• Architectural research
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• 제20권3호
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• pp.93-102
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• 2018

With the change in Earth's ecosystems due to climate change, a number of studies on zero energy buildings have been conducted globally, due to the depletion of energy and resources. However, most studies have concentrated on residential and office buildings and the performance predictions were made only in the design phase. This study verifies the zero-energy performance in the operational phase by acquiring and analyzing data after the completion of an exhibition building. This building was a retention building, in which a renewable energy system using a passive house building envelope, solar photovoltaic power generation panels, as well as fuel cells were adopted to minimize the maintenance cost for future energy-zero operations. In addition, the energy performance of the building was predicted through prior simulations, and this was compared with actual measured values to evaluate the energy performance of the actual operational records quantitatively. The energy independence rate during the measurement period of the target building was 123% and the carbon reduction due to the energy production on the site was 408.07 tons. The carbon reduction exceeded the carbon emission (331.5 tons), which verified the carbon zero and zero-energy performances.

• 설비공학논문집
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• 제12권12호
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• pp.1090-1097
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• 2000

Predicting building energy use can be useful to evaluate its energy performance. This study proposed empirical approach for predicting building energy use with regression analysis. For the empirical analysis, simple regression models were developed based on the historical energy consumption data as a function of daily outside temperature, the predicting equations were derived for different operational modes and day types, then the equations were applied for predicting energy use in a building. BY selecting a real building as a case study, the feasibilities of the empirical approach for predicting building energy use were examined. The results showed that empirical approach with regression analysis was fairly reliable by demonstrating prediction accuracy of $pm10%$ compared with the actual energy consumption data. It was also verified that the prediction by regression models could be simple and fairly accurate. Thus, it is anticipated that the empirical approach will be useful and reliable tool for many purposes: retrofit savings analysis by estimating energy usage in an existing building or the diagnosis of the building operational problems with real time analysis.

• 설비공학논문집
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• 제20권3호
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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.

• 설비공학논문집
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• 제26권5호
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• pp.231-239
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• 2014

This paper represents a method of existing building energy simulation using energy audit data. Energy audit must be carried out for reasonable analysis, because characteristics of existing buildings such as efficiency of fan, pump, flow rate, pressure, COP and operating schedule could be changed during the building operation. These building characteristics should be measured to estimate actual energy consumption of the existing building. In this study, we conducted energy audit and calculated energy savings for a 7-stories building as a case-study. The energy audit data were used to calibrate the building model of EnergyPlus simulation. Baseline model validated according to M&V guideline index. As a result, building characteristics are significant parameters making a big impact on energy savings in existing buildings.

• 한국태양에너지학회 논문집
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• 제28권2호
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• pp.10-18
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• 2008

This study aims to figure out typical meteorological data according to Korean time in order to evaluate building energy performance. Various methods of calculating typical meteorological data were compared and examined to improve accuracy and reliability of this study. This study analyzed and examined such methodologies as typical meteorological data for HASP/ACLD-8001, UK CIBSE TRY developed by CIBSE and prEN ISO 15927-4, (=ISO TRY) an international standard to evaluate annual energy demand of cooling and heating devices. In addition, actual data of KMA corresponding to Seoul in $1985{\sim}2005$ were statistically analyzed according to calculation methodology. The calculated typical meteorological data were compared te actual data using MBE, RMSE and t-Statistic. As a result, According to the comparison between average annual for HASP/ACLD-8001 and ISO TRY standard year, the average annual for HASP/ACLD-8001 is closer to actual measurement, showing that the use of typical meteorological data for HASP/ACLD-8001 is preferred. However, since the input format requested by current simulation is the same international standard as TRY. Therefore, it is necessary to improve accuracy of TRY calculation methodology and accordingly figure out Korean typical meteorological data based on average year.

• 한국태양에너지학회 논문집
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• 제30권6호
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• pp.89-94
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• 2010

A large portion of the energy cost of a building is cooling and heating to maintain a comfortable indoor environment. Air conditioning is now one of the important parts in the building design, as increase in energy consumption and pollutant emission in energy conversion process. In this study, elements that affects the energy consumption of model building are identified and the perfomance analysis of the alternative a Low Energy Cooling Systems considering characteristics of model building and energy saving performance is analyzed. In this study, elements that affect the energy consumption of office building are identified and energy saving performance of the alternative air conditioning system is analyzed. As a result, applied to earn and suggest basic data for energy saving measures. In this study, EnergyPlus simulation program was used to evaluate the energy load when alternative Low Energy Cooling Systems are applied to the model building. The reliability of simulation program is verified by comparing actual energy load from operation data of building management office and predicted energy load using simulation program. For Low Energy Cooling System application which considers the purpose and characteristics of the building, reasonable and energy-saving air conditioning method obtained by analyzing energy consumption elements for each expected air conditioning methods is used to deduct result of this study.

• 한국건축친환경설비학회 논문집
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• 제12권6호
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• pp.627-640
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• 2018

Submetering electricity consumption data enables more detail input of end use components, such as lighting, plug, HVAC, and occupancy in building energy modeling. However, such an modeling efforts and results are rarely tried and published in terms of the estimation accuracy of electricity demand. In this research, actual submetering data obtained from a university building is analyzed and provided for building energy modeling practice. As alternative modeling cases, conventional modeling method (Case-1), using reference schedule per building usage, and main metering data based modeling method (Case-2) are established. Detail efforts are added to derive prototypical schedules from the metered data by introducing variability index. The simulation results revealed that Case-1 showed the largest error as we can expect. And Case-2 showed comparative error relative to Case-3 in terms of total electricity estimation. But Case-2 showed about two times larger error in CV (RMSE) in lighting energy demand due to lack of End Use consumption information.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2006년도 하계학술발표대회 논문집
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• pp.903-908
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• 2006

Building energy simulation has become a useful tool for predicting cooling, heating and air-conditioning loads for facilities. It is important to provide building energy performances feed back to the mechanical and electrical system operator and engineer for energy conservation and maintenance of building. From this research, we set up the typical weather data of location, basic description of building, geometric modelling data and the specification of Installed primary HVAC system for establishing the simulation model about energy consuming that take place in multipurpose building complex. The simulation tool of building energy - EnergyPlus (DOE and BLAST based simulation S/W), it has been used and accomplished calculations and analyses for evaluating the effect of the system types and operating condition of central HVAC plant on the building energy consumption. In this paper, we offer comparison and simultaneous results those involve electricity consumption pattern and amount between actual operation versus EnergyPlus simulation to the object building during summer season.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.723-727
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• 2015

Building energy use estimation relies on building characteristics, its energy systems, occupants, and weather. Energy estimation of new buildings is considerably an easy task when compared to modeling existing buildings as they require calibration with actual data. Particularly, when energy estimation of existing building stock is warranted at a city-scale, the problem is exacerbated owing to lack of construction drawings and other engineering specifications. However, as collection of buildings and other infrastructure constitute cities, such predictions are a necessary component of developing and maintaining sustainable cities. This paper uses Artificial Neural Network techniques to predict electricity consumption for residential buildings situated in the City of Gainesville, Florida. With the use of 32,813 samples of data vectors that comprise of building floor area, built year, number of stories, and range of monthly energy consumption, this paper extends the prediction to environmental impact assessment of electricity usage at the urban-scale. Among others, one of the applications of the proposed model discussed in this paper is the study of urban scale Life Cycle Assessment, and other decisions related to creating sustainable cities.

• Journal of Construction Engineering and Project Management
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• 제5권4호
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• pp.16-22
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. Several case studies were conducted to experimentally evaluate their impact on BIM-based energy analysis to calculate energy load. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.