• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.3
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• pp.205-212
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• 2008

This paper describes the application of an inverse building model to a calibrated forward building model using EnergyPlus program. Typically, inverse models are trained using measured data. However, in this study, an inverse building model was trained using data generated by an EnergyPlus model for an actual office building. The EnergyPlus model was calibrated using field data for the building. A training data set for a month of July was generated from the EnergyPlus model to train the inverse model. Cooling load prediction of the trained inverse model was tested using another data set from the EnergyPlus model for a month of August. Predicted cooling loads showed good agreement with cooling loads from the EnergyPlus model with root-mean square errors of 4.11%. In addition, different control strategies with dynamic cooling setpoint variation were simulated using the inverse model. Peak cooling loads and daily cooling loads were compared for the dynamic simulation.

• Atmosphere
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• v.29 no.4
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• pp.417-427
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• 2019

The purpose of this study is to build urban canopy model (Met Office Reading Urban Surface Exchange Scheme, MORUSES) based to Unified Model (UM) by using urban building information data in Seoul, and then to compare the improving urban canopy model simulation result with that of Seoul Automatic Weather Station (AWS) observation site data. UM-MORUSES is based on building information database in London, we performed a sensitivity experiment of UM-MOURSES model using urban building information database in Seoul. Geographic Information System (GIS) analysis of 1.5 km resolution Seoul building data is applied instead of London building information data. Frontal-area index and planar-area index of Seoul are used to calculate building height. The height of the highest building in Seoul is 40m, showing high in Yeoido-gu, Gangnam-gu and Jamsil-gu areas. The street aspect ratio is high in Gangnam-gu, and the repetition rate of buildings is lower in Eunpyeong-gu and Gangbuk-gu. UM-MORUSES model is improved to consider the building geometry parameter in Seoul. It is noticed that the Root Mean Square Error (RMSE) of wind speed is decreases from 0.8 to 0.6 m s^-1 by 25 number AWS in Seoul. The surface air temperature forecast tends to underestimate in pre-improvement model, while it is improved at night time by UM-MORUSES model. This study shows that the post-improvement UM-MORUSES model can provide detailed Seoul building information data and accurate surface air temperature and wind speed in urban region.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.773-775
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• 2003

From the view point of data fusion, we integrate LIDAR data and digital aerial images to perform 3D building modeling in this study. The proposed scheme comprises two major parts: (1) building block extraction and (2) building model reconstruction. In the first step, height differences are analyzed to detect the above ground areas. Color analysis is then performed for the exclusion of tree areas. Potential building blocks are selected first followed by the refinement of building areas. In the second step, through edge detection and extracting the height information from LIDAR data, accurate 3D edges in object space is calculated. The accurate 3D edges are combined with the already developed SMS method for building modeling. LIDAR data acquired by Leica ALS 40 in Hsin-Chu Science-based Industrial Park of north Taiwan will be used in the test.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.1
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• pp.47-54
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• 2019

This study proposes a new ERAM model with building population weighting. Previous studies of applying weightings on ERAM model on the scale of urban space were focused on the relationship between the street and the human behavior. However, this study focuses on the influences that buildings give to human behavior and develops a building population weighted ERAM model. This research starts by analyzing ERAM model to its basic compositions, which are adjacency matrix and row vector. It applies building population weighting to the row vector, while previous studies put weightings in the adjacency matrix. Building population weighted ERAM model calculates the building population weighting based on GIS data, which provides objective and massive data of buildings in the urban scale. For the verification of the model, Insa-dong and Myeong-dong were analyzed with both ERAM model and building population weighted ERAM model. The results were analyzed through the correlation test with actual pedestrian population data of the two districts. As a result, the explanation ability of building population weighted ERAM model for the pedestrian population turned out to be higher than the ERAM model. Since building population weighted ERAM model has the structure that can be combined with other weighted ERAM models, it is expected to develop a multi-weighted ERAM model with better explanation ability as a further study.

• Journal of Construction Engineering and Project Management
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• v.3 no.1
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• pp.14-21
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• 2013

To analyze building energy consumption, the building description for building energy performance analysis (BEPA) is required. The required data input for subject building is a basic step in the BEPA process. Since building information modeling (BIM) is applied in the construction industry, the required data for BEPA can be gathered from a single international standard file format like IFCXML. However, in most BEPA processes, since the required data cannot be fully used from the IFCXML file, a building description for BEPA must be created again. This paper proposes IFCXML-based automatic data input approach for BEA. After the required data for BEPA has been defined, automatic data input for BEPA is developed by a prototype system. To evaluate the proposed system, a common BIM file from the BuildingSMART website is applied as a sample model. This system can increase the efficiency and reliability of the BEPA process, since the data input is automatically and efficiently improved by directly using the IFCXML file..

• International conference on construction engineering and project management
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• 2013.01a
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• pp.173-180
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• 2013

To analyze building energy consumption, the building description for building energy performance analysis (BEPA) is required. The required data input for subject building is a basic step in the BEPA process. Since building information modeling (BIM) is applied in the construction industry, the required data for BEPA can be gathered from a single international standard file format like IFCXML. However, in most BEPA processes, since the required data cannot be fully used from the IFCXML file, a building description for BEPA must be created again. This paper proposes IFCXML-based automatic data input approach for BEA. After the required data for BEPA has been defined, automatic data input for BEPA is developed by a prototype system. To evaluate the proposed system, a common BIM file from the BuildingSMART website is applied as a sample model. This system can increase the efficiency and reliability of the BEPA process, since the data input is automatically and efficiently improved by directly using the IFCXML file.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• Architectural research
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• v.1 no.1
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• pp.23-30
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• 1999

This research investigates a system for house design automation. The system is based on an object-oriented building data model, aiming to support the house design process conducted by non-expert users. Its object model, with simple yet powerful user interfaces, enables a CAD system to handle a complicated building system with much ease. Hence, the model dramatically simplifies the design process beyond just the automatic document generation. In this paper, we discuss the aspects of the building data model, introduce critical concepts such as grid objects and structured floor plan, and present a prototype system called GPLAN. The system is implemented in the framework of our building data model, and it provides a host of intelligent features that have been proved useful for house design automation.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2011.05a
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• pp.137-140
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• 2011

A systemic building maintenance management is necessary to supply an convenience and safety environment by maintain the origin features for a building's life. However, the exist maintenance management system has some problems such as interoperability of information or standardization of data. In those reasons, a critical information for maintenance a building may be lost and changed. In addition, the data could be crashed or lost on a process of re-input or re-produce. This paper purpose the interoperability in exchanging data between design/construction and operation phases. In addition, this model will enhance the efficiency of building maintenance tasks through information quality improvement and data reproduction prevention.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.199-209
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• 2007

Traditionally, aerial images have been used as main sources for compiling topographic maps. In recent years, lidar data has been exploited as another type of mapping data. Regarding their performances, aerial imagery has the ability to delineate object boundaries but omits much of these boundaries during feature extraction. Lidar provides direct information about heights of object surfaces but have limitations with respect to boundary localization. Considering the characteristics of the sensors, this paper proposes an approach to extracting buildings from lidar and aerial imagery, which is based on the complementary characteristics of optical and range sensors. For detecting building regions, relationships among elevation contours are represented into directional graphs and searched for the contours corresponding to external boundaries of buildings. For generating building models, a wing model is proposed to assemble roof surface patches into a complete building model. Then, building models are projected and checked with features in aerial images. Experimental results show that the proposed approach provides an efficient and accurate way to extract building models.