Development and Application of the KLT Method for the Energy

Development and Application of the KLT Method for the Energy Performance Evaluation of Non-residential Buildings in the Early Design Stage

Jung-Youb Lee1, Seung-Bok Leigh*2, Taeyeon Kim2 and Sooyoun Cho3 1

LG CNS, Public/SGT Service Unit Smart Service Group Engineering Service Team, South Korea 2 Professor, Department of Architectural Engineering, Yonsei University, South Korea 3 Ph.D. Student, Department of Architectural Engineering, Yonsei University, South Korea

Abstract There is a growing interest in sustainable design in the building industry to reduce energy consumption and minimize adverse environmental impacts of buildings. The strategies for sustainable design are as follows: 1) reducing the size of the building's equipment system and saving energy through an optimal design; 2) maximizing natural energy use through a passive solar heating system; and 3) utilizing an active system through applications of high-performance heating, ventilation, and air conditioning (HVAC) and lighting systems, installation of new and renewable energy facilities, and so on. It is vital to evaluate and compare the energy efficiencies of design alternatives at an early design stage, and hence, to improve the energy performance of the final building, as design elements determined at an early phase in the architectural design process greatly influence the energy performance of the building itself. Further, costs increase over time with the number of design changes made. In the course of this research, the KLT (Korean lighting and thermal energy) method was revised and developed based on the lighting and thermal energy (LT) method, adjusting for South Korea's climate and architectural regulations, which can be used to assess the energy performance of buildings. This study was conducted to determine the process of selecting optimal design alternatives to maximize building energy performance at an early stage in the process. Keywords: sustainable design; early design stage; LT method; non-residential building; energy evaluation tool

1. Introduction 1.1 Purpose of the Study There is a growing interest in sustainable design in the building industry to save energy and to minimize the adverse environmental impacts of buildings. Sustainable design, a method in the building field for realizing sustainable development, refers to building design that minimizes primary energy consumption and environmental load by reducing the energy used by a building's air conditioning, heating, and lighting systems.1) The energy-saving strategy in an effective building design can minimize heat gain in summer and heat loss in winter, and can also dramatically reduce the heating/cooling and lighting energy used through natural lighting. In particular, the design elements determined in the early stage of the building design process, such as the building direction, plan, section, *Contact Author: Seung-Bok Leigh, Professor, Department of Architectural Engineering, Yonsei University, Seoul, South Korea Tel: +82-2-2123-7830 Fax: +82-2-2123-7831 E-mail: [email protected] ( Received April 7, 2014 ; accepted July 3, 2015 )

and facade design, may have the greatest impact on the energy performance of buildings.2-4) A later change in the design elements determined at an early stage in the process can result in great expense. Therefore, it is necessary to evaluate the energy performance of design alternatives and to select the optimum alternative in terms of building energy performance early in the building design process. Computer simulation tools, including EnergyPlus, DOE2, and eQUEST, are widely used for the analysis of energy performance; however, some architects have expressed difficulty in learning such tools. It may be inefficient to apply a computer simulation to calculate energy when specific building types and systems have not yet been determined. Therefore, this study focused on the development and applicability of the KLT (Korean lighting and thermal energy) method, which enables a simple evaluation of the energy performance of design alternatives in an early design stage for non-residential buildings in South Korea. 1.2 Method and Procedure of the Study The KLT method was developed by modifying and adding to the existing LT energy evaluation method. The KLT method is a tool for selecting the optimum design alternative in terms of energy performance

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in the early design stage. The existing LT method is a tool that was developed based on European climate and architectural regulations and on outdated technology. Thus, it is difficult to directly apply the LT method to buildings in South Korea. In this study, inputs were adjusted in line with domestic criteria to conduct EnergyPlus simulations. The results were databased in the form of the KLT curve (the primary energy curve of cooling/heating and lighting energy usage). In addition, the KLT worksheet program was constructed by inserting the existing LT worksheet into an Excel spreadsheet so that energy performance and environmental impacts could be automatically calculated by entering the input parameters, thus avoiding the inconvenience of manual entry required for the existing LT method, and also to make the tool widely available. To verify the accuracy of the new KLT method, a sustainable building designed by P Company, located in Incheon Songdo, was selected as a testbed building. The EnergyPlus simulation results were then compared with the tolerance, and the process of selecting the optimal design alternative for energy performance in the early design stage was examined by applying the KLT method. 2. LT Method 2.1 About the LT Method The LT (lighting and thermal energy) method is an easy and simple tool when compared to complicated energy simulation tools. It was developed in the UK to calculate the annual energy consumption of non-residential buildings based on climate data for northern and southern England. By using the resultant graphs and charts, important parameters applicable to real buildings, such as building form and facade design, are entered in a worksheet. Next, annual energy consumption for cooling, heating, and lighting are calculated through numerical computation. The LT method provides the annual primary energy consumption per unit area for each cardinal direction and ceiling height towards the facade of a building through various LT curve graphs. These graphs also take into consideration various factors including: energy elements for cooling, heating, lighting, and ventilation; heat loss through the building envelope; heat loss through ventilation; and energy factors relating to solar gain and natural lighting. The LT method may be used to evaluate the relative energy performance of the initial building design, including the following scenarios:5-6) 1) Determination of passive zones and non-passive zones Passive zones, which enable natural lighting and air conditioning, are generally designed by estimating their height as twice that of a building's roof. Calculation of the area of the passive zone occurs by direction in the

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following order of priority: south, east/west, and north, with any remaining space considered non-passive zones. The non-passive zone of the top floor is determined separately due to increased effects of daylight solar irradiance and heat transfer through the roof. A bufferadjacent zone is an interior space in close proximity to a buffer space (i.e., atrium, sunspace). Taking the daylight transmission property of the buffer space into consideration, the depth of the buffer-adjacent zone is estimated at roof height. The general estimation uses the LT curve of the east/west direction regardless of its actual direction.5) 2) Regulation of window-to-wall ratio In building design, the window-to-wall ratio, determined by an elevation plan, greatly affects the energy consumption of a building. The LT method uses the window-to-wall ratio in the LT curve as a major variable in energy calculations; thus, the windowto-wall ratio should be regulated for zones in each azimuth. The window-to-wall ratio is determined by dividing the glass area by the surface area.5) 2.2 Limitations of the LT Method The LT method is a tool used to evaluate the relative energy performance of alternatives in the early design stage rather than a tool for calculating the exact energy performance of an actual building. However, the relative analysis of cooling, heating, and lighting energy is possible through the LT method. Thus, the LT method could suggest the relative importance of various parameters that affect energy performance. When the LT method is manually calculated, only a few limited parameters can be entered into the worksheet while many other parameters are only assumed, which ultimately results in a design to reduce the energy consumption. For example, the model applied to the LT method assumes that artificial lighting is switched on only when natural lighting falls below the minimum illumination level. This is based on the assumption that an automatic sensing system of the illumination level is included in the design. Therefore, this model has already taken into account the substantial differences between sustainable building and conventional energy-wasting buildings. Through this process, the potential performance of buildings can also be evaluated. However, the LT method is limited in that it can only be applied to non-residential building types such as certain office buildings, schools, hospitals, and governmental buildings. The method is not suitable for complex or high-rise buildings, and is thus disadvantageous in its limited design parameters and limited availability for design alternatives. If this method is applied to South Korea's nonresidential buildings, it will be restrictively applied to a quantitative evaluation because of the climate data differences between South Korea and the UK.7)

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3. Development of the KLT Method The LT method uses the passive zone area and window-to-wall ratio as key parameters in evaluating the energy performance of a building form and facade design determined in the early design stage, including the local climate, the effect of the adjacent buildings, the level of illumination, and the level of indoor heat gain. Thus, for the development of an LT method suitable to domestic conditions, a database referencing energy consumption should be established through an analysis of the energy simulation in numerous cases. 3.1 Energy Simulation Overview and Base Model Configuration In this study, the EnergyPlus v7.2 tool, developed by the U.S. Environmental Protection Agency, was used for simulation analysis, and Google SketchUp and its plug-in file, Openstudio, were used in energy simulation modeling. The composition of the base model is shown in Fig.1. A medium-sized five-story office building with a 5,400 m2 floor area was used. In separating the energy load between the passive and non-passive zones according to the cardinal direction, one of the stories had a total of seven zones: two passive zones to the south, two passive zones to the west, one passive zone to the east, and one passive zone and one non-passive zone to the north. The story height was set at 3 m. The depth of the passive zone was set at 6 m, twice the story height. Table 1. Model Data Gross floor area Floor area No. of floors Floor-to-ceiling height No. of zones

5400 m2 1080 m2 5 3m 7

Some conditions, including the level of insulation performance and equipment, do not meet the current conditions in South Korea. Thus, the following new settings are needed: 1) Domestic climate regions The climate data (typical meteorological year (TMY2)) of three Korean cities (Seoul, Daejeon, and Busan), representing the central and southern regions, were used. 2) Overall heat transmission coefficient of the wall The overall heat transmission coefficient of the wall was set to 0.27 W/m2·K in accordance with the energysaving building design standards of the Green Building Composition Act. 3) Overall heat transmission coefficient of glass The overall heat transmission coefficient of glass was set to 2.0 W/m2·K in accordance with the energysaving building design standards of the Green Building Composition Act. 4) Illuminance level The illumination was set at three levels (150, 300, and 500 lux), based on the KS Office illumination standards, and the LT method automatically assumes that artificial lighting is switched on only when natural lighting levels fall below the illumination standards level. 5) Indoor heat gain level In accordance with ASHRAE Fundamental 2009, the occupancy density (0.16 person/m2) and equipment load (5 and 10 W/m2) were set. The indoor heat gain was set at two levels (15 and 30 W/m2) by combining the occupation and equipment loads. 6) Schedule The office schedule was used in regard to the occupants, lighting, and equipment used, based on ANSI/ASHRAE Standard 55-2010.

Fig.1. Building Modeling for EnergyPlus Simulation

Case 1 0%

Case 2 20%

Case 3 40%

Case 4 60%

Case 5 80%

Fig.2. Case Set-Up According to the Glazing Ratio

3.2 Setting of Input Conditions that Meet South Korea's Climate The existing LT method was developed based on the climate data of southern and northern England.

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7) Indoor setting temperature The temperature was set to 20°C for heating and 26°C for cooling, so that the HVAC system could run. 8) Miscellaneous The reflectance of the structures was set to 0.7 in the ceiling, 0.5 in the wall, and 0.25 in the floor. The frequency of ventilation was set to once. A 4.5 cooling system coefficient of performance (COP) and 0.8 heating system COP were applied to the equipment system for energy calculation. 3.3 Deduction of the KLT Curve through Simulation Prior to performing the simulation, a total of 18 cases were set, as illustrated in Fig.3., to derive a total of 72

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curves in all directions in each case. Energy simulation for deriving the KLT curve, which represents the primary energy consumption by the glazing ratio, was performed based on the conditions defined in Section 3.2.

insulation performance degradation does greatly increase. As a result, the heating load greatly increases. The horizontal zone has a horizontal rooftop skylight. The solar gain greatly increases through the skylight as the window-to-wall ratio increases. As a result, the indoor area is very likely to become overheated. Table 3. Loads of Passive Zones According to Orientation (Seoul/150 lux/15 W)

Fig.3. Case Set-up for Simulation and Number of Curves Derived from the Simulation

First, the load of each zone was calculated by applying the conditions in Table 2. to an EnergyPlus simulation. The average value was calculated by summing up the loads of each zone by direction. Then the annual load per unit area by energy use was calculated as shown in Table 3.8)

Window ratio (%) 0 20 40 60 80

South zone Cooling load (kWh/m2yr)

Heating load (kWh/m2yr)

Lighting load (kWh/m2yr)

54.78 56.85 65.89 75.32 84.98

35.97 41.03 42.12 43.81 45.97

19.17 4.56 4.28 4.20 4.13

Considering the load value in Table 3. and the conversion factor in Table 4. and the efficiency of the equipment system (4.5 cooling system COP, 0.8 heating system COP), the primary energy consumption was calculated using the following formula:

Table 2. Conditions of EnergyPlus Simulation Criteria Temperature setting Hours of operation People Equipment Illumination level Lighting level Ventilation rate HVAC Weather Orientation U-value Window SHGC Window VLT

Input data Heating: 20°C Cooling: 26°C Week: 08:00-19:00 0.16 people/m2 140 W/person 5, 10W/m2 150, 300, 500 lux 6, 12, 15 W/m2 (dimming control) 1 ACH Ideal load air system Seoul, Daejeon, Busan TMY2 South Wall: 0.27 W/m·K Window: 2.0 W/m·K 0.267 0.69

The energy load pattern change by direction according to the increase of the glazing ratio demonstrates that when the window-to-wall ratio increased by more than 20% in all directions, the lighting load greatly decreased. When considering the south and east zones, the cooling and heating loads were determined to have increased. It is also confirmed that as the window-to-wall ratio increases, natural lighting is used to ensure necessary indoor illumination while the indoor solar gain through glass increases and the insulation performance of the building envelope decreases. In the north zone, even if the window-towall ratio increases, the solar gain does not greatly increase although the heat transmission loss by the

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Table 4. Conversion Factor by Energy Type (Site to Source) conversion factor Energy type (TOE/TOE) Electricity 3.20 Natural gas 1.08 District cooling 0.70 District heating 1.35 Steam 0.25 Gasoline 1.05 Diesel 1.05 Coal 1.05

The KLT curve in Fig.4. illustrates the primary energy calculations dependent on the window-to-wall ratio by energy use. The four curves in each direction consisted of one set. The database was formed by a total of 18 sets according to the region's climate, illumination level, and indoor heat gain. 3.4 Establishment of the KLT Worksheet Program In the existing LT method, all work related to the building energy calculations on the LT worksheet must be handwritten. It was inconvenient for the authors to select a context-sensitive LT curve and to read the values on the curve. Therefore, the method was improved so that the energy consumption and carbon dioxide (CO2) emissions can be automatically calculated on an Excel spreadsheet by entering the input data selectively to address such inconvenience and to popularize the tool. First, to automate the calculation, all curves in the KLT curve database (DB) were expressed by a quartic functional formula regarding the glazing ratio (see Fig.5.). When various parameters, including the

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Fig.4. The KLT Curve DB

Fig.5. KLT Worksheet Program

climate, building type, illumination level, indoor heat gain level, and window-to-wall ratio, are entered into the KLT worksheet program shown in Fig.6., an appropriate functional formula for the automatic calculation of energy consumption and CO2 emission will be determined. Thus, the KLT method can be used relatively easily when the energy performance of several design alternatives is evaluated in the early design stage of non-residential buildings. Furthermore, the relative analysis of the cooling, heating, and lighting energy use of design alternatives is possible through the KLT method. Thus, this may present the relative importance among the various parameters that affect energy performance.

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Fig.6. KLT Curve Function DB

4. Verification of the KLT Method To verify the accuracy of the tool, the energy consumption analysis results were compared using two methods: the KLT method and EnergyPlus, a

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conventional energy simulation tool. 4.1 Selection and Overview of Target Buildings To verify the accuracy of the KLT method, we established two target buildings. The target building in Case 1 is an imaginary 10-story building with an aspect ratio of 1.5:1 with a core space facing north. The core space can be used as a stairwell or elevator shaft, which can be excluded from energy calculations. However, internal natural air-conditioning and lighting can be limitedly applied to this space. In Case 2, the target building is located on the Yonsei University Songdo campus in Yeonsu-gu, Incheon City. It was constructed as a green building demonstration project. The target building consists of offices and apartments, including an atrium between a four-story office and a three-story office above ground. The atrium was generally planned to be a non-air-conditioned space. 4.2 Energy Performance Evaluation through EnergyPlus Simulation To perform EnergyPlus simulation, the target buildings were modeled (see Fig.7.). The simulation conditions were entered as shown in Table 2. (Seoul, equipment 5 W/m2, illumination level 300 lux, lighting level 12 W/m2).

Fig.7. Modeling for EnergyPlus Simulation

atrium. As shown in Fig.8., a monthly load pattern was observed for the two cases by analyzing the simulation. The result of converting the annual energy load into the primary energy consumption is shown in Table 5. Towards this end, the system efficiency applied 0.8 heating system COP and 4.5 cooling system efficiency, and a 3.2 conversion factor was applied for electricity and 1.05 for gas. Table 5. Comparison of Primary Energy Consumption (Case 1) Load Primary energy Criteria (MWh) consumption (MWh) Lighting 96.27 308.06 Heating 230.39 302.38 Cooling 282.84 266.76 (Case 2) Criteria Lighting Heating Cooling

Load (MWh) 29.03 166.37 180.94

Primary energy consumption (MWh) 92.91 218.36 128.67

Looking at energy use in terms of primary energy, in Case 1 lighting is the major source of energy use (308.06 MWh), followed by heating (302.38 MWh) and cooling (266.76 MWh). The confirmation of highenergy consumption by lighting can be ascribed to the core space, which reduces the passive zone for natural lighting, as it was designed with a deep floor plan. For Case 2, the largest use of energy was for heating (218.36 MWh), followed by cooling (128.67 MWh) and lighting (92.91 MW). Energy consumption for lighting was significantly low because the target building had a shallow floor plan utilizing natural lighting, and dimming control was assumed. (Case 1)

(Case 2)

Fig.8. Monthly Load Pattern by Load Type

For the target building in Case 1, the energy was calculated for the energy load (cooling, heating, and lighting) of the internal space excluding the core space in the north. On the other hand, the energy load calculation of the target building in Case 2 was performed for office space including the central

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Fig.9. Passive and Non-Passive Zones by Building Floor

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4.3 Energy Performance Evaluation through the Application of the KLT Method As in EnergyPlus simulation analysis, energy analysis was conducted using the KLT method for both cases. Fig.9. shows the composition of the target building in terms of passive and non-passive zones. For Case 1, all 10 floors have the same height (3 meters) and the same composition in terms of passive and non-passive zones. Additionally, they have a deep floor plan, and therefore the non-passive zone areas are designed to be considerably large. Regarding Case 2, the building has a shallow floor plan, allowing for a smaller area of non-passive zones. Looking at each floor, 1F has a height of 4.2 meters, which is taller than 2~4F (by 2.7 meters), enabling the design of a larger passive zone compared to 2~4F. However, the interior and buffer-adjacent zones are smaller than on 2~4F. I n s u m m a r y, b y u s i n g t h e b u i l d i n g s h a p e information, areas of the passive and non-passive zones were calculated, and the glazing ratio was set by direction, as shown in Table 6. Table 6. (a) Profile of the Office and (b) Set Zone Area and Window-to-wall Ratio Based on the Facade Direction (a) Height East-west North-south Office Floor (m) length (m) length (m) Office (Case 1) 1~10 3 34.5 18.36 Office 1 (Case 2)

1 2~4

4.2 2.7

Office 2 (Case 2)

1 2~3

4.2 2.7

10.2

39.6

Zone area (m2) and window-to-wall ratio (%) East

West

2070 40

741.6 40

741.6 40

446.8 80

631.4 40

816.1 80

Lighting

448.68

70.83

98.71

15.58

45

Heating

310.42

49.01

58.98

9.31

27

Cooling

289.91

45.77

63.78

10.07

28

Total

1049.01

165.61

221.47 34.96

100

(Case 2) Criteria Lighting Heating Cooling Total

North

NonRoof Buffer passive zone

(Case 1) 0 278.1 40 (Case 2) 20.3 43.2 804.9 80 0 80

2502.9 64.8 -

The analysis of the primary energy consumption using the KLT worksheet program is shown in Table 7. Hence, the KLT worksheet program can calculate the primary energy consumption and CO2 emissions to evaluate both the energy and environmental impacts of a building design.

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Net annual primary energy consumption MWh kWh/m2 92.03 32.55 60.26 21.31 137.31 48.56 289.60 102.43

Net annual CO2 emission t kg/m2 % 20.25 7.16 33 11.45 4.05 18 30.21 10.68 49 61.90 21.89 100

4.4 Comparison of Simulation Results To v e r i f y t h e a c c u r a c y o f t h e K LT m e t h o d calculations, the EnergyPlus simulation results for the test target buildings were compared with the results using the KLT method, along with the error range. Table 8. Comparison of Primary Energy Consumption between Simulation Tools (a) Net annual primary Criteria energy consumption (MWh) Error EP simulation KLT method Lighting 308.06 448.68 46% Heating 302.38 310.42 3% Cooling 266.76 289.91 9% Total 848.50 1049.01 24% (b) Criteria

(b) South

Table 7. Comparison of Primary Energy Consumption (Case 1) Net annual primary Net annual CO2 energy consumption emissions Criteria MWh kWh/m2 t kg/m2 %

Lighting Heating Cooling Total

Net annual primary energy consumption (MWh) EP simulation KLT method 92.91 93.03 218.36 224.08 128.67 130.42 439.94 447.53

Error 0% 3% 1% 2%

For Case 1, the lighting, cooling, and heating energy use showed 46% (141 MWh), 3% (8 MWh), and 9% (23 MWh) error, respectively, based on the primary energy. The overall energy consumption showed 24% (200 MWh) error. Due to the deep floor plan of Case 1, with its large non-passive zone that leads to an excessive calculation of energy consumption with the LT method, we observed a large error value for lighting of the nonpassive zone. In general, the LT method estimated the depth of the passive zone with a factor of two compared to the height of a floor. Therefore, 100% artificial lighting must be considered for the remaining interior space, which is higher than twice the floor height. According to the LT method calculation, the EnergyPlus estimate can be excessive when looking at a deep floor plan building without a partitioned internal wall.

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For Case 2, the lighting, cooling, and heating energy use showed 0% (0 MWh), 3% (2 MWh), and 1% (6 MWh) error, respectively, based on the primary energy. The overall energy consumption showed 2% (8 MWh) error. In this case, no considerable error values were obtained for lighting, cooling, and heating energy calculations. Lighting showed almost no error (0%), while Case 1 showed a large error for the same energy use. This can result from the fact that the building has a shallow floor plan with a low non-passive zone area. In conclusion, when evaluating the KLT method in terms of accuracy, while it proved somewhat difficult to assess the quantitative energy performance of buildings, it is deemed appropriate as a tool for analyzing the relative energy performance of different design alternatives according to the building form and facade in the early design stage. 5. Conclusions and Future Research Project The KLT method, which analyzes the consumption of heating, cooling, and lighting energy according to the core orientation, was created by modifying the energy evaluation methodology of the LT method. This is a tool that selects an optimum design alternative of energy performance by evaluating the energy performance of buildings in the early design stage when designing non-residential buildings. The existing LT method is a tool developed based on the European climate and architectural regulations and outdated technology. Thus, the LT method is difficult to apply under Korean conditions. In this study, a new tool was developed by entering the domestic conditions based on the LT method. In this process, EnergyPlus simulation was performed for a total of 90 cases. The results were databased in the form of the KLT curve (the primary energy curve of the use of cooling/heating and lighting energy). In addition, the KLT worksheet program was constructed by applying the existing LT worksheet into Excel so that the energy performance and environmental impact can be automatically rather than manually calculated by entering input parameters. To verify the accuracy of the new KLT method, we established two target buildings. We then compared the EnergyPlus simulation results with the tolerance. As a result, we concluded that the EnergyPlus method had some drawbacks in quantitative analysis of building energy performance. However, the method can be applied for an initial blueprint of buildings and aspect design as an alternative tool for energy performance analysis. This study focused on building the KLT worksheet program that derives a KLT curve of domestic contextsensitive office buildings and automates calculations. Thus, additional research needs to be performed to build the UHF (Urban Horizontal Factor) database suitable for domestic conditions, and to apply the method to educational and governmental buildings

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by changing the criteria, including the occupancy schedule, ventilation frequency, illumination level, and indoor heat gain. Acknowledgements This research was supported by a grant (11 High-tech Urban G03) from the High-tech Urban Development Program funded by the Ministry of Land, Infrastructure, and Transport of the Korean government. References

1) Norbert Lechner, Heating, Cooling, Lighting Sustainable Design Methods for Architects, Wiley, 2008. 2) Jung, J. Prediction Equations for Energy Consumption Through Surveys on Energy Consumption in Apartment Buildings, Journal of Asian Architecture and Building engineering 13(3), pp.657664.2014. 3) Min, C. & Eon, R, Development of System-Integrated Design Prototypes for Zero Emission Buildings, Journal of Asian Architecture and Building Engineering 12(1), pp.133-140.2013. 4) Hae, K. & Eon, R, A Development of Energy Load Prediction Equations for Multi-Residential Buildings in Korea, Journal of Asian Architecture and Building engineering 11(2), pp.383389.2012. 5) Nick Baker, Koen Steemers, Energy and Environment in Architecture: A Technical Design Guide, Taylor & Francis, 2004. 6) Nick Baker, Koen Steemers, LT Method 3.0—a strategic energydesign tool for Southern Europe, Energy and Buildings 23 (1996) pp.251-256. 7) SoHyung Byun, An application of LT method for design-decisions to improve energy performance of non-domestic buildings during the early stage of remodeling process, 2004. 8) EnergyPlus Input Output Reference: Site to Source Energy Conversion Factors, U.S. Department of Energy. 9) D. Woo, Design optimization through Integrated Design Process, 2013.

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