A feasibility study on a building's window system based on dye-sensitized solar cells

Energy and Buildings 81 (2014) 38–47

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A feasibility study on a building’s window system based on dye-sensitized solar cells Jae Wook Lee a , Jiyoung Park b,∗ , Hyung-Jo Jung c,∗ a b c

Building Works Design Group, Hyundai E&C Co. Ltd., Seoul 110-920, Republic of Korea Department of Architecture, Inha University, Incheon 402-751, Republic of Korea Department of Civil and Environmental Engineering, KAIST, Daejeon 305-701, Republic of Korea

a r t i c l e

i n f o

Article history: Received 1 August 2013 Received in revised form 22 May 2014 Accepted 6 June 2014 Available online 13 June 2014 Keywords: Semi-transparent photovoltaic DSSC (dye-sensitized solar cell) Window Climate Building energy

a b s t r a c t This study assessed the applicability of semi-transparent photovoltaics (PVs) that can both produce electricity and transmit light in the window system of a building. The potential of window-integrated semi-transparent photovoltaics (WISPVs) in the current global climate was evaluated by varying the window performance and the conversion efficiency of dye-sensitized solar cells (DSSCs). The feasibility of WISPVs was examined quantitatively based on the standard building envelope properties from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) and the reported DSSC conversion efficiency. The following seven cities, which are representative of what can typically be found in various climate zones, were selected for the building energy simulation: Miami (Zone 1), Sao Paulo (Zone 2), Sydney (Zone 3), New York (Zone 4), Seoul (Zone 4), Berlin (Zone 5), and Moscow (Zone 6). According to the simulation results from the ESP-r program, the WISPVs were more effective in Zone 4, but less effective in Zones 5 and 6. © 2014 Elsevier B.V. All rights reserved.

1. Introduction According to the life cycle cost (LCC) of a building’s energy consumption, the maintenance phase comprises approximately 83% of the total energy consumption with construction (16%), planning and design (0.4%) and disposal (0.4%) comprising only small fraction [1,2]. A building’s heating, cooling and lighting consumes approximately 80–90% of the maintenance phase, accounting for more than half of the energy consumption of a building [3,4]. To effectively reduce the maintenance costs of a building, it is important to consider the energy efficiency of a structure during the initial building design phase by conducting a simulation of the performance. The overall energy consumption of a building can be reduced in two leading ways. In the first, the maintenance cost in building operations can be optimized because its energy requirements are affected mainly by the envelope properties, inter alia and window system. Therefore, varying the window performance properties and the position of window installation should be considered quantitatively. The second way involves the application of renewable

∗ Corresponding authors at: KAIST, Civil and Environmental Engineering, Daejeon 305-701, Republic of Korea. Tel.: +82 42 3503626. E-mail addresses: [email protected] (J.W. Lee), [email protected] (J. Park), [email protected] (H.-J. Jung). http://dx.doi.org/10.1016/j.enbuild.2014.06.010 0378-7788/© 2014 Elsevier B.V. All rights reserved.

energy to the building envelope, particularly PV technology. As the next generation solar cells, organic-based photovoltaics, which can be transparent to light, have been studied extensively as an alternative window system in recent years [5–9]. The application of semi-transparent photovoltaics to the building envelope can generate photovoltaic electric energy, and adjust the heating, cooling and lighting loads. A precise evaluation of window-integrated semi-transparent photovoltaics (WISPVs) using these factors is needed. In current WISPV technology, a range of photovoltaic systems have been considered, such as amorphous silicon (a-Si) thin films, polycrystalline or monocrystalline Si, dye-sensitized solar cells (DSSCs) and polymer solar cells [10]. The development of WISPVs has become more feasible since the advent of organic-based photovoltaics (OPVs) including DSSCs, which are relatively cheap, flexible and can be made semi-transparent [11]. OPVs, however, typically show low energy conversion efficiency. Therefore, previous studies of OPV focused mainly on improving the efficiency [12]. Accordingly, research on WISPVs has focused on fully utilizing the maximum conversion efficiency using a window design strategy [13]. On the other hand, a range of window system properties need to be studied before WISPVs can be used practically, including the window wall ratio (WWR), solar heat gain coefficient (SHGC), solar transmittance (Tsol ), visible transmittance (Tvis ), light-to-solar-gain ratio (LSG), and the U-value under local or global climate conditions.

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6.1m

4.57m

3.05m 25 50 75 100 (%), WWR Fig. 1. Three-dimensional view of the prototypical building module.

Based on this research background, two main objectives were addressed: (1) an evaluation of the window performance properties, such as WWR, SHGC, Tvis , Tsol , LSG, and U-value, by varying the climate conditions in a DSSC-applied window system; and (2) a feasibility study of DSSCs in a building window system regarding the generation and consumption of energy by a building.

2. Methodology Three main factors were chosen as the input data: the conversion efficiency of DSSCs, climate conditions and window properties. The building energy consumption consisting of four elements (heating, cooling, lighting, and photovoltaic energy generation) was used as the output data. The analysis was performed in the following five steps: set the building module and envelope properties for input data; classify the 6 types of the world climate; select 4 types of DSSCs and apply them to the window system; perform data analysis based on the overall energy consumption related to the variations in the conditions; and (5) suggest guidelines for the DSSC window system in the given climates. (1) (2) (3) (4)

2.1. Simulation tool and input data The ESP-r simulation program was used to evaluate the appropriate performance criteria for a window system in buildings [14,15]. This software package is recognized and used widely in more than 60 countries as an industry standard for the simulations. The authors employed the latest version of ESP-r 11.1 (updated in 2011), which considers the energy use of heating, cooling and lighting, peak energy load demand, and the occupant’s thermal comfort in buildings. The simulated building [16,17] was comprised of 4 perimeter zones containing 5 office modules each with each office module consisting of a zone, 4.57 m (15 ft) in depth by 6.1 m (20 ft) in width, with a floor-to-floor height of 3.05 m (10 ft). The office module is shown in Fig. 1. The window frame was made of 57.2 mm aluminum with a thermal break that has a U-factor of 5.68 W/m2 K. The lighting and equipment loads were 10.76 W/m2 and 8.07 W/m2 , respectively. The U-factor data of the other envelopes, such as the wall (0.26–0.39 W/m2 K), floor and roof, was inserted based on level of climates addressed in the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Standard [18]. The glazing system was defined parametrically to better understand their effects on the energy performance. The glazing area is meant to be installed by WWRs corresponding to 25, 50, 75 and 100% of the wall area.

Fig. 2. Plan view of the prototypical building module. Table 1 Combination performance properties regarding window system data. Window

SHGC

Tvis

LSG

U-factor (W/m2 K)

NFRC ID

A B C D

0.20 0.19 0.17 0.15

0.39 0.31 0.24 0.20

1.95 1.63 1.41 1.33

0.81 0.83 0.79 0.84

8669 5208 5204 5772

2.2. Dye-sensitized solar cell data In previous research, the properties of DSSCs used in the simulations were obtained from the laboratory-scale fabrication of four DSSCs and corresponding measurements [13]. By adopting the previous DSSC systems in this study, the transmittance of the four DSSCs were 45, 38, 30 and 25%, and the conversion efficiencies were 10.26, 11.50, 12.60 and 13.00%, respectively. The solar transmittance data was selected from Fig. 2 based on the individual visible transmittance. A search of all the available window systems in the National Fenestration Rating Council (NFRC) database was performed (Fig. 2) [19], and their visible transmittance and solar transmittance properties were plotted. In the simulation, the solar transmittance was chosen from the median value for the given visible transmittance. Fig. 2 shows four types of DSSC glazing according to the data marked ‘A’ to ‘D’. 2.3. Optical properties for the type of window system Using the four selected types of single DSSC glazing, the ‘A’ to ‘D’ window systems showed similar features in a center-of-glass U-factor of 1.49 to 1.53 W/m2 K, a target that can be achieved with two layers of the glass system and an argon fill. The window system has a combination of selected DSSC glazing located on the outside, and low-e glasses located inside, as shown in the inset in Fig. 2. The properties of low-e glass installed in the window system have the following values: Tsol = 0.706, Tvis = 0.87 and filled with argon gas to a thickness of 12.7 mm. The combination performance properties regarding the window system data used for the simulation were selected from the NFRC database, and are listed in Table 1. 2.4. Climate data The climate data was selected from ASHRAE and Energy Efficiency & Renewable Energy (EERE) [18,20]. Each categorized zone was also divided by the thermal criteria, where heating or cooling is used. According to the climate classification of the ASHRAE standard, which ranges from Zones 1 to 8, Zones 1 to 6 were selected

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Fig. 3. Input variables as the glazing performance properties.

Table 2 Basic information of the seven climates; climate classification, location, HDD and CDD. Miami ASHRAE climate Longitude (◦ ) Latitude (◦ ) HDD18 (◦ C) CDD18 (◦ C) HDD10 (◦ C) CDD10 (◦ C) Direct solar radiation (kW h/m2 yr) Diffusive solar radiation (kW h/m2 yr) a

a

1A −80.43 25.65 126 2025 0 4819 50.1 24.4

Sao Paulo

Sydney

New York

Seoul

Berlin

Moscow

2A −46.65 −23.62 252 939 0 3607 38.2 27.2

3A 151.18 −33.95 687 634 5 2871 47.7 23.5

4A −73.97 40.78 2675 661 1114 2020 43.5 23.1

4A 126.55 37.48 2782 560 1223 1920 25.0 23.3

5C 13.40 52.47 3156 170 1191 1125 23.0 19.1

6A 37.63 55.75 4655 99 2498 862 22.4 20.1

(A) moist, (B) dry, (C) marine.

for the global climate distribution in this study. Zones 7 (very cold) and 8 (subarctic) were excluded because of the extreme weather conditions. The following seven prototype cities of the climate zones were selected for the building energy simulation; Miami (Zone 1), Sao Paulo (Zone 2), Sydney (Zone 3), New York (Zone 4), Seoul (Zone 4), Berlin (Zone 5) and Moscow (Zone 6). Fig. 3 and Table 2 provide basic information of the region, cooling and heating degree days. These seven cities are representative of each climate Zone from 1 to 6, and can cover most of the climate ranges on the planet. In Zone 4, New York and Seoul reflect the areas with the most diverse global climate. In terms of the annual cooling and heating degree-days (CDD and HDD), cooling is dominant in Miami, which is ASHRAE numbered 1, and heating is dominant in Moscow, which is numbered 6. In Fig. 4, where the direct normal solar radiation values are represented for the cities, the selected cities in each zone (black bar) have the average value among the cities in the same zone. Miami has on average more than 50 kW h/m2 direct solar radiation per year, whereas the radiation in all other locations is considerably lower than 50 kW h/m2 . Generally, the average direct solar radiation value decreases from ASHARE Zones 1 to 6. 2.5. Calculation assumptions For the building energy simulation, Tables 3–5 and Fig. 5 present the calculation assumptions on the heating and cooling load gain, infiltration, occupancy, lighting and equipment schedules [21–23]. A space of 37.16 m2 (400 ft2 ) is available for each person in the

building under consideration. Based on this, the internal loads for the equipment (PC, printer etc.) were calculated to be 8.07 W/m2 peak load. The sensible load per persons was 297 W, which is 8 W sensible/m2 . The offices have a heating set point of 21 ◦ C and a cooling set point of 24 ◦ C. During the night and on weekends, the Table 3 Set point temperature for cooling (◦ C). Time

Weekday

Saturday

Sunday

06:00–18:00 18:00–22:00 22:00–06:00

24 24 27

24 27 27

27 27 27

Table 4 Set point temperature for heating (◦ C). Time

Weekday

Saturday

Sunday

06:00–18:00 18:00–22:00 22:00–06:00

21 21 16

21 16 16

16 16 16

Table 5 Infiltration schedules (at 0 Pa). Time

Weekday

Saturday

Sunday

06:00–18:00 18:00–22:00 22:00–06:00

0.25 0.25 1

0.25 1 1

1 1 1

Direct Solar Radiation (kWh/m2-yr) 70

60

50

40

30

20

10

0 Hanoi Mombasa ASHRAE #1

Miami

Rio de Janeiro Honolulu New Delhi

Lima

San Paulo Brisbane

Shanghai

Valencia

San Francisco

Paris London ASHRAE #4

Seoul Rome New York Madrid

Fig. 4. Case study locations.

Sydney Buenos Aires

J.W. Lee et al. / Energy and Buildings 81 (2014) 38–47

Cairo

Mexico city ASHRAE #3

Fig. 5. Annual direct solar radiation in the global regions.

ASHRAE #2

Taipei

Santiago

Prague Sapporo ASHRAE #5

Amsterdam Berlin Vancouver

Boston

ASHRAE #6

Oslo Moscow

Stockholm Toronto

41

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1

0.9 0.8 0.7 Occ. (week)

Frequency

0.6

Occ. (sat) Occ. (sun)

0.5

Light (week) Light (sat)

0.4

Light (sun)

Equip. (week) 0.3

Equip. (sat) Eqip. (sun)

0.2 0.1 0 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

Time Fig. 6. Occupancy, lighting and equipment schedules.

Fig. 7. Average performance of the individual window system in the building module.

heating set point was 16 ◦ C and the cooling set point was 27 ◦ C. There is no humidification or dehumidification. The air change rate in the offices on working days from 06:00 to 22:00 is 0.25 AC/h and the other setting AC/h is shown in Table 5. As a lighting control, the calculation data was set with a constant lighting level of 538 lx (50 foot-candles) by continuous dimming. 3. Results and discussion The simulations were conducted for the seven selected cities with various WWRs, orientations and four glass types. Table 6 lists the detailed variations and output results for the simulation. In total, 448 simulations of the building energy was performed to model 4 types of DSSC windows, 4 types of window wall ratios, 4 directions of orientations, and 7 cities that cover 6 types of climates. The graphs of each effect factor, which are the total energy, WWR, orientation, SHCG, Tvis , and conversion efficiency of DSSC, are drawn examining each city. Of the total energy consumption, which consisted of heating, cooling, lighting and photovoltaic energy generation data, the top 3 minimum energy consumption

Table 6 Variables and outputs of the simulation. Variables

Outputs

DSSC selection

Four types by Tvis and Tsol change 100/75/50/25 (%) S/N/E/W Miami/Sao Paulo/Sydney/New York/Seoul/Berlin/Moscow

WWR Orientation Cities

(+) Heating (+) Cooling (+) Lighting (−) Photovoltaic generation

window systems were plotted based on the figure, and the design guidelines of the potential of window-integrated semi-transparent photovoltaics (WISPVs) in the current global climate. 3.1. Statistical analysis of the total energy consumption Fig. 6 shows the average performance of the individual window systems in the seven climates tested. Generally, from ASHRAE climates no. 1 to 6, the overall cooling energy consumption decreases,

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Fig. 8. Effect of an orientation change on the building energy consumption in the seven climate conditions.

whereas the heating energy increases. On the other hand, the lighting energy load shows the lowest change among the building energy measurements. In Miami and Moscow, which are considered ASHRAE Zones 1 and 6, the total energy consumption was considerable due to the effects of the extremely hot or cold climates. The total energy consumption in Seoul and New York of Zone 4 were similar, whereas the ratio between the cooling and heating load were different. 3.2. Orientation effect The energy consumption of a building varies with the orientations of the window system, as shown in Fig. 7. In coolingdominated cities, such as Miami, Sao Paulo and Sydney, the North face (or South face in the Southern hemisphere) is the obviously efficient mounting direction of a window system for energy savings.

Owing to the gains in solar heating, which increases the cooling load, the north face allows for a relatively low and apparently beneficial gain in solar heat. On the other hand, in cities, such as New York, Seoul, Berlin and Moscow, where both heating and cooling are comparable, installing the window in the South can reduce the total energy load because the solar heat gain has a positive effect by increasing the indoor temperature in the heating seasons. In addition, the appropriate orientation of the window face is critical because significant savings in the energy consumption rate can be achieved; 15% in Berlin or 29% in Sao Paulo. 3.3. Window wall ratio (WWR) effect Choosing the appropriate window size is an important parameter for an energy-efficient window system because the energy generation and consumption rates can be affected by the size of the

Fig. 9. Effect of a WWR change on the building energy consumption in the seven climate conditions.

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90

Cooling Energy Load (kWh/m2-yr)

80 70 60 Miami Sao Paulo

50

Sydney 40

New York Seoul

30

Berlin Moscow

20 10 0 0

0.05

0.1

0.15

0.2

0.25

Solar Aperture (SHGC * WWR) Fig. 10. Expected annual cooling energy usage as a function of the solar aperture.

PV system and window, respectively. Fig. 8 shows the results of a simulation of the energy loads as a function of the WWR from 100% to 25% envelope for each city. Generally, the overall building energy load decreased except in Sao Paulo and Sydney. When the WWR was decreased from 100 to 25%, the heating energy load decreased the most in Berlin (∼58.3%), and the least in Seoul (∼39.4%), and the cooling energy load decreased the most in New York (∼40.4%) and the least in Sao Paulo (∼32.8%). When the WWR was increased from 25 to 100%, the lighting energy load increased the most in Miami (∼43.7%) and the least in Berlin (∼32.5%). The photovoltaic energy generation varies at a constant rate according to the change in WWR. As a building has relatively small windows, the sum of the decreasing heating and cooling energy loads is greater than the lighting energy load. This suggests that the application of a low WWR value is effective in reducing the energy consumption. On the other hand, visible comfort and aesthetic aspects are also important in the design of buildings. Therefore, the optimal balance should be found.

3.4. Effect of SHGC on the heating and cooling load Fig. 9 shows the incremental annual cooling energy consumption as a function of the solar aperture. The total building energy load for cooling also increased with increasing SHGC to WWR ratio. The cooling load reduction ratio was dominant in Miami (Zone 1), and the ratio tended to decrease from Zones 1 to 6. The solar aperture and cooling energy load showed a linear relationship with a slope of 169 in Miami and 51 in Moscow. In the heating energy load, there was no general correlation between the solar aperture and heating energy load. On the other hand, there was a linear relationship in each WWR case. Fig. 10 shows that a small WWR value is important for reducing the heating energy consumption and a small solar aperture affects the growth of the heating energy load. In view of leverage in heating energy, WWR greatly affects the heating energy consumption more than the SHGC value.

Fig. 11. Expected annual heating energy usage as a function of the solar aperture.

J.W. Lee et al. / Energy and Buildings 81 (2014) 38–47

45

30

Miami Sao Paulo

28

New York Seoul

24

Berlin 22

Moscow

20 18 16 14 12 10 0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Effective Aperture (Tvis * WWR) Fig. 12. Expected annual lighting energy usage as a function of the effective aperture.

45 40 35 New Delhi

30

Miami 25

SanPaulo Sydney

20

New York 15

Seoul Berlin

10

Moscow 5 0 0

10

20

30 40 50 Direct Solar Radiation (kWh/m2-yr)

60

70

Fig. 13. Expected annual photovoltaic energy as a function of direct solar radiation.

15 Annual Photovoltaic Energy Generation (kWh/m2-yr)

Annual Energy Generation (kWh/m2-yr)

Lighting Energy Load (kWh/m2-yr)

Sydney 26

13

11

Miami (South) San Paulo (North) Sydney (North)

9

New York (South) Seoul (South)

7

Berlin (South) Moscow (South) 5

3 9.5

10

10.5

11 11.5 12 Conversion Efficiency (%)

12.5

13

13.5

Fig. 14. Expected annual photovoltaic energy in the specific elevation as a function of the conversion efficiency.

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Fig. 15. Chart of the top three DSSC windows in annual heating, cooling, lighting and photovoltaic generation source energy as a function of the window system variable and seven cities’ climate in passive regulation.

3.5. Effect of Tvis on lighting energy load Fig. 11 shows the incremental annual lighting energy consumption as a function of the effective aperture (Tvis × WWR). Generally, the total building energy load for lighting decreases with increasing value of the effective aperture. The change in the lighting load by the change in effective aperture had a maximum and minimum value in Miami and Moscow, respectively. The influential factors affecting the lighting energy load include solar radiation, visible transmittance, latitude, and fenestration system size. Among these values, the visible transmittance and WWR are complementary, and the tangent line of the graph (Fig. 11) decreases gradually. To minimize the lighting energy load, the effective aperture needs to be increased and the correlation of its components, which are the visible transmittance and WWR, also need to be evaluated. 3.6. Effect of the photovoltaic energy generation efficiency According to the experimental data of the conversion efficiency, the total annual energy generation of each envelope showed a linear correlation between the direct solar radiation and annual energy generation. In Fig. 12, the best performance elevation was drawn by varying the conversion efficiency. Generally, the regression data follows the results shown in Fig. 13. Each elevation increased linearly with an improvement in conversion efficiency. Some contributory factors, such as the ambient temperature and diffused solar radiation of different orders, exist in the PV conversion efficiency and energy generation figures. Because the ambient temperature can have inverse relationships with the PV conversion efficiency or the energy generation [24], the mock-up scale test for variant weather conditions needs to be performed to produce precise simulation data. 3.7. Design guidelines for semi-transparent window selection in buildings The total energy load was evaluated by selecting a WISPV system and design. Fig. 14 shows the top three performers in each window system according to the climate. The following design guidelines

were suggested based on the simulation results. Although these guidelines have a limitation in aesthetic and psychological aspects because they focus on the energy consumption and generation point, the guidelines can help assess the energy optimization part in the total design evaluation phase (Fig. 15). (1) In all the cities except for Berlin and Moscow, which have colder climates, the relative window size in the building envelope with a higher WWR has an advantage. A comparison with the general evaluation of the WWR effect on the building energy load [17] revealed the optimal WWR value to have an opposite trend. In the case of the energy load, a small WWR appears to be profitable. On the other hand, considering energy generation, WISPVs can be applied widely not only for their generation ability but also to adjust the heating, cooling, and lighting energy load because of their transparency. (2) Regarding the mount positioning of the window, the highest solar radiation can be received in all the cities examined with a south facing window (or North facing in the Southern hemisphere), which has an advantage for total energy savings. (3) A comparison of the DSSC window types in Miami, Sao Paulo, Sydney and New York showed that the ‘D’ type of DSSC windows have the best efficiency. A ‘D’ type DSSC has a relatively high conversion efficiency but a low visible transmittance and SHGC value. (4) Photovoltaic energy generation can cover at least 12.0% in Miami, and at most 15.9% in Seoul and 21.9% in New York of the total energy load, whereas Berlin and Moscow cover a small energy load. (5) The benefits of installing a DSSC window system can change the ranking of the best energy consumption performance. In Zones 1 to 4, one of the top three effective window systems follows the property that applied the most energy generating WISPV. 4. Conclusions A feasibility simulation that assessed the window system and BIPV system effects on the energy consumption of buildings was developed. This method involved regression analysis of the ESPr computer simulation program. The WISPV system can lead to

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economizing building energy in a range of the global climates. The aim of this study was to confirm the applicability of the WISPV system for energy saving buildings in a range of climates. Based on the passive regulation building envelop properties and the DSSC conversion efficiency, the PV output of a DSSC window system covers the total building energy load. The main findings can be summarized as follows: (a) Applying a WISPV system in a building can change the conventional optimized orientation, WWR and window properties, such as the solar heat gain coefficient and visible transmittance. (b) The WISPV system has a leading advantage in energy saving buildings, mostly in ASHRAE climate Zones 1 to 4, which range from hot to mixed climates. (c) The DSSC energy output comprised 12.0%, 20.5%, 18.9%, 21.9%, and 15.9% of the total building energy consumption in Miami, Sao Paulo, Sydney, New York, and Seoul, respectively, which consisted of heating, cooling and lighting. The climate data needs to be considered when reviewing the applicability of WISPVs in a building system. Direct solar radiation affects the photovoltaic energy generation and the dry-bulb temperature fluctuations, such as the heating degree days and cooling degree days, and needs to be analyzed further. In addition, further research on the degree of clearness in DSSCs will be needed to determine how the transparence of DSSCs affects the energy generation and lighting energy load. Furthermore, considering previous research on PV facade by evaluating energy inputs and outputs through radiation, convection, conduction and power generated [25,6], heats effect increased by the WISPV system itself to the cooling and heating load has to be simulated. In this regard, future studies should focus on the following: (1) laboratory scale test for the WISPV properties, such as visible transparence, U-value, SHGC and conversion efficiency; (2) multi-window application tests (e.g. South and East face application); (3) comparison of the simulation data with laboratory-scale data; and (4) simulation test of a highly transparent WISPV system application. Acknowledgments This study was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Trade, Industry and Energy (no. 20123030020090) and the INHA University Research Fund Grant (INHA-46473-1). References [1] I. Sartori, A.G. Hestnes, Energy use in the life cycle of conventional and lowenergy buildings: a review article, Energy and Buildings 39 (3) (2007) 249–257.

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