• Proceedings of the KSRS Conference
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• v.2
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• pp.899-902
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• 2006

For a quick and accurate 3D modelling of a building, laser scanning data, digital maps, aerial photographs and satellite images should be fusioned. Moreover, library establishment according to a standard structure of a building and effective texturing method are required in order to determine the structure of a building. In this study, we made a standard library by categorizing Korean village forms and presented a model that can predict a structure of a building from a shape of the roof on an aerial photo image. We made an ortho image using the high-definition digital image and considerable amount of ground scanning point cloud and mapped this image. These methods enabled a more quick and accurate building modelling.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.937-942
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• 2008

In this paper, new methodology is proposed to estimate the cooling load using design parameters of building and predicted weather data. Only two parameters such as maximum and minimum temperature are necessary to obtain hourly distribution of cooling load for the next day. The maximum and minimum temperature that are used for input parameters can be obtained from forecasted weather data. Benchmarking building(research building) is selected to validate the performance of the proposed method, and the estimated cooling loads in hourly bases are calculated and compared with the measured data for benchmarking building. The estimated results show fairly good agreement with the measured data for benchmarking building.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.10
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• pp.497-503
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• 2017

In recent years, studies on the prediction of building load using Artificial Neural Network (ANN) models have been actively conducted in the field of building energy In general, building loads predicted by ANN models show a sharp deviation unless large data sets are used for learning. On the other hands, some of the input data are hard to be acquired by common measuring devices. In this work, we estimate daily building loads with a limited number of input data and fewer pastdatasets (3 to 10 days). The proposed model with fewer input data gave satisfactory results as regards to the ASHRAE Guide Line showing 21% in CVRMSE and -3.23% in MBE. However, the level of accuracy cannot be enhanced since data used for learning are insufficient and the typical ANN models cannot account for thermal capacity effects of the building. An attempt proposed in this work is that learning procersses are sequenced frequrently and past data are accumulated for performance improvement. As a result, the model met the guidelines provided by ASHRAE, DOE, and IPMVP with by 17%, -1.4% in CVRMSE and MBE, respectively.

• KIEAE Journal
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• v.17 no.5
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• pp.77-86
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• 2017

Purpose: Because of the growing concern over fossil fuel use and increasing demand for greenhouse gas emission reduction since the 1990s, the building energy analysis field has produced various types of methods, which are being applied more often and broadly than ever. A lot of research products have been actively proposed in the area of the building energy simulation for over 50 years around the world. However, in the last 20 years, there have been only a few research cases where the trend of building energy analysis is examined, estimated or compared. This research aims to investigate a trend of the building energy analysis by focusing on methodology and characteristics of each method. Method: The research papers addressing the building energy analysis are classified into two types of method: engineering analysis and algorithm estimation. Especially, EPG(Energy Performance Gap), which is the limit both for the existing engineering method and the single algorithm-based estimation method, results from comparing data of two different levels- in other words, real time data and simulation data. Result: When one or more ensemble algorithms are used, more accurate estimations of energy consumption and performance are produced, and thereby improving the problem of energy performance gap.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.248-250
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• 2003

An automatic approach and strategy for extracting building information from aerial images using combined image analysis and interpretation techniques is described in this paper. A dense DSM is obtained by stereo image matching. Multi-band classification, DSM, texture segmentation and Normalised Difference Vegetation Index (NDVI) are used to reveal building interest areas. Then, based on the derived approximate building areas, a shape modelling algorithm based on the level set formulation of curve and surface motion has been used to precisely delineate the building boundaries. Data fusion, based on the Dempster-Shafer technique, is used to interpret simultaneously knowledge from several data sources of the same region, to find the intersection of propositions on extracted information derived from several datasets, together with their associated probabilities. A number of test areas, which include buildings with different sizes, shape and roof colour have been investigated. The tests are encouraging and demonstrate that the system is effective for building extraction, and the determination of more accurate elevations of the terrain surface.

• Journal of Environmental Impact Assessment
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• v.22 no.1
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• pp.99-107
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• 2013

This study is an impact assessment of building blocks on urban inundation depth and area. LiDAR data is used to generate two original data set in terms of DEM with $5{\times}5$ meter and building block elevation layer of the study drainage area in Cheongju and then the building block elevation layer is modified again to the mesh data with same size to DEM. Two-dimensional inundation analysis is carried out by applying 2D SWMM model. The inundation depth calculated by using the building block elevation layer shows higher reliability than the DEM. This is resulted from the building block interference to surface flow. In addition, the maximum flooded area by DEM is two times wider than the area by building block layer. In the case of the surface velocity, the difference of velocity is negligible in either DEM or building block case in the low building impact zone. However, If the impact of building on the surface velocity was increase, the gap of velocity was significant.

• 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.

• Earthquakes and Structures
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• v.11 no.6
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• pp.1043-1060
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• 2016

The engineering background of this article is an ancient wooden building with extremely high historic and cultural values in Tibet. A full understanding of the dynamic behaviour of this historic building under in-service environments is the basis to assess the condition of the structure, especially its responses to earthquake, environmental and operational loading. A dynamic monitoring system has been installed in the building for over one year and the large amounts of high quality data have been obtained. The paper aims at studying the dynamic behaviour of the wooden building in seismic and operational conditions using the field monitoring data. Specifically the effects of earthquake and crowd loading on the structure's dynamic response are investigated. The monitoring data are decomposed into principal components using the Singular Spectrum Analysis (SSA) technique. The relationship between the average acceleration amplitude and frequencies of the principle components and operational conditions has been discussed. One main contribution is to understand the health condition of complex ancient building based on large databases collected on the field.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.3
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• pp.136-150
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• 2008

Many studies have been carried out for automatic extraction of building by LiDAR data or airphoto. Combining the benefits of 3D location information data and shape information data of image can improve the accuracy. So, in this research building recognition algorithm based on contour was used to improve accuracy of building recognition by LiDAR data and elaborate building boundary recognition by airphoto. Building recognition algorithm based on contour can generate building boundary and roof structure information. Also it shows better accuracy of building detection than the existing recognition methods based on TIN or NDSM. Out of creating buffers in regular size on the building boundary which is presumed by contour, this research limits the boundary area of airphoto and elaborate building boundary to fit into edge of airphoto by double active contour. From the result of this research, 3D building boundary will be able to be detected by optimal matching on the constant range of extracted boundary in the future.

• Journal of the Korean Solar Energy Society
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• v.29 no.3
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• pp.66-72
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• 2009

As 'Low Carbon Green Building' is highly required, programs to evaluate building performance are actively and commonly used. For most of these programs, dynamic responses of buildings against external weather changes are very important. In order to simulate the programs, weather data of each region must be properly entered to estimate accurate amount of building energy consumption. To this end, the existing weather data and weather data of KSES were compared and analyzed to find out how weather changes. Energy load of Korea's standard houses was also analyzed based on this data. As a result, data corresponding to June ${\sim}$ September when cooling is supplied shows 23% of average increase with 30% of peak increase(June). On the other hand, data corresponding to November ${\sim}$ February when heating is supplied shows 29% of average decrease with 34% of peak decrease(November). Increase in cooling load and decrease in heating load in the above data comparison/analysis show that KSES 2009 data reflects increase in average temperature caused by global warming unlike the existing data. Increase in dry-bulb temperature depending on weather change of standard houses increases cooling load by 17% and decreases heating load by 36%