• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.14 no.2
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• pp.1-7
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• 2018

The purpose of this study was to analyze the energy consumption of Elementary, Middle and High Schools in Daejeon Metropolitan City. The main results are as follow: 1) Annual energy consumption per class was 12,825 (kWh/class) at elementary schools, 15,780 (kWh/class) at middle schools, and 29,447 (kWh/class) at high schools, 2) Generally the smaller the size of the school, the higher the energy consumption per class. However according to the HVAC system there was no consistent pattern of energy consumption per class. 3) According to Box and Whisker's Chart, distribution of energy consumption of elementary and middle schools' had small range. However, the range of high schools increased. 4) Energy consumption in winter season was larger than that of summer season in schools.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.1
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• pp.82-87
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• 2005

The purpose of this study is to suggest the characteristics and actual state of energy consumption by the analysis of energy consumption data in an office building. This study examines and analyzes daily and monthly energy consumption of an office building located in Seoul, Korea regarding type of load and business classification within a building. The results are as follows. 1) Energy consumption of office building for each type of load show similar consumption patterns, regardless of seasons such as cooling period and heating period. 2) Out of all annual energy consumption, consumption for lighting took about $43\;\%,$ general electric Power about $23\;\%,$ emergency power $25\;\%,$ computer center $5\;\%$ and cooling power $4\;\%,$ showing that the consumption for lighting was highest, and the percentage of energy consumption for cooling power for operation of cooling facilities took the lowest percentage. 3) Annual gas consumption used for heating and hot water supply were $38,\;36\;\%$ for officetel and office respectively, and $26\;\%$ for arcade. 4) Electricity consumptions used for cooling power for each use of building, office and officetel recorded in July and August of cooling seasons. Even though it shows different patterns for each month, energy consumption showed unique pattern throughout the cooling seasons.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.10
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• pp.914-922
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• 2004

In this study, measurement and analysis of energy consumption of a research building have been conducted. The energy audit procedure includes monitoring of electricity and LNG consumption over a period of three yews from 2000 to 2002. Data acquisition system for collecting energy consumption data of HVAC equipment such as chillers, fan filter units, AHUs, cooling towers, boilers, pumps, fan coil units, air compressors and etc. has been installed in a building located in Seoul. Data collected at an interval of 1 minute are analyzed for studying the energy consumption pattern of a research building. Percentage of energy consumption of all HVAC equipment is $51.0\%$ in 2000, $55.4\%$ in 2001, and $62.3\%$ in 2002, respectively. Electricity consumption of chillers accounts for $17.6\%$ of the total energy consumption, which is the largest. Annual energy consumption-rate per unit area is $840.5Mcal/m^2{\cdot}y$ in 2000, $1,064.8Mcal/m^2{\cdot}y$ in 2001, and $1,393.0Mcal/m^2{\cdot}y$ year 2002, respectively.

• 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.

• Journal of the Korean Solar Energy Society
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• v.34 no.4
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• pp.1-7
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• 2014

The microgrid system is the combination of photovoltaic(PV) array, load, and battery energy storage system. The control strategies were defined as multi-modes of operation, including rest operation without use of battery, power charging, and power discharging, which enables grid connected mode or islanded mode. Photovoltaic power is a problem of the uniformity of power quality because the power generated from solar light is very sensitive to variation of insolation and duration of sunshine. As a solution to the above problem, energy storage system(ESS) is considered generally. There fore, in this study, we did basic research activities about optimization method of the amount of energy used, using a smart microgrid test-bed constructed in building. First, we analyzed the daily, monthly and period energy pattern amount of power energy used, and analyzed PV power generation level which is built on the roof. Utilizing building energy pattern analysis data, we was studied an efficient method of employing the ESS about building power consumption pattern and PV generation.

• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.336-341
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• 2008

For the last half a century, Korea has been experienced rapid economic growth and industrialization development, however they cause serious problems that environment pollution and energy shortage are appeared, and the biggest problem that we are now confronted are required solutions through all over the world. Now, Korea's energy consumption is the 10th in the whole world. Among them, energy for buildings, about 25% in the whole Energy which spend in Korea, is very serious. Especially, the energy consumption of school buildings which have heating & cooling system according to improvements of educational environment are rapidly increasing. These features are explicit in the University, Because it has lots of colleges and facilities for lecture, experiment, and research. Especially, electric power consumption account for 75 percent of energy consumption in educational institutions. Accordingly, it is important to understand and analyze the pattern of electricity energy consumption which is used. This study attempts to appoint the place which is one of university and to investigate the characteristics of energy consumption like electricity, gas, oil.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.10
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• pp.966-973
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• 2001

In this study, measurement and analysis of energy consumption of an office building with cleanroom and laboratory have been conducted. Data acquisition system for collecting energy consumption data of the whole building including air-conditioning equipments has been installed in a building located in Seoul. Data are collected for a period of one year in 2000 and analyzed for studying the energy consumption pattern. The percentage of electrical energy used for air-conditioning system is measured to be 46.1%. The collected data will serve as valuable information for diagnosing and improving the energy system of the building.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2490-2506
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• 2022

In Mobile Edge Computing (MEC), attackers can speculate and mine sensitive user information by eavesdropping wireless channel status and offloading usage pattern, leading to user privacy leakage. To solve this problem, this paper proposes a Privacy-preserving and Energy-efficient Offloading Algorithm (PEOA) based on Lyapunov optimization. In this method, a continuous Markov process offloading model with a buffer queue strategy is built first. Then the amount of privacy of offloading usage pattern in wireless channel is defined. Finally, by introducing the Lyapunov optimization, the problem of minimum average energy consumption in continuous state transition process with privacy constraints in the infinite time domain is transformed into the minimum value problem of each timeslot, which reduces the complexity of algorithms and helps obtain the optimal solution while maintaining low energy consumption. The experimental results show that, compared with other methods, PEOA can maintain the amount of privacy accumulation in the system near zero, while sustaining low average energy consumption costs. This makes it difficult for attackers to infer sensitive user information through offloading usage patterns, thus effectively protecting user privacy and safety.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.11
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• pp.1956-1963
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• 2010

Studies on the development of various energy management programs and real-time bidirectional information infrastructures have been actively conducted to promote the reduction of power demands and CO2 emissions effectively. In the conventional energy management programs, the demand response program that can transition or transfer the power use spontaneously for power prices and other signals has been largely used throughout the inside and outside of the country. For measuring the effect of such demand response program, it is necessary to exactly estimate short-term loads. In this study, the power consumption patterns in both individual and group consumers were analyzed to estimate the exact short-term loads, and the relationship between the actual power consumption and seasonal factors was also analyzed.