• 한국산학기술학회논문지
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• 제19권9호
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• pp.348-354
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• 2018

최근 BEMS(Building Energy Management System)는 공간정보를 적극 활용하고 있다. 공간정보가 포함된 BIM(Building Information Modeling)을 잘 활용한다면, 사용자는 공간정보와 연계된 직관적 건물 에너지 관리가 가능하다. 이 연구는 BIM-BEMS 연계를 위한 MF(Model Filter)을 활용한 데이터 맵핑 방법인 M-BDL(MF-based BIM Data Linkage)제안한다. 최근 BEMS은 3차원 공간정보를 적극 활용하고 있다. 이를 통해 사용자는 공간정보가 연계된 직관적인 건물 에너지관리가 가능하다. BIM 데이터를 에너지 관리 시스템에 활용하기 위해서는, 사용자 요구사항 관점에서 필요한 BIM 데이터만 BEMS과 연계할 필요가 있다. 하지만, Rich dataset인 BIM을 그대로 연계한다면 사용자가 불필요한 정보까지 관리해야 하는 부담을 주게 된다. M-BDL을 통해, 무거운 BIM 데이터에서 BEMS에 필요한 데이터만 맵핑함으로써, BIM데이터를 경량화할 수 있었고, 유지보수에 필요한 데이터량을 줄일 수 있다. 이 기술은 필요한 BIM 데이터만 필터링된 BIM 데이터와 BEMS 데이터베이스 간 연계할 수 있는 M-BDL 맵핑 방법을 제안한다.

• 대한건축학회논문집:구조계
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• 제36권2호
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• pp.175-180
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• 2020

The purpose of this study was to clarify precautions during the design and operation phases for energy reduction in a public office building. To check the operation status of the building, we measured the indoor temperature and humidity in the office space of the building installed central heating and cooling systems. And we analyzed these data and annual BEMS data. As a result, we found six problems related to decreasing system efficiency. Based on these, we presented the information to improve the efficiency of the system from the design and operation phase. Also, we present the need for a system to support the decision-making of operational managers in real-time for the energy efficiency of the building.

• International Journal of Internet, Broadcasting and Communication
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• 제10권4호
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• pp.65-69
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• 2018

In this paper, we propose an interworking architecture for building indoor air quality monitoring server (BEMS) using IIoT (Industrial Internet of Things). The proposed monitoring server adopts IIoT-based standard protocol so that interaction with BEMS installed in existing buildings can be performed easily. It can effectively communicate with indoor air quality measurement sensor installed in the building based on IIoT, Indoor air quality monitoring is possible. We implemented a proposed monitoring server, and confirmed the availability and monitoring of data from sensors in the building.

• 한국건축친환경설비학회 논문집
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• 제12권6호
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• pp.669-679
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• 2018

Energy consumption savings in buildings should be reviewed in diverse areas such as air conditioning system and lighting responsible for cooling and heating, and energy management systems such as BAS (Building Automation System) and BEMS (Building Energy Management System) are introduced to improve energy consumption efficiency and to promote economic control of related facilities by integrated management of energy generated and consumption in buildings. The measured building of this study uses regenerative geothermal system. Measured values of heat pump and system COP were 4.7 and 4.2 respectively, and they were found to be higher 11.9% and 23.5% than rated values. As a result of analyzing the air conditioning and lighting energy from the first floor to the fourth floor performing the air conditioning, the second and third floors, which have a high frequency of use, are compared with the first and fourth floors 50% higher energy consumption ratio. On the other hand, the general heat storage system uses the nighttime power of the previous day to store heat and use it the next day. The total number of days of abnormal operation during the summer season is 61 days. The electricity cost corresponding to the abnormal operation is 1,840,641 KRW, and the normal operation using the nighttime power is 1,363,561 KRW, which is difference of 477,080 KRW, 35% increase in cost. We will utilize it as the main data of BEMS through analysis of winter operation characteristics as well as summer operation characteristics.

• 대한건축학회논문집:구조계
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• 제35권2호
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• pp.45-52
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• 2019

BEMS(Building Energy Management Systems) have been applied to office buildings and collect relevant building energy data, e.g. temperatures, mass flow rates and energy consumptions of building mechanical systems and indoor spaces. The aforementioned measured data can be beneficially utilized for developing data-driven machine learning models which can be then used as part of MPC(Model Predictive Control) and/or optimal control strategies. In this study, the authors developed ANN(Artificial Neural Network) models of an AHU (Air Handling Unit) and a chiller for a real-life office building using BEMS data. Based on the ANN models, the authors developed optimal control strategies, e.g. daily operation schedule with regard to optimal start and stop of the AHU and the chiller (500 RT). It was found that due to the optimal start and stop of the AHU and the chiller, 4.5% and 16.4% of operation hours of the AHU and the chiller could be saved, compared to an existing operation.

• 설비공학논문집
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• 제26권10호
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• pp.467-473
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• 2014

Since the operating conditions of HVAC systems are different from those for which they are designed, on-going commissioning is required to optimize the energy consumed and the environment in the building. This study presents a methodology to analyze operational data and its applications. A predicted operation model is to be produced through a statistical data analysis using multiple regressions in SPSS. In this model, the dependent variable is the pre-cooling time, and the independent variables include the power output of the supply air inverter during pre-cooling, the supply air set temperature during pre-cooling, the indoor temperature-indoor set temperature just before pre-cooling, supply heat capacity, and the lowest outdoor air temperature during non-cooling/non-heating hours. The correlation coefficient R2 of the multiple regression model between the pre-cooling hour and the internal/external factors is of 0.612, and this could be used to provide information related to energy conservation and operating guidance.

• International journal of advanced smart convergence
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• 제8권1호
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• pp.82-86
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• 2019

We aim to use public data different from the remote BEMS energy diagnostics technology and already established and then switch the conventional operation environment to a big-data-based integrated management environment to operate and build a building energy management environment of maximized efficiency. In Step 1, various network management environments of the system integrated with a big data platform and the BEMS management system are used to collect logs created in various types of data by means of the big data platform. In Step 2, the collected data are stored in the HDFS (Hadoop Distributed File System) to manage the data in real time about internal and external changes on the basis of integration analysis, for example, relations and interrelation for automatic efficient management.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.706-707
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• 2015

Our country is aiming at 30% reductions in building energy consumption accounting for 39% of the total energy consumption by 2020[1]. For this purpose, the government is developing and applying the Building Energy Management System (hereinafter, referred to as "BEMS", Smart plug, etc.) while the researches on new renewable energy development. BEMS, which is applied with focus on large buildings, is inducing energy management of the entire building through energy measurement and data management, but considering its economic efficiency, it's very difficult to apply BEMS to small & medium-size buildings. Hereupon, this study intends to implement the case analysis of deterioration and economic efficiency of major equipment in buildings on the basis of electricity consumption which has been measured targeting small & medium-size buildings for a certain period by taking into account that equipment deterioration is a contributor to the increase in energy consumption.

• KEPCO Journal on Electric Power and Energy
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• 제5권3호
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• pp.157-163
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• 2019

현재까지 피크완화 및 에너지 절감을 위해 한국전력공사 120여개 사옥에 K-BEMS (KEPCO Building Energy Management System)가 운영 중이다. 이 시스템은 PV, PCS, BESS, EMS 등으로 구성되어 있으며 건물에너지 수요예측을 기반으로 BESS, PV 등을 활용하여 에너지 관리를 도모하고 있다. 이 시스템은 단기 과거데이터에 신경망기법을 단순 적용하여 수요를 예측함에 따라 예측 정확도가 높지 않고 운영자 수작업을 통한 BESS 충방전으로 피크 저감이 곤란하며 운영 경제성 제고가 어려운 실정이다. 이러한 문제를 해결하기 위해 전력연구원에서는 2016년부터 3년간 연구과제를 수행하였는데 이를 통해 에러를 최소화하며 높은 신뢰도를 가지는 실시간 수요예측기법과 이에 기반한 BESS충방전 최적화 자동화 기술 개발, 성능을 검증하였기에 이를 본 논문에서 소개하고자 한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2015년도 추계학술발표대회
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• pp.1110-1112
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• 2015

에너지 절감을 위해 다양한 분야에서 노력을 기울이고 있지만 전체 에너지 사용량의 약 20% 이상을 차지하는 건물 분야는 정부의 정책과 제도적인 지원 하에 에너지 절감활동을 활발하게 진행하고 있다. 특히 $3000m^2$ 이상의 중대형 건물의 경우 BEMS(Building Energy Management System)기반의 건물에너지 관리가 의무화 될 예정이다. 하지만 기존 BEMS의 경우 특정 기업에 의한 단독 솔루션 형태로 제공되고 있어 BEMS간 데이터 상호호환성을 보장하지 않고, 단순 모니터링 기능에 의존하여 저장/관리 되지 않고 버려지는 데이터들이 많아 차후 문제가 발생한 경우 과거 데이터를 통한 분석 작업에 어려움이 있다. 따라서 본 논문에서는 건물에너지 통합관리 측면에서 원격지에 설치된 다양한 BEMS들의 센서/미터 데이터들을 웹을 통해 수집하고 데이터 웨어하우스에 저장/관리되며 건물에너지 통계, 분석 및 진단을 가능하도록 하는 데이터 웨어하우스 기반의 원격 건물에너지 통합 관리 시스템 설계에 대해 서술한다.