# Effect of Architectural Building Design Parameters on Thermal Comfort and Energy Consumption in Higher Education Buildings

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

_{2}emissions [1]. The energy consumption per square metre for buildings needs to be reduced by around 30% by 2030 (compared to 2015) in order for the global climate ambitions set forth in the Paris Agreement to be achieved [2]. According to previous studies [3,4,5], improvement in building performance has a key role to play in reducing energy consumption. It is important that buildings are designed to provide high performance in various scenarios, including a wide variability of thermal occupant behaviour [6]. Building performance can be expressed by different performance indexes, such as energy consumption, occupant satisfaction and indoor environmental quality (IEQ) [7,8]. Providing a comfortable and healthy indoor environment is one of the core functions of building energy systems [9,10]. Improving building performance can not only achieve high energy efficiency but can also improve the level of occupant thermal satisfaction.

## 2. Research Methods

#### 2.1. Simulation Process Using Monte Carlo (MC) Technique

#### 2.2. Prototype Description of the Application of BES Model to Building Design Example and Climate

^{3}, see Figure 2.

- −32.80 latitude and 151.83 longitude.
- Altitude of 33 m above sea level.
- Mean annual minimum and maximum temperatures of 14.3 °C and 21.8 °C.
- Annual mean global radiation of 4.8 kWh/m
^{2}.

#### 2.3. Determination of Input and Output Variables

#### 2.4. Sampling and Assignment of Probability Density Functions

**Table 1.**Statistical output and distribution profiles for normal distribution characteristics of input parameters.

# | Input Parameter (Probability Distribution: Normal) | Input Units | Summary Statistics | ||||||
---|---|---|---|---|---|---|---|---|---|

Min | Max | Mean | Median | Q1 * (25%) | Q3 * (75%) | SD * | |||

1 | Window to wall ratio [11,16,51,56] | % | 5 | 75 | 40 | 40 | 33 | 47 | 10 |

2 | Cooling set-point temperature [16,51] | °C | 19 | 28 | 25 | 25 | 24 | 26 | 2 |

3 | Heating set-point temperature [16,51] | °C | 17 | 23 | 20 | 20 | 19 | 21 | 1 |

4 | Building orientation [11,56] | Angle (°θ ) | 0 (N) * | 315 | 157 | 135 | 45 | 225 | 103 |

5 | Occupancy density [86] | people/m^{2} | 0.1 | 0.5 | 0.3 | 0.3 | 0.3 | 0.3 | 0.05 |

6 | Mech. Vent. rate per area [87] | l/s/m^{2} | 2 | 8 | 5 | 5 | 4 | 6 | 1 |

7 | Thermal mass [16,51] | - | −1 | 1 | −0.001 | 0 | −1 | 1 | 0.8 |

8 | Roof window opens ratio [88,89] | % | 3 | 17 | 10 | 10 | 9 | 11 | 2 |

9 | Infiltration [16,86] | Ac/h | 0.4 | 2 | 1 | 1 | 0.9 | 1 | 0.2 |

**Table 2.**Statistical output and distribution profiles for discrete distribution characteristics of input parameters.

# | Input Parameter (Probability Distribution: Discrete) | Input Details | |
---|---|---|---|

10 | External wall construction [11,16,56,88,91] | Description | Frequency |

- I.
- Brickwork single leaf construction light plaster
| U-value = 1.949 * | 371 | |

R-value = 0.513 * | |||

- II.
- Brick air l/w concrete block & l/w plaster
| U-value = 0.950 | 370 | |

R-value = 1.053 | |||

- III.
- Brick mineral insulation thermolite block & l/w plaster
| U-value = 0.403 | 367 | |

R-value = 2.482 | |||

- IV.
- Brick/block wall (insulated to 1985 regs)
| U-value = 0.351 | 372 | |

R-value = 2.846 | |||

- V.
- Uninsulated lightweight wall (metal clad)
| U-value = 2.767 | 366 | |

R-value = 0.361 | |||

11 | Roof construction [11,56,88,91] | ||

- I.
- Flat roof—25 mm stone chippings on 19 mm asphalt on 40 mm screed
| U-value = 3.439 | 311 | |

R-value = 0.291 | |||

- II.
- Flat roof—19 mm asphalt on 13 mm fibreboard
| U-value = 2.605 | 301 | |

R-value = 0.384 | |||

- III.
- Flat roof—19 mm asphalt on 13 mm screed on 50 mm wood wool slab
| U-value = 1.431 | 308 | |

R-value = 0.699 | |||

- IV.
- Flat roof—6 mm lightweight metallic cladding
| U-value = 6.223 | 308 | |

R-value = 0.161 | |||

- V.
- Flat roof U-value = 0.25 W/m
^{2}K
| U-value = 0.252 | 310 | |

R-value = 3.972 | |||

- VI.
- Green roof construction
| U-value = 0.239 | 308 | |

R-value = 4.192 | |||

12 | Glazing type [11,16,56,88] | ||

- I.
- Sgl Bronze 3 mm
| U-value = 6.257 | 362 | |

SHGC = 0.713 | |||

- II.
- Sgl Ref-C-H Clr 6 mm
| U-value = 5.302 | 378 | |

SHGC = 0.320 | |||

- III.
- Dbl LoE (e3 = 0.1) Clr 3 mm/13 mm Air
| U-value = 2.708 | 365 | |

SHGC = 0.697 | |||

- IV.
- Dbl Bronze 3 mm/6 mm Air
| U-value = 3.226 | 373 | |

SHGC = 0.619 | |||

- V.
- Dbl Clr 6 mm/13 mm Air
| U-value = 1.798 | 368 | |

SHGC = 0.643 | |||

13 | Local shading type [11,86] | 0.5 m projection Louvre | 230 |

1.0 m projection Louvre | 235 | ||

1.5 m projection Louvre | 234 | ||

No shading | 222 | ||

0.5 m Overhang | 235 | ||

1.0 m Overhang | 231 | ||

1.5 m Overhang | 231 | ||

2.0 m Overhang | 228 | ||

14 | Location template [94,95] | Newcastle | 923 |

Sydney | 923 | ||

15 | Crack template (airtightness) [96,97,98] | Excellent | 364 |

Good | 373 | ||

Medium | 373 | ||

Poor | 366 | ||

Very poor | 372 |

^{2}K; R-value = m

^{2}K/W.

## 3. Results and Discussion

#### 3.1. Uncertainty Analysis (UA)

#### 3.2. Sensitivity Analysis (SA)

#### 3.2.1. Influential Factors on Energy Consumption for Each Output

^{2}), location template, local shading type and glazing type did not have a notable influence on total site energy consumption, and therefore, these inputs were ignored in further analysis of total site energy consumption for this model.

#### 3.2.2. Influential Factors on Thermal Comfort for each Output

^{2}) and infiltration (ac/h). Crack template (building airtightness), external wall construction, building rotation, window to wall ratio, local shading type, thermal mass, location template and roof window did not have a notable influence on thermal comfort level and therefore, these inputs were ignored in further analysis of thermal comfort for this model.

#### 3.3. The Effect of ABDPs on Indoor Thermal Environment

#### 3.3.1. Effect of Cooling and Heating Set-Point Temperatures

#### 3.3.2. Effect of Roof and Wall Construction and Thermal Mass

^{2}K to 0.239 W/m

^{2}K. This decrease in the operative temperature could significantly reduce the thermal discomfort hours 3.25 times, thereby reducing energy consumption by 41.0%. The results of [110] show that roof construction types are the key parameter in decreasing or increasing the risk of thermal discomfort hours in tropical climates. Compared to different construction methods and materials, they provided up to 15 times better indoor thermal conditions by decreasing the number of thermal discomfort hours based on the indoor operative temperature.

^{2}K of the wall construction obtained the minimum value of energy consumption, while the U-value = 0.544 W/m

^{2}K of the wall construction obtained the minimum value of thermal discomfort hours based on simulation output (the range of U-value was between 2.767 W/m

^{2}K and 0.403 W/m

^{2}K). The U-value = 0.239 W/m

^{2}K of the roof construction obtained the minimum value of both energy consumption and thermal discomfort hours based on simulation output (the range of U-value was between 6.223 W/m

^{2}K and 0.239 W/m

^{2}K).

#### 3.3.3. Effect of Glazing, Window to Wall Ratio and Shading Devices

#### 3.3.4. Effect of Occupancy Density

#### 3.3.5. Effect of Infiltration Rate and Mechanical Ventilation Rate per Area

#### 3.3.6. Effect of Building Orientation

#### 3.4. Relationship between Students’ Thermal Discomfort Hours and Building Energy Consumption

## 4. Conclusions

- (1)
- The simulation results showed a significant potential to optimise the ABDPs to achieve energy saving and thermal comfort in educational buildings in NSW, Australia.
- (2)
- Based on the parametric and sensitivity analyses, there is a very weak relationship between the students’ thermal discomfort hours and the building cooling/heating load. However, the cooling and heating set-point temperatures, as well as roof construction, had a significant impact on the sensitivity of the ABDPs for both building energy consumption and student thermal comfort (p = 0.0000).
- (3)
- Increasing the cooling set-point temperature from 22 °C to 28 °C and using a U-value of 0.239 W/m
^{2}K in roof construction can reduce the operative temperatures by 14.2% and 20.0%, respectively. These reductions could significantly lower the thermal discomfort hours by 6.0 and 3.25 times, respectively. - (4)
- The findings of this study are particularly useful for architectural design teams because they enable designers to decide easily which of the sensitive ABDPs are more important than the others based on the simulation outcomes. Moreover, architectural design teams can save time by not focusing on ABDPs that have small effects on thermal comfort and energy consumption.
- (5)
- However, there remains a number of important challenges and areas for additional study. For example, there is a need for more studies regarding indoor thermal performance and students’ thermal comfort, as well as students’ academic performance. More work is required to design performance criteria to quantitatively evaluate the ABDPs with UA and SA capabilities.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Acronyms

ABCB | Australian Building Codes Board |

ABDPs | Architectural Building Design Parameters |

BCA | Building Code of Australia |

BES | Building Energy Simulation |

EPW | EnergyPlus Weather file |

HVAC | Heating, Ventilation and Air Conditioning |

IAQ | Indoor Air Quality |

IEQ | Indoor Environmental Quality |

LHS | Latin Hypercube Sampling |

MC | Monte Carlo |

MV | Mechanical Ventilation |

NSW | New South Wales |

PA | Parametric Analysis |

PMV | Predicted Mean Vote |

PPD | Predicted Percentage Dissatisfaction |

ROC | Rate Of Change |

SA | Sensitivity Analyses |

SD | Standard Deviation |

SHGC | Solar Heat Gain Coefficient |

SRC | Standardised Regression Coefficient |

UA | Uncertainty Analysis |

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**Figure 1.**Illustration of the methodology for processing the MC technique and statistical analysis by using the BES tool and SPSS software.

**Figure 2.**Classroom (

**a**) ground floor plan of selected educational building (1:35 scale) (

**b**) interior settings.

**Figure 4.**UA results (

**a**) distribution of total energy consumption. (

**b**) Distribution of total thermal discomfort hours.

**Figure 5.**The effect of cooling set-point temperature on (

**a**) energy consumption. (

**b**) Students thermal discomfort hours.

**Figure 6.**The effect of heating set-point temperature on (

**a**) energy consumption. (

**b**) Students thermal discomfort hours.

**Figure 7.**The effect of U-Value of external wall construction on (

**a**) energy consumption. (

**b**) Students’ thermal discomfort hours.

**Figure 8.**The effect of U-value of roof construction on (

**a**) energy consumption and (

**b**) students’ thermal discomfort hours.

**Figure 9.**The effect of glazing type on (

**a**) energy consumption and (

**b**) students’ thermal discomfort hours.

**Figure 10.**The effect of window to wall ratio on (

**a**) energy consumption and (

**b**) students’ thermal discomfort hours.

**Figure 11.**The effect of shading types on (

**a**) energy consumption (

**b**) students’ thermal discomfort hours. Legend. 1: 0.5 projection Louvre, 2: 1.0 projection Louvre, 3: 1.5 projection Louvre, 4: No shading, 5: 0.5 m Overhang, 6: 1.0 m Overhang, 7: 1.5 m Overhang, 8: 2.0 m Overhang.

**Figure 12.**The effect of occupancy density on (

**a**) energy consumption and (

**b**) students’ thermal discomfort hours.

**Figure 13.**The effect of infiltration rate on (

**a**) energy consumption and (

**b**) students’ thermal discomfort hours.

**Figure 14.**The effect of mechanical ventilation rate per area on (

**a**) energy consumption and (

**b**) students’ thermal discomfort hours.

**Figure 15.**The effect of building orientation on (

**a**) energy consumption and (

**b**) students’ thermal discomfort hours.

**Figure 16.**Normal distribution of (

**a**) students’ discomfort hours and (

**b**) building energy consumption.

The Extent of the Impact | ABDPs | Output 1 Thermal Discomfort Hours | ABDPs | Output 2 Energy Consumption | ||
---|---|---|---|---|---|---|

SRC | p-Value | SRC | p-Value | |||

Cooling set-point temperature (°C) | 0.5705 | 0.0000 * | Cooling set-point temperature (°C) | −0.6926 | 0.0000 * | |

Flat roof construction | −0.4317 | 0.0000 * | Flat roof construction | −0.4086 | 0.0000 * | |

Heating set-point temperature (°C) | −0.3389 | 0.0000 * | Heating set-point temperature (°C) | 0.1852 | 0.0000 * | |

Occupancy density (people/m^{2}) | −0.0501 | 0.0006 * | External wall construction | 0.0553 | 0.0001 * | |

Glazing type | −0.0409 | 0.0049 * | ||||

Mech. vent rate per area (l/s/m^{2}) | 0.0345 | 0.0178 * | Infiltration (ac/h) | 0.0525 | 0.0001 * | |

Infiltration (ac/h) | 0.0319 | 0.0282 * | Window to wall ratio (%) | 0.0519 | 0.0001 * | |

Occupancy density (people/m^{2}) | 0.0472 | 0.0006 * | ||||

Crack template (airtightness) | −0.0272 | 0.0612 | Crack template (airtightness) | −0.0214 | 0.1165 | |

External wall construction | 0.0259 | 0.0749 | Roof window opens ratio (%) | −0.0214 | 0.1166 | |

Building orientation (°) | 0.0176 | 0.2270 | Thermal mass | −0.0157 | 0.2478 | |

Window to wall ratio (%) | −0.0162 | 0.2646 | Building rotation (°) | 0.0129 | 0.3436 | |

Local shading type | −0.0087 | 0.5496 | Mech. vent rate per area (l/s-m^{2}) | 0.0099 | 0.4695 | |

Thermal mass | −0.0061 | 0.6743 | Location template | −0.0097 | 0.4744 | |

Location template | −0.0028 | 0.8472 | Local shading type | −0.0085 | 0.5306 | |

Roof window opens (%) | −0.0027 | 0.8531 | Glazing type | 0.0029 | 0.8302 |

Correlations | |||
---|---|---|---|

Discomfort (All Clothing) (h) | Total Site Energy Consumption (kWh) | ||

Discomfort (All Clothing) (h) | Pearson Correlation | 1 | −0.033 |

Sig. (2-tailed) | - | 0.153 | |

N | 1845 | 1845 | |

Total site energy consumption (kWh) | Pearson Correlation | −0.033 | 1 |

Sig. (2-tailed) | 0.153 | - | |

N | 1845 | 1845 |

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**MDPI and ACS Style**

Alghamdi, S.; Tang, W.; Kanjanabootra, S.; Alterman, D.
Effect of Architectural Building Design Parameters on Thermal Comfort and Energy Consumption in Higher Education Buildings. *Buildings* **2022**, *12*, 329.
https://doi.org/10.3390/buildings12030329

**AMA Style**

Alghamdi S, Tang W, Kanjanabootra S, Alterman D.
Effect of Architectural Building Design Parameters on Thermal Comfort and Energy Consumption in Higher Education Buildings. *Buildings*. 2022; 12(3):329.
https://doi.org/10.3390/buildings12030329

**Chicago/Turabian Style**

Alghamdi, Salah, Waiching Tang, Sittimont Kanjanabootra, and Dariusz Alterman.
2022. "Effect of Architectural Building Design Parameters on Thermal Comfort and Energy Consumption in Higher Education Buildings" *Buildings* 12, no. 3: 329.
https://doi.org/10.3390/buildings12030329