• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.139-146
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• 2012

In the server cluster environment, one of the ways saving energy is to control server's power according to traffic conditions. This is to determine the ON/OFF state of servers according to energy usage of data center and each server. To do this, we need a way to estimate each server's energy. In this paper, we use a software-based power consumption estimation model because it is more efficient than the hardware model using power meter in terms of energy and cost. The traditional software-based power consumption estimation model has a drawback in that it doesn't know well the computing status of servers because it uses only the idle status field of CPU. Therefore it doesn't estimate consumption power effectively. In this paper, we present a CPU field based power consumption estimation model to estimate more accurate than the two traditional models (CPU/Disk/Memory utilization based power consumption estimation model and CPU idle utilization based power consumption estimation model) by using the various status fields of CPU to get the CPU status of servers and the overall status of system. We performed experiments using 2 PCs and compared the power consumption estimated by the power consumption model (software) with that measured by the power meter (hardware). The experimental results show that the traditional model has about 8-15% average error rate but our proposed model has about 2% average error rate.

• Environmental and Resource Economics Review
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• v.19 no.2
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• pp.215-242
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• 2010

This study constructs a bayesian neoclassical DSGE model that applies oil usage. The model includes technology shocks, oil price shocks, and shocks to energy policies as exogenous driving forces. First, this study aims to analyze the roles of these exogenous shocks in the Korean business cycle. Second, this study examines the effects of long-term changes in the energy consumption structure, including the reduction in oil use as a share of energy consumption and improvement in oil efficiency. In the case of oil price shocks, results show that these shocks exert recessionary pressure on the economy in line with those obtained in the previous literature. On the other hand, shocks to energy policies, which reduce oil consumption per capital, result in opposite consequences to oil price shocks, decreasing oil consumption. Also, counterfactual exercises show that long-term changes in the energy consumption structure would mitigate the contractionary effects of oil price shocks.

• Journal of the Korean Operations Research and Management Science Society
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• v.4 no.2
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• pp.35-39
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• 1979

In order to recommend future national policy directions on energy supply and consumption and to suggest energy technological priorities to be developed, comprehensive energy models have been developed through this study in a sense of strategic and systematic approach. The “energy input-output model” has been formulated to analyze the mutual impacts between energy consumption patterns and industrial structures and to calculate energy intensities of industrial sectors. The long-term energy demands to the year 2000 were forecasted by using multi-regressional method and the optimal energy flow balances for five-year interval have been studied by using the “energy linear programming model” being took full account of interfuel substitutability and technology.

• Journal of IKEEE
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• v.13 no.3
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• pp.39-46
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• 2009

Wireless sensor networks consist of small, autonomous devices with wireless networking capabilities. In order to further increase the applicability in real world applications, minimizing energy consumption is one of the most critical issues. Therefore, accurate energy model is required for the evaluation of wireless sensor networks. In this paper we analyze the power consumption for wireless sensor networks. To develop the power consumption model, we have measured the power characteristics of commercial Kmote node based on TelosB platforms running TinyOS. Based on our model, the estimated lifetime of a battery powered sensor node can use about 6.9 months for application of human detection using PIR sensors. This result indicates that sensor nodes can be used in a monitoring system for elderly living alone.

• KIEAE Journal
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• v.16 no.2
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• pp.11-16
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• 2016

Purpose: International efforts to save Earth's environment against global warming and environmental pollution have been made in many countries. Energy consumption of buildings has been continuously increasing, and it has been over 40% of total energy consumption in the world. Energy consumption of buildings in Korea reaches 24% of total energy consumption. So, Korea government has executed building energy rating systems to control energy consumption of buildings. Method: This study was carried out to evaluate the energy performance of apartment unit plans according to converting balconies into living areas. For the study, six types of input models were made. Two input models(SP1 and SP 2) were the standard units that balcony areas were not converted into living areas, and four ones(EP 1, EP 2, EP 3 and EP 4) were the extended unit plans that balcony areas were turned into living areas. All of them were simulated with ECO2 software to assess building energy efficiency. Result: According to the results, the energy performance of the EP 2 and EP 4 models were 21. 8% higher than SP 1 model and 9.2% higher than SP 2 model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1133-1151
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• 2010

Energy balanced consumption routing is based on assumption that the nodes consume energy both in transmitting and receiving. Lopsided energy consumption is an intrinsic problem in low-cost sensor networks characterized by multihop routing and in many traffic overhead pattern networks, and this irregular energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of maximizing network lifetime through balancing energy consumption for uniformly deployed low-cost sensor networks. We formulate the energy consumption balancing problem as an optimal balancing data transmitting problem by combining the ideas of corona cluster based network division and optimized transmitting state routing strategy together with data transmission. We propose a localized cluster based routing scheme that guarantees balanced energy consumption among clusters within each corona. We develop a new energy cluster based routing protocol called "OECR". We design an offline centralized algorithm with time complexity O (log n) (n is the number of clusters) to solve the transmitting data distribution problem aimed at energy balancing consumption among nodes in different cluster. An approach for computing the optimal number of clusters to maximize the network lifetime is also presented. Based on the mathematical model, an optimized energy cluster routing (OECR) is designed and the solution for extending OEDR to low-cost sensor networks is also presented. Simulation results demonstrate that the proposed routing scheme significantly outperforms conventional energy routing schemes in terms of network lifetime.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4724-4747
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• 2016

To efficiently reduce on-grid energy consumption, the admission control algorithm in the hybrid energy powered cellular network (HybE-Net) with base stations (BSs) powered by on-grid energy and solar energy is studied. In HybE-Net, the fluctuation of solar energy harvesting and energy consumption may result in the imbalance of solar energy utilization among BSs, i.e., some BSs may be surplus in solar energy, while others may maintain operation with on-grid energy supply. Obviously, it makes solar energy not completely useable, and on-grid energy cannot be reduced at capacity. Thus, how to control user admission to improve solar energy utilization and to reduce on-grid energy consumption is a great challenge. Motivated by this, we first model the energy flow behavior by using stochastic queue model, and dynamic energy characteristics are analyzed mathematically. Then, fuzzy logic based admission control algorithm is proposed, which comprehensively considers admission judgment parameters, e.g., transmission rate, bandwidth, energy state of BSs. Moreover, the index of solar energy utilization balancing is proposed to improve the balance of energy utilization among different BSs in the proposed algorithm. Finally, simulation results demonstrate that the proposed algorithm performs excellently in improving solar energy utilization and reducing on-grid energy consumption of the HybE-Net.

• Advances in environmental research
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• v.4 no.3
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• pp.173-181
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• 2015

This article outlines a case study of water and energy savings in a typical building through a modelling process and analysis of simultaneous water-energy saving measures. Wet cooling towers are one of the most important equipments in buildings with a considerable amount of water and energy consumption. A variety of methods are provided to reduce water and energy consumption in these facilities. In this paper, thorough the modeling of a typical building, water and energy consumption are measured. Then, After application of modern methods known to be effective in saving water and energy, including the ozone treatment for cooling towers and shade installation for windows, i.e. fins and overhangs, the amount of water and energy saving are compared with the base case using the Simergy model. The annual water consumption of the building, by more than 50% reduction, has been reached to 500 cubic meters from 1024 cubic meters. The annual electric energy consumption has been decreased from 405,178 kWh to 340,944 kWh, which is about 16%. After modeling, monthly peak of electrical energy consumption of 49,428 has dropped to 40,562 kWh. The reduction of 18% in the monthly peak can largely reduce the expenses of electricity consumption at peak.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.14-21
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• 2021

Green networking has become a issue to reduce energy wastes and CO2 emission by adding energy managing mechanism to wired data networks. Energy consumption of the overall wired data networks is driven by access networks, expect for end devices. However, on a global scale, it is more difficult to manage centrally energy, measure and model the real energy use and energy savings potential of the access networks. This paper presented the multiple linear regression model to predict energy consumption of wired access networks using supervised learning of machine learning with data collected by existing investigated materials, actual measured values and results of many models. In addition, this work optimized the performance of it by various experiments and predict energy consumption of wired access networks. The performance evaluation of the regression model was achieved by well-knowned evaluation metrics.