systems would be useful to reduce the outdoor air-conditioning load in clean rooms. In the present study, an experiment with an outdoor air flow of 1000 $m^3$/h was conducted to compare the air-conditioning process and energy consumption in outdoor air-conditioning systems with electrodeboiler steam humidifiers and air-washer water spray humidification systems. The experimental results showed that the water-spray-humidification-type outdoor air-conditioning system consumed less electrical power than did the steam-humidification-type system and was more energy efficient during winter.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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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 let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.

• Journal of Environmental Policy
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• v.11 no.4
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• pp.85-105
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• 2012

In this paper, the energy efficiency of wastewater utilities was evaluated to explore ways to save energy via operational measures. The correlation of each wastewater characteristic parameter to energy was assessed to find a set of parameters that explained most of the variations in energy use among utilities. The results show that increases in inflow, influent COD concentration, and ratio of advanced treatment generally increased the energy use. On the other hand, increases in load factor (influentaverage flow/design flow) reduced the energy use. In the regression analysis, the energy efficiency was highest in the A2O advanced process. On the other hand, the membrane process (among the advanced processes) and the contacted aeration process (among the secondary processes) require more efforts in saving energy. However, the data base system related to energy use must be supplemented in order for more accurate analysis of energy consumption in wastewater treatment facilities. In particular, i) electricity consumption of relay pumps and, ii) energy usage per unit process, iii) pump power usage to discharge treated wastewater in a long distance, if necessary, and iv) alternative energy production and utilization status must be recorded. By utilizing the results of the analysis conducted in this study, it is possible to quantify a level of energy savings needed and establish customized energy saving measures to achieve a certain target level for benchmarking a successful case of wastewater utilities.