• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.348-354
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• 2018

This study proposes MF (Model Filter)-based M-BDL (MF-based BIM Data Linkage), which is a model filter-based data mapping method for BIM (Building Information Modeling)-BEMS linkage. Recently, BEMS (Building Energy Management System) is actively utilizing 3D spatial information. This allows the user to intuitively manage the facility energy linked to spatial information. To use BIM data in energy management systems, it is essential to link BEMS with BIM data only in terms of the user requirements. On the other hand, if the BIM is a rich dataset and is linked as it is, the user will need to manage the unnecessary information. By mapping only the data required for BEMS in heavy BIM data through M-BDL, the BIM data can be lightened and the amount of data required for maintenance can be reduced. This technology proposes a mapping method that can link the BIM data with the filtered BIM data.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.2
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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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• v.10 no.4
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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.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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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.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.2
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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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.10
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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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• v.8 no.1
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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.

• International conference on construction engineering and project management
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• 2015.10a
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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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• v.5 no.3
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• pp.157-163
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• 2019

K-BEMS System was introduced to reduce peak load and to save total energy of the 120 buildings that KEPCO headquarter and branch offices use. K-BEMS system is composed of PV, battery, and hybrid PCS. In this system, ESS, PV, lighting is used to save building energy based on demand prediction. Currently, neural network technique for short past data is applied to demand prediction, and fixed scheduling method by operator for ESS charging/discharging is used. To enhance this system, KEPCO research institute has carried out this K-BEMS research project for 3 years since January 2016. As the result of this project, we developed new real-time highly reliable building demand prediction technique with error free and optimized automatic ESS charging/discharging technique. Through several field test, we can certify the developed algorithm performance successfully. So we will describe the details in this paper.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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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.