# Determination of Optimum Building Envelope Parameters of a Room concerning Window-to-Wall Ratio, Orientation, Insulation Thickness and Window Type

## Abstract

**:**

## 1. Introduction

_{2}emissions could be reduced when the optimum insulation thickness is applied. Canbolat et al. [9] investigated the optimum insulation thickness and payback period for two different climates. Using the Taguchi method, the importance order of the examined parameters was found, and the heating degree days was found to be the most efficient parameter based on the results. Alsayed and Tayeh [10] analyzed the optimum insulation thickness for Palestinian buildings considering weather data, insulation types, energy prices, and wall construction. Results of the study highlight the influence of degree days base temperature and insulation type on the optimum insulation thickness. Ozel et al. [11] investigated the optimum insulation thickness according to degree days, life cycle cost, and entransy loss methods. The calculations were undertaken for two different insulation materials. Acikkalp and Kandemir [12] presented a technique that combines economic and environmental effects to determine optimum insulation thickness. The proposed method is based on the degree day approach. A case study was carried out for the Bilecik province of Turkey. The optimum insulation thickness values were found to be between 0.13 and 0.47 m, depending on the environmental and economic priority. Barrau et al. [13] calculated the optimum insulation thickness considering different lifetimes of building and insulation materials. Results of the study show that changing the building lifetime from 20 to 50 years increases the optimum insulation thickness. Moreover, changing the optimization criteria from economic to environmental priority highly affects the results. Some researchers also combined life cycle assessment and exergy analysis to find the optimum insulation thickness [14,15,16]. To enhance the accuracy of predictions for the optimal thermal performance of the buildings, effective software programs were recommended to be used [17]. Simulation programs such as EnergyPlus [18,19,20,21,22,23] and TRNSYS [24,25] have been used by some researchers.

## 2. Materials and Methods

#### 2.1. Description of the Reference Zone

^{2}floor area. The windows are considered to be located at the center of each exterior wall of the room. The dimensions of the room are 10 m (l), 10 m (w), 3 m (h). External walls are insulated, and insulation thickness is an investigation parameter. The properties of the building components are presented in Table 1. Three different glazing types (single, double, and triple glazed) were used to determine the optimum one. The heating temperature set-points were assumed to equal to 24 °C for the base case; however, this was also investigated parametrically. Occupant density was considered equivalent to 0.1 occupants/m

^{2}, and specific lighting gains were determined as 10 W/m

^{2}during occupied hours if the total horizontal radiation level was lower than 120 W/m

^{2}. For the base case, the infiltration rate was considered to be 0.2 ACH (Air Change per Hour); this was also investigated parametrically. In Table 1, the thermal properties of the external wall are given. The main design parameters of the zone are shown in Table 2. Three types of windows were investigated: (1) single glazed window, (2) double glazed window, (3) triple glazed window with argon filling. Four different façade orientations were analyzed as (1) south-east, (2) south-west, (3) north-east, (4) north-west.

#### 2.2. Model Design and Calculation Methods

^{3}), ${\mathrm{c}}_{p}$ is the air specific heat (kJ/kg·K), $\mathrm{V}$ is the airflow rate (m

^{3}/s). ${Q}_{sol}$ is the fraction of solar radiation entering a building zone through external windows that transfer as a convective gain to the inside air. ${Q}_{ISH}$ is the absorbed solar radiation on all internal shading devices that is directly transferred to the inside air.

#### 2.3. System Performance Evaluation Parameters

^{3}), x is the insulation thickness (m), and ${A}_{w}$ is the wall area without the glazing (m

^{2}). Karabay and Arıcı [33] obtained manufacturer prices and correlated the cost of the multiple panes. The investment cost of the multi-pane window $\left({C}_{I}\right)$ per unit is given below [33]:

## 3. Analysis

## 4. Results and Discussion

#### 4.1. Orientation

#### 4.2. Window-to-Wall Ratio (WWR)

#### 4.3. Insulation Thickness

#### 4.4. Infiltration Rate

#### 4.5. Room Set-Point Temperature

#### 4.6. Fuel Type

## 5. Conclusions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Akan, A.E. Determination and Modeling of Optimum Insulation Thickness for Thermal Insulation of Buildings in All City Centers of Turkey. Int. J. Thermophys.
**2021**, 42, 49. [Google Scholar] [CrossRef] - Mirabella, N.; Röck, M.; Saade, M.R.M.; Spirinckx, C.; Bosmans, M.; Allacker, K.; Passer, A. Strategies to improve the energy performance of buildings: A review of their life cycle impact. Buildings
**2018**, 8, 105. [Google Scholar] [CrossRef] [Green Version] - Shehadi, M. Energy consumption optimization measures for buildings in the midwest regions of USA. Buildings
**2018**, 8, 170. [Google Scholar] [CrossRef] [Green Version] - Kaynakli, O. Optimum thermal insulation thicknesses and payback periods for building walls in Turkey. J. Therm. Sci. Technol.
**2013**, 33, 45–55. [Google Scholar] - Kaynakli, O. Parametric Investigation of Optimum Thermal Insulation Thickness for External Walls. Energies
**2011**, 4, 913–927. [Google Scholar] [CrossRef] - Kurekci, N.A. Determination of optimum insulation thickness for building walls by using heating and cooling degree-day values of all Turkey ’ s provincial centers. Energy Build.
**2016**, 118, 197–213. [Google Scholar] [CrossRef] - Bektas Ekici, B.; Aytac Gulten, A.; Aksoy, U.T. A study on the optimum insulation thicknesses of various types of external walls with respect to different materials, fuels and climate zones in Turkey. Appl. Energy
**2012**, 92, 211–217. [Google Scholar] [CrossRef] - Yuan, J.; Farnham, C.; Emura, K. Optimum Insulation Thickness for Building Exterior Walls in 32 Regions of China to Save Energy and Reduce CO
_{2}Emissions. Sustainability**2017**, 9, 1711. [Google Scholar] [CrossRef] [Green Version] - Canbolat, A.S.; Bademlioglu, A.H.; Saka, K.; Kaynakli, O. Investigation of parameters affecting the optimum thermal insulation thickness for buildings in hot and cold climates. Therm. Sci.
**2020**, 24, 2891–2903. [Google Scholar] [CrossRef] [Green Version] - Alsayed, M.F.; Tayeh, R.A. Life cycle cost analysis for determining optimal insulation thickness in Palestinian buildings. J. Build. Eng.
**2019**, 22, 101–112. [Google Scholar] [CrossRef] - Özel, G.; Açikkalp, E.; Görgün, B.; Yamik, H.; Caner, N. Optimum insulation thickness determination using the environmental and life cycle cost analyses based entransy approach. Sustain. Energy Technol. Assessments
**2015**, 11, 87–91. [Google Scholar] [CrossRef] - Açıkkalp, E.; Yerel, S. A method for determining optimum insulation thickness: Combined economic and environmental method. Therm. Sci. Eng. Prog.
**2019**, 11, 249–253. [Google Scholar] [CrossRef] - Barrau, J.; Ibañez, M.; Badia, F. Impact of the optimization criteria on the determination of the insulation thickness. Energy Build.
**2014**, 76, 459–469. [Google Scholar] [CrossRef] - Ashouri, M.; Astaraei, F.R.; Ghasempour, R.; Ahmadi, M.H.; Feidt, M. Optimum insulation thickness determination of a building wall using exergetic life cycle assessment. Appl. Therm. Eng.
**2016**, 106, 307–315. [Google Scholar] [CrossRef] - Dombayci, O.A.; Ulu, E.Y.; Guven, S.; Atalay, O.; Ozturk, H.K. Determination Of Optimum Insulation Thickness For Building External Walls With Different Insulation Materials Using Environmental Impact Assessment. Therm. Sci.
**2020**, 24, 303–311. [Google Scholar] [CrossRef] [Green Version] - Keçebaş, A. Determination of optimum insulation thickness in pipe for exergetic life cycle assessment. Energy Convers. Manag.
**2015**, 105, 826–835. [Google Scholar] [CrossRef] - Albatayneh, A. Optimising the parameters of a building envelope in the east mediterranean Saharan, cool climate Zone. Buildings
**2021**, 11, 43. [Google Scholar] [CrossRef] - Hachem-Vermette, C.; MacGregor, A. Energy optimized envelope for cold climate indoor agricultural growing center. Buildings
**2017**, 7, 59. [Google Scholar] [CrossRef] [Green Version] - Kalua, A. Envelope thermal design optimization for urban residential buildings in Malawi. Buildings
**2016**, 6, 13. [Google Scholar] [CrossRef] [Green Version] - Atmaca, A.; Gedik, G.; Wagner, A. Determination of Optimum Envelope of Religious Buildings in Terms of Thermal Comfort and Energy Consumption. Energies
**2021**, 14, 6597. [Google Scholar] [CrossRef] - Xu, X.; Feng, G.; Chi, D.; Liu, M.; Dou, B. Optimization of Performance Parameter Design. Energies
**2020**, 11, 3252. [Google Scholar] [CrossRef] [Green Version] - Chiesa, G.; Acquaviva, A.; Grosso, M.; Bottaccioli, L.; Floridia, M.; Pristeri, E.; Sanna, E.M. Parametric Optimization of Window-to-Wall Ratio for Passive Buildings Adopting A Scripting Methodology to Dynamic-Energy Simulation. Sustainability
**2019**, 11, 3078. [Google Scholar] [CrossRef] [Green Version] - Wang, Z.; Zhao, J. Optimization of Passive Envelop Energy Efficient Measures for Office Buildings in Different Climate Regions of China Based on Modified Sensitivity Analysis. Sustainability
**2018**, 10, 907. [Google Scholar] [CrossRef] [Green Version] - Axaopoulos, I.; Axaopoulos, P.; Gelegenis, J.; Fylladitakis, E.D. Optimum external wall insulation thickness considering the annual CO
_{2}emissions. J. Build. Phys.**2019**, 42, 527–544. [Google Scholar] [CrossRef] - Usman, M.; Frey, G. Multi-Objective Techno-Economic Optimization of Design Parameters for Residential Buildings in Different Climate Zones. Sustainability
**2022**, 14, 65. [Google Scholar] [CrossRef] - Altun, A.F.; Kiliç, M. Influence of window parameters on the thermal performance of office rooms in different climate zones of Turkey. Int. J. Renew. Energy Res.
**2019**, 9, 226–243. [Google Scholar] - Djokovic, J.M.; Bujnak, J.; Hadzima, B.; Pastorek, F.; Dwornicka, R.; Ulewicz, R. Selection of the Optimal Window Type and Orientation for the Two Cities in Serbia and One in Slovakia. Energies
**2022**, 15, 323. [Google Scholar] [CrossRef] - Gasparella, A.; Pernigotto, G.; Cappelletti, F.; Romagnoni, P.; Baggio, P. Analysis and modelling of window and glazing systems energy performance for a well insulated residential building. Energy Build.
**2011**, 43, 1030–1037. [Google Scholar] [CrossRef] - Tsikaloudaki, K.; Laskos, K.; Theodosiou, T.; Bikas, D. Assessing cooling energy performance of windows for office buildings in the Mediterranean zone. Energy Build.
**2012**, 49, 192–199. [Google Scholar] [CrossRef] - Tsikaloudaki, K.; Theodosiou, T.; Laskos, K.; Bikas, D. Assessing cooling energy performance of windows for residential buildings in the Mediterranean zone. Energy Convers. Manag.
**2012**, 64, 335–343. [Google Scholar] [CrossRef] - Kon, O. Calculation of fuel consumption and emissions in buildings based on external walls and windows using economic optimization. J. Fac. Eng. Archit. Gazi Univ.
**2018**, 33, 101–113. [Google Scholar] [CrossRef] - Ozel, M. Influence of glazing area on optimum thickness of insulation for different wall orientations. Appl. Therm. Eng.
**2019**, 147, 770–780. [Google Scholar] [CrossRef] - Karabay, H.; Arici, M. Multiple pane window applications in various climatic regions of Turkey. Energy Build.
**2012**, 45, 67–71. [Google Scholar] [CrossRef] - Derradji, L.; Imessad, K.; Amara, M.; Boudali Errebai, F. A study on residential energy requirement and the effect of the glazing on the optimum insulation thickness. Appl. Therm. Eng.
**2017**, 112, 975–985. [Google Scholar] [CrossRef] - Gelegenis, J.; Axaopoulos, P. A multi-parametric mathematical approach on the selection of optimum insulation thicknesses in buildings. Buildings
**2017**, 7, 15. [Google Scholar] [CrossRef] [Green Version] - Akan, A.P.; Akan, A.E. Modeling of CO
_{2}emissions via optimum insulation thickness of residential buildings. Clean Technol. Environ. Policy**2021**. [Google Scholar] [CrossRef] - Dylewski, R.; Adamczyk, J. Optimum thickness of thermal insulation with both economic and ecological costs of heating and cooling. Energies
**2021**, 14, 3835. [Google Scholar] [CrossRef] - Şencan Şahin, A.; Kovacı, T.; Dikmen, E. Determination and economic analysis of the optimum insulation thickness of building walls, considering annual CO
_{2}emission. Pamukkale Univ. J. Eng. Sci.**2021**, 27, 60–69. [Google Scholar] [CrossRef] - Hamelin, M.C.; Zmeureanu, R. Optimum envelope of a single-family house based on life cycle analysis. Buildings
**2014**, 4, 95–112. [Google Scholar] [CrossRef] - Jaber, S.; Ajib, S. Optimum, technical and energy efficiency design of residential building in Mediterranean region. Energy Build.
**2011**, 43, 1829–1834. [Google Scholar] [CrossRef] - Central Bank of Turkey Central Bank of Turkey. Available online: https://www.tcmb.gov.tr/ (accessed on 14 March 2022).
- Turkish Statistical Institute (TURKSTAT). Available online: https://www.tuik.gov.tr/ (accessed on 14 March 2022).
- Comparison of the unit energy prices in Turkey. Available online: https://www.enerji-dunyasi.com/belge-indir/4/730/yakit-fiyatlari-karsilastirma-tablosu-02-03-2022.xlsx/ (accessed on 14 March 2022).
- Ertürk, M. Optimum insulation thicknesses of pipes with respect to different insulation materials, fuels and climate zones in Turkey. Energy
**2016**, 113, 991–1003. [Google Scholar] [CrossRef] - Ozel, M. Determination of optimum insulation thickness based on cooling transmission load for building walls in a hot climate. Energy Convers. Manag.
**2013**, 66, 106–114. [Google Scholar] [CrossRef] - Çağlayan, S.; Özorhon, B.; Özcan-deniz, G.; Yiğit, S. A life cycle costing approach to determine the optimum insulation thickness of existing buildings. J. Therm. Sci. Technol.
**2020**, 40, 1–14. [Google Scholar] - Altun, A.F.; Kilic, M. Design and performance evaluation based on economics and environmental impact of a PV-wind-diesel and battery standalone power system for various climates in Turkey. Renew. Energy
**2020**, 157, 424–443. [Google Scholar] [CrossRef] - Ozel, M.; Ozel, C. Effect of window-to-wall-area ratio on thermal performance of building wall materials in Elazığ, Turkey. PLoS ONE
**2020**, 15, e0237797. [Google Scholar] [CrossRef]

**Figure 6.**The influence of the window-to-wall ratio on annual heating cost in Istanbul (3 cm insulation, NW oriented).

**Figure 7.**The influence of the window-to-wall ratio on the annual heating cost in Hakkari (3 cm insulation, NW oriented).

**Figure 8.**The influence of the window-to-wall ratio and the glazing type on the net present cost of the system in Istanbul (3 cm insulation, NW oriented).

**Figure 9.**The influence of the window-to-wall ratio and the glazing type on the net present cost of the system in Hakkari (3 cm insulation, NW oriented).

**Figure 10.**The influence of the window-to-wall ratio, orientation, and the glazing type on the net present cost of the system in Istanbul (3 cm insulation).

**Figure 11.**The influence of insulation thickness on the net present cost (40% WWR, NW oriented façade) for the selected locations.

**Figure 14.**Energy savings ratio versus varying insulation thicknesses and glazing types for the designed zone in Hakkari (40% WWR).

**Figure 15.**Energy savings ratio versus varying insulation thicknesses and glazing types for the designed zone in Istanbul (40% WWR).

Material | Thickness (m) | Conductivity (kJ·h^{−1}m^{−1}K^{−1}) | Density (kg·m^{−3}) |
---|---|---|---|

Plaster | 0.020 | 5 | 2000 |

Brick | 0.210 | 3.2 | 1800 |

Plaster | 0.030 | 5 | 2000 |

Insulation | 0.03–0.15 | 0.144 | 40 |

Parameter | Value |
---|---|

Area | 100 m^{2} |

Height | 3 m |

Window Type | Single glazed (5.69 W/m^{2}·K) |

Double glazed (1.1 W/m^{2}·K) | |

Triple glazed (0.61 W/m^{2}·K) | |

Infiltration rate | 0.2–0.4–0.6–0.8–1 ACH |

Orientation | North-east, north-west |

South-east, south-west | |

Heating set-point temperature | 18–20–22–24–26 °C |

Parameter | Value ^{1} |
---|---|

Natural Gas | Unit price: 0.18 $/m^{3} [43] |

LHV: 9.59 kWh/m^{3} | |

$\mathsf{\eta}:98\%$ | |

Coal | Unit price: 0.13 $/m^{3} [43] |

LHV: 5.76 kWh/kg | |

$\mathsf{\eta}:65\%$ [7] | |

Liquid Petrol Gas (LPG) | Unit price: 1.60 $/kg [43] |

LHV: 12.9 kWh/kg | |

$\mathsf{\eta}:90\%$ [7] | |

Electricity | Unit price: 0.15$/kWh [43] |

LHV: 1 kWh/kWh | |

$\mathsf{\eta}:99\%$ | |

Fuel-Oil | Unit price: 1.03 $/kg [43] |

LHV: 11.28 kWh/kg | |

$\mathsf{\eta}:80\%$ [44] | |

Interest rate (i) | 15% |

Inflation rate (g) | 14.6% |

Project lifetime | 20 years [45,46] |

The unit price of the selected insulation material | 100 $/m^{3} [12] |

^{1}TL/USD currency conversion set at 01.03.2022 1 $ = 13.93 TL.

Selected City | Istanbul | Hakkari |
---|---|---|

TS 825 Climate Zone | 2 | 4 |

Latitude | 41°00′ N | 37°44′ N |

Longitude | 28°97′ E | 43°74′ E |

Altitude (Elevation) | 40 m | 1728 m |

HDD (Heating Degree Days) | 1865 | 3470 |

CDD (Cooling Degree Days) | 6 | 18 |

Köppen Classification Major group | C (Mild) | D (Continental) |

Köppen Classification Sub group | Csa | Dsa |

Case | Insulation Thickness (cm) | Glazing Type | Window to Wall Ratio | Orientation | Infiltration Rate | Heating Set-Point | Fuel Type |
---|---|---|---|---|---|---|---|

1 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Natural Gas |

2 | 6 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Natural Gas |

3 | 9 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Natural Gas |

4 | 12 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Natural Gas |

5 | 15 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Natural Gas |

6 | 3 | Single/Double/Triple | 40% | NE | 0.2 ACH | 24 °C | Natural Gas |

7 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Natural Gas |

8 | 3 | Single/Double/Triple | 40% | SE | 0.2 ACH | 24 °C | Natural Gas |

9 | 3 | Single/Double/Triple | 40% | SW | 0.2 ACH | 24 °C | Natural Gas |

10 | 3 | Single/Double/Triple | 20% | NW | 0.2 ACH | 24 °C | Natural Gas |

11 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Natural Gas |

12 | 3 | Single/Double/Triple | 60% | NW | 0.2 ACH | 24 °C | Natural Gas |

13 | 3 | Single/Double/Triple | 80% | NW | 0.2 ACH | 24 °C | Natural Gas |

14 | 3 | Single/Double/Triple | 100% | NW | 0.2 ACH | 24 °C | Natural Gas |

15 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Natural Gas |

16 | 3 | Single/Double/Triple | 40% | NW | 0.4 ACH | 24 °C | Natural Gas |

17 | 3 | Single/Double/Triple | 40% | NW | 0.6 ACH | 24 °C | Natural Gas |

18 | 3 | Single/Double/Triple | 40% | NW | 0.8 ACH | 24 °C | Natural Gas |

19 | 3 | Single/Double/Triple | 40% | NW | 1.0 ACH | 24 °C | Natural Gas |

20 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 18 °C | Natural Gas |

21 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 20 °C | Natural Gas |

22 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 22 °C | Natural Gas |

23 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Natural Gas |

24 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 26 °C | Natural Gas |

25 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Natural Gas |

26 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Fuel-Oil |

27 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Coal |

28 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | LPG |

29 | 3 | Single/Double/Triple | 40% | NW | 0.2 ACH | 24 °C | Electricity |

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**MDPI and ACS Style**

Altun, A.F.
Determination of Optimum Building Envelope Parameters of a Room concerning Window-to-Wall Ratio, Orientation, Insulation Thickness and Window Type. *Buildings* **2022**, *12*, 383.
https://doi.org/10.3390/buildings12030383

**AMA Style**

Altun AF.
Determination of Optimum Building Envelope Parameters of a Room concerning Window-to-Wall Ratio, Orientation, Insulation Thickness and Window Type. *Buildings*. 2022; 12(3):383.
https://doi.org/10.3390/buildings12030383

**Chicago/Turabian Style**

Altun, Ayşe Fidan.
2022. "Determination of Optimum Building Envelope Parameters of a Room concerning Window-to-Wall Ratio, Orientation, Insulation Thickness and Window Type" *Buildings* 12, no. 3: 383.
https://doi.org/10.3390/buildings12030383