• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1132-1136
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• 2014

In the context of increasing electric energy consumption in a data center, energy efficiency improvement is strongly emphasized. In a data center, electric energy is largely consumed by DC power supply system, which is based on a rectifier composed by multiple parallel converters. Therefore, rectifier efficiency must be improved for minimizing loss of DC power supply system. Rectifier efficiency can be modulated by load allocation to converters because converter efficiency depends on input AC power. In this paper, we propose a new control method to maximize rectifier efficiency. The method can control load allocation to converters by introducing active power converter control scheme and start-and-stop of converters. In order to illustrate optimal load allocations in a rectifier, a maximization problem of rectifier efficiency is formulated as a nonlinear optimization one. The problem is solved by Lagrangian relaxation method and the computation results provide the validity of proposed method.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.4
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• pp.354-362
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• 2000

Optimal supervisory control strategy for the set points of controlled variables in the central cooling system has been studied by computer simulation. A quadratic linear regression equation for predicting the total cooling system power in terms of the controlled and uncontrolled variables was developed using simulated data collected under different values of controlled and uncontrolled variables. The optimal set temperatures such as supply air temperature, chilled water temperature, and condenser water temperature, are determined such that energy consumption is minimized as uncontrolled variables, load, ambient wet bulb temperature, and sensible heat ratio, are changed. The chilled water loop pump and cooling tower fan speeds are controlled by the PID controller such that the supply air and condenser water set temperatures reach the set points designated by the optimal supervisory controller. The influences of the controlled variables on the total system and component power consumption was determined. It is possible to minimize total energy consumption by selecting the optimal set temperatures through the trade-off among the component powers. The total system power is minimized at lower supply, higher chilled water, and lower condenser water set temperature conditions.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.522-528
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• 2012

Currently, the energy consumption of electric lighting in an office building is accounted for more than 30% of the total energy consumption. In order to reduce the energy consumed by the indoor lighting, the daylight as a natural energy resource can play an important role in energy savings. The daylight can have positive impacts on improvement of work efficiency and productivity, and also make people feel more psychologically stable. Moreover, by using the daylight, we can definitely reduce the energy consumption in office buildings. Thus, the purpose of this study is to determine the LED dimming ratio depending on the ratio by calculating the amount of artificial illumination required according to the change of daylight by using the light simulation software that can quantify and visualize the performance of daylight. As a result, the energy savings could be obtained up to 30%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4A
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• pp.365-372
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• 2007

In IEEE 802.11b wireless network, stations synchronize themselves to the beacons periodically sent by the access point(AP) when they are running in low power mode. In case of missing beacon due to noise or traffic from neighboring wireless network stations must be awake until they get the next beacon, which causes energy consumption in stations. In this paper, we propose a scheme searching next beacon consuming little energy. The problems of missing beacon in low power mode of IEEE 802.11b wireless interface are described and a new method to reduce energy consumption is proposed. The proposed method is simulated with the network simulator, ns2, and compared with the low power mode of the IEEE 802.11b. The result measured in terms of station's wakeup time shows some enhancement in energy consumption when some errors occur in receiving frames.

• Journal of Electrochemical Science and Technology
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• v.9 no.1
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• pp.37-43
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• 2018

In order to improve current efficiency and decrease energy consumption in the electrosynthesis of ammonium persulfate, electrolytic properties of four cation exchange membranes, namely, the $JCM-II^{(R)}$ membrane, $Nafion^{(R)}$ 324 membrane CMI-$7000^{(R)}$ membrane and a self-made perfluorosulfonic ion exchange membrane (PGN membrane) were investigated using a sintered platinized titanium anode and a Pb-Sb-Sn alloy cathode in a self-made electrolytic cell. The effect of cell voltage and electrolyte flow rate on the current efficiency and the energy consumption were investigated. The results indicated that the PGN membrane could improve current efficiency to 94.85% and decrease energy consumption to $1119kWh\;t^{-1}$ (energy consumption per ton of the ammonium persulfate generated) under the optimal operating conditions and the highest current efficiency of the $JCM-II^{(R)}$ membrane, $Nafion^{(R)}$ 324 membrane and CMI-$7000^{(R)}$ membrane were 80.73%, 77.76% and 73.22% with their lowest energy consumption of $1323kWh\;t^{-1}$, $1539kWh\;t^{-1}$ and $2256kWh\;t^{-1}$, respectively. The PGN membrane has the advantages of high current efficiency and energy power consumption and has sufficient mechanical strength with the reinforced mesh. Therefore the PGN membrane will has good value in popularization in the industrial electrosynthesis of ammonium persulfate in the future.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.10a
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• pp.289-292
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• 2008

This paper represents the electric energy saving using light control system. Without Light Control System(LCS), it is very hard to classify the amount of electric energy saving for lighting, since it is determined by the end-user's preference in times. Typical LCS has an fixed algorithm to control each light's intensity by ON/Off Control, Step Control, or Dimming Control. We have studied the electric energy saving effect in a LCS equiped building with natural lighting. The electric power consumption at the peak power consumption time with LCS reduced up to 65% of the non-controled case.

• Journal of IKEEE
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• v.22 no.3
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• pp.822-828
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• 2018

Recently, energy issues such as massive blackout due to increase in power consumption have been emerged, and it is necessary to improve the accuracy of prediction of power consumption as a solution for these problems. In this study, we investigate the difference between the actual power consumption and the predicted power consumption through the deep learning- based power consumption forecasting experiment, and the possibility of adjusting the power reserve ratio. In this paper, the prediction of the power consumption based on the deep learning can be used as a basis to reduce the power reserve ratio so as not to excessively produce extra power. The deep learning method used in this paper uses a learning model of long-short-term-memory (LSTM) structure that processes time series data. In the computer simulation, the generated power consumption data was learned, and the power consumption was predicted based on the learned model. We calculate the error between the actual and predicted power consumption amount, resulting in an error rate of 21.37%. Considering the recent power reserve ratio of 45.9%, it is possible to reduce the reserve ratio by 20% when applying the power consumption prediction algorithm proposed in this study.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.8
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• pp.629-634
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• 2009

In this paper, drying mechanism is analyzed for improving the energy efficiency of an electric clothes dryer which consumes more electric power than other appliances. For the purpose, characteristic curves of the dryer such as temperature, relative humidity, evaporation rate, mass transfer coefficient, remaining moisture content curves are experimentally obtained. Based on the experimental results and analysis of drying mechanism, the effect of power of a heater and heat loss on the power consumption of an electric clothes dryer are systematically presented. These results demonstrate the feasibility of controlling heat loss at the heater as well as the backduct component to decrease the power consumption of an electric clothes dryer.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2262-2267
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• 2008

In this paper, drying mechanism is analyzed for improving the energy efficiency of an electric clothes dryer which consumes more electric power than other appliances. For the purpose, characteristic curves of the dryer such as temperature, relative humidity, evaporation rate, mass transfer coefficient, remaining moisture content curves are experimentally obtained. Based on the experimental results and analysis of drying mechanism, the effect of power of a heater and heat loss on the power consumption of an electric clothes dryer are systematically presented. These results demonstrate the feasibility of controlling heat loss at the heater as well as backduct component to decrease the power consumption of an electric clothes dryer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.312-334
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• 2023

In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources. The evolution of cloud has taken place in the development of data centers and numerous servers across the globe. But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides. It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration. But the performance may get affected in case of harsh consolidation of VMs. So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption. This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure. The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm. PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers. Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns. Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request. The proposed algorithms' efficiency was validated through different simulations of extensive nature. The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA. On the basis of the project algorithms, the energy consumption got reduced by 22% while there was an improvement observed in SLA up to 80% compared to other benchmark algorithms.