• Journal of the Korean Solar Energy Society
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• v.29 no.5
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• pp.8-13
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• 2009

Feasibilities of the application of a micro gas turbine cogeneration system to a large size hospital building are studied by estimating energy demands and supplies. The energy demand for electricity is estimated by surveying and sorting the consumption records for various equipment and devices. The cooling heating, and hot water demands are further refined with TRNSYS and ESP-r to generate load profiles for the subsequent operation simulations. The operation of the suggested cogeneration system in conjunction with the load data is simulated for a time span of a year to predict energy consumption and gain profile. The simulation revealed that the thermal efficiency of the gas turbine is about 30% and it supplies 60% of the electricity required by the building. The recovered heat can meet 56% of total heating load and 67% of cooling, and the combined efficiency reaches up to 70%.

• Journal of the Korean Solar Energy Society
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• v.40 no.1
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• pp.15-24
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• 2020

Building energy consumption generally depends on living patterns of residents and outdoor air temperature changes. Although outdoor air temperature changes effect on building energy consumption, there is no calibration method for the comparison before and after Green Remodeling or BEMS installation etc., Big data of building energy consumption are collected and managed by 『National Integrated Management System of Building Energy』 in Korea, and they are utilized for the development of a calibration method for individual buildings as shown as the calibration method for small-area building stocks in the previous research. This study aims to develope a calibration method using big data of building energy consumption of individual buildings and outdoor air temperature changes, and to propose application of appropriate calibration methods for individual buildings or small-area building stocks according to the calibration purpose and conditions.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.14 no.4
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• pp.1-6
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• 2018

In this study, the energy consumption of the typical floor was compared by the total energy comsumption of the building in highrise building. In gerneral, many researchers are studying on the typical floor in highrise buildings for avoiding complexity in energy simulation. But few papers are studied on energy consumption along the floors. In the model bulding, the energy consumption data were acquired by BEMS system in 2011. According the data, the total net energy consumption was $193.99kWh/m^2$ for all area and the total net energy consumption was $247.61kWh/m^2$ for HVACR area. The total electricity and gas energy are used 47.7% for heating and cooling, 33.5% for lighting and plug, 12.9% for conveyance power and 5.9% for restaurant. In comparison of only ground floor, amount of energy consumption in the lobby is 10%, and 90% of total energy consumption is used in the typical floor. For this result, energy simulation on the typical floor is acceptable for calculating the total energy consumption in the highrise building.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.580-581
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• 2016

국제적으로 지속적인 이슈가 되고 있는 에너지 절감에 대한 대책으로 다양한 에너지 절감 기술들이 연구 개발되고 있다. 특히 전체 에너지 사용량의 약 20%이상을 차지하는 건물(가정/상업/공공)부문에서는 에너지 진단 및 분석을 수행하기 위해 건물 에너지 관리 시스템(BEMS: Building Energy Management System)과 건물 자동화 시스템(BAS: Building Automation System) 그리고 다양한 환경정보들을 수집하여 활용한다. 하지만 기존 분석 방식은 결과의 신뢰성에 최소한의 영향을 주면서 데이터 관리 효율을 높이는 방법에 초점을 맞춰 연구가 진행되었으며, 이를 위해 기존에 수집된 데이터를 압축하거나 샘플링하는 사전 정제 과정을 거치게 되었다. 하지만 빅데이터 플랫폼을 활용하면 더 이상 신뢰성을 낮추면서까지 데이터를 정제할 필요가 없어지고, 수집되는 모든 데이터에 대한 다차원 분석을 빠르게 수행할 수 있게 된다. 따라서 본 논문에서는 하드웨어의 한계로 기존 건물에너지 진단 및 분석 시스템에서 제공하지 못했던 다양한 분석 및 진단 서비스들을 빠르고 정확하게 제공하도록 하는 빅데이터 플랫폼 기반 건물 에너지 통합 관리 시스템 설계에 대해 서술한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.1038-1040
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• 2022

지속적인 지구 평균 기온 상승으로 인해 우리나라를 포함한 전 세계적으로 탄소중립을 위한 혁신이 이루어지고 있다. 본 연구는 해양수산부 '해양수산분야 2050 탄소중립 로드맵'의 기준에 따라 에너지 자립률을 극대화하고 효율을 최적화시킨 제로에너지 탄소중립 건축물을 제시한다. 태양광 발전 시스템에서, 패널의 태양 일주추적 기능을 통해 에너지 발전률을 극대화하고, 패널 하향정렬 및 딥러닝 모델을 통해 유지 보수를 용이하게 하여 성능 저하를 예방한다. 폐열을 이용한 열 회수/바이패스 환기 시스템을 통해 에너지 효율을 최적화하고, 온/습도에 가중치를 부여하여 모호했던 환기 시스템 결정 기준을 에너지 효율화에 맞게 최적화해 제시한다. 탄소중립 BEMS 기능이 내재된 앱 개발로 위의 건축물 시스템을 제어·관리한다. 본 연구를 통해 제로 에너지 건축물으로서 항만 건물의 가능성을 제고하고, 탄소중립 항만의 구현을 기대한다.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.6
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• pp.63-69
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• 2018

The machine learning model can capture the dynamics of building systems with less inputs than the first principle based simulation model. The training data for developing a machine learning model are usually selected in a heuristic manner. In this study, the authors developed a machine learning model which can describe supply air temperature from an AHU in a real office building. For rational reduction of the training data, the progressive sampling method was used. It is found that even though the progressive sampling requires far less training data (n=60) than the offline regular sampling (n=1,799), the MBEs of both models are similar (2.6% vs. 5.4%). In addition, for the update of the machine learning model, the normalized mutual information (NMI) was applied. If the NMI between the simulation output and the measured data is less than 0.2, the model has to be updated. By the use of the NMI, the model can perform better prediction ($5.4%{\rightarrow}1.3%$).