• Solar Energy
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• v.18 no.3
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• pp.41-50
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• 1998

The purpose of this study is to analyze the energy consumption status for various building types in Taejon. 35 sample buildings were classified into 8 building types, i.e., sports center & swimming pools, hotels, telecommunication exchange service facility, hospitals, research laboratories, department stores, exhibition galleries, universities. According to analyses, energy consumption patterns varies significantly for each building type. Sports centers consumes highest rate(689 $Mcal/sqm{\cdot}yr$) and universities lowest rate(86 $Mcal/sqm{\cdot}yr$) among selected building types.

• 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.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.457-466
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• 2016

To achieve eco-friendly hospitals it is necessary to empirically verify the effect of meteorological factors on the power consumption of the hospital. Using daily meteorological big data from 2009 to 2013, we studied the weather conditions impact to power consumption and analyzed the patterns of power consumption of two hospitals. R analysis revealed that temperature among the meteorological factors had the greatest impact on the hospital power consumption, and was a significant factor regardless of hospital size. The pattern of hospital power consumption differed considerably depending on the hospital size. The larger hospital had a linear pattern of power consumption and the smaller hospital had a quadratic nonlinear pattern. A typical pattern of increasing power consumption during a hot summer and a cold winter was evident for both hospitals. The results of this study suggest that a hospital's functional specificity and meteorological factors should be considered to improve energy savings and eco-friendly building.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.3
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• pp.39-43
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• 2004

국제적인 환경 규제 문제($CO_2$, SF$_{6}$(육불화황), PFC (과불화화합물) 등)가 새로운 무역장벽으로 등장하여 우리나라 주요 산업 부문에 미치는 영향이 점차 현실로 나타나고 있다. 이러한 환경 규제에 적절한 대처를 하기 위해 에너지 소비 증가와 이에 따르는 에너지 소비 패턴 및 인식에 대한 새로운 접근 방법이 요구되고 있다. 그동안 전력기기의 고효율화, 에너지 효율 향상과 수요관리 강화, 미활용 에너지원의 이용률 제고, 집단에너지 보급 확대 등 하드웨어적인 기술개발에 중점을 두고 추진되었으나, 앞으로는 전원설비, 전력전송 설비, 부하설비 등의 최적 설계기술을 통한 에너지절약 기술개발과 같은 소프트웨어적인 기술 개발이 요청되고 있다. (중략)

• KIEAE Journal
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• v.12 no.2
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• pp.77-82
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• 2012

Data mining is to discover problems in the large amounts of data. Also, data mining trying to find the cause of the problem and the structure. Building energy consumption patterns, the amount of data is infinite. Also, the patterns have a lot of direct and indirect effects. Discussion is needed about the correlation. This work looking for the cause of energy consumption. As a result, energy management can find out the issue. Building energy analysis utilizing data mining techniques to predict energy consumption. And the results are as follows: 1) Using data mining technique, We classified complicated data to several patterns and gained meaningful informations from them. 2) Using cluster analysis, We classified building energy consumption data of residents and analyzed characters of patterns.

• Journal of Energy Engineering
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• v.22 no.2
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• pp.211-225
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• 2013

To run an optimal operation of Integrated energy supply facilities, we need to analyze heat consumption patterns of District heating users and derive optimum and maximum load ratio of heat production facilities unit. This study selects three District heat production facilities. It also classifies District heating users into residential apartment buildings and eight non-residential buildings and analyzes heat consumption results for an year. Finally it carries out the analysis of how the ratio change of each type affects maximum load ratio, facility utilization ratio, heat supply range. According to this study, three different District heat facilities of residential apartment building show similar daily and annual heat consumption patterns. Annual average load ratio, maximum load ratio and annual heat demand increase as outdoor temperatures decrease. Non-residential buildings in urban District focused on apartment buildings display similar by the daily and annual heat consumption patterns. Yet their daily and annual maximum load ratio differ according to outdoor temperature, District, building types and their composition ratio. In the case of urban District focused on apartment buildings reach optimum and maximum load ratio when apartment buildings reaches 60-70% of the total. At that point heat supply range becomes maximized and the most economic efficiency is obtained.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1223-1230
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• 2021

Recently, energy resource management is a major concern around the world. Energy management activities minimize environmental impacts of the energy production. This paper presents design and prototyping of a home electric energy monitoring system that provides residential consumers with real time information about their electricity use. The developed system is composed of an in-house sensing system and a server system. The in-home sensing system is a set of wireless smart plug which have an AC power socket, a relay to switch the socket ON/OFF, a CT sensor to sense current of load appliance and a Kmote. The Kmote is a wireless communication interface based on TinyOS. Each sensing node sends its detection signal to a home gateway via wireless link. The home gateway stores the received signals into a remote database. The server system is composed of a database server and a web server, which provides web-based monitoring system to residential consumers. We analyzed and presented energy consumption data from electrical appliances for 3 months in home. The experimental results show the promising possibilities to estimate the energy consumption patterns and the current status.

• Journal of the Korean Solar Energy Society
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• v.32 no.4
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• pp.90-95
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• 2012

Of school buildings, university building requires various case analysis unlike buildings in the elementary, middle and high schools in accordance with its characteristic for variables such as characteristic of department, construction structure and material, the number of persons admitted and schedule. Through the case research on the 'D' university located in Daejeon, this study made a comparison on the monthly and yearly consumption of gas and electricity of the most recent 3 years and implemented analysis on the usage pattern and standby power of air conditioning and heating by the hour and month using PCCS(Power Consumption Consulting System) as respects electricity that is considered to have a possibility of energy-saving. The result of analysis showed that enormous amount of electric power was used during the night time for freeze protection and burst in winter season and standby power was increased in winter season as a result.

• The Journal of Bigdata
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• v.6 no.1
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• pp.127-132
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• 2021

Korea is both a resource-poor country and a energy-consuming country. In addition, the use and dependence on electricity is very high, and more than 20% of total energy use is consumed in buildings. As research on deep learning and machine learning is active, research is underway to apply various algorithms to energy efficiency fields, and the introduction of building energy management systems (BEMS) for efficient energy management is increasing. In this paper, we constructed a database based on energy usage by device per household directly collected using smart plugs. We also implement algorithms that effectively analyze and predict the data collected using RNN and LSTM models. In the future, this data can be applied to analysis of power consumption patterns beyond prediction of energy consumption. This can help improve energy efficiency and is expected to help manage effective power usage through prediction of future data.

• 한국태양에너지학회:학술대회논문집
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• 2011.11a
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• pp.282-287
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• 2011

Of school buildings, university building requires various case analysis unlike buildings in the elementary, middle and high schools in accordance with its characteristic for variables such as characteristic of department, construction structure and material, the number of persons admitted and schedule. Through the case research on the 'D' university located in Daejeon, this study made a comparison on the monthly and yearly consumption of gas and electricity of the most recent 3 years and implemented analysis on the usage pattern and standby power of air conditioning and heating by the hour and month using PCCS(Power Consumption Consulting System) as respects electricity that is considered to have a possibility of energy-saving. The result of analysis showed that enormous amount of electric power was used during the night time for freeze protection and burst in winter season and standby power was increased in winter season as a result.