The accuracy of the predictive model is evaluated by randomly selecting a set of result values and comparing them with the corresponding set of test values, the closer the predicted value is to the test value, the higher accuracy prediction has.
Besides, the study also uses the index Nash Sutcliffe Efficiency (NSE) to evaluate the predictive performance of the model:
NSE = 1- ∑ (𝑦𝑡−𝑥𝑡)
𝑛 2 1
∑ (𝑦𝑛1 𝑡−𝑦̅)2 n is the sample size
yt is the value selected for evaluation
xt is the predicted value
ȳ is the mean of yt in the sample
The closer the NSE value is to 1, the more accurate the model's prediction performance is.
Combining physical and statistical models using Machine Learning techniques as shown below:
In which: Training data is used for RF model, test data is used to check the accuracy of the predictive model after being trained.
3.7. Software used in the study
In the thesis, research support software has been used for specific purposes as shown in the table below:
Creating 3D models with the characteristics of a typical office by
DesignBuilder software
Identifying primary simulation variables and their ranges of value
Simulation processing by DesignBuilder software
Energy consumption datasets with different values of variables
Creating RF model to predict energy consumption
Defining design alternatives having the optimal energy consumption Splitting Training data and Testing data
Figure 3.5: Procedure of building a model to predict energy consumption
Table 3.3: List of software used in the study
No Name of software Objective
1 IBM SPSS Statistics 22 Inferential statistics
2 Microsoft Excel 2019 Descriptive statistics
3 DesignBuilder V6.1.0.006 Energy consumption
simulation
4 Python 3.10.9 and Spyder IDE 5.4.1
Creating RF model to predict the energy
consumption
ANALYSIS
1.1. Determining the factors
To analyze and present factors affecting the selection of materials - construction towards sustainable construction, the author has studied based on previous studies, specialized technical documents and consulted experienced experts in the field of green buildings and buildings with efficient energy use. Most of these experts have over 10 years of experience in designing, project management or contractor of projects applying green standards. They are also working for reputed companies in construction fields such as Investors, Design Consultants, Project Management Consultants, Contractors.
After referring to these sources, the study identified 12 main factors impacting the decision of selecting design options. Because the objective of this research is toward sustainable construction, the results include factors not only relating to material – construction characteristics but also energy consumption which are divided into 2 groups as follows:
Table 4.1: Factors affecting to decision of choosing design-construction options
No Factors Referred source
A. Factors relating to energy consumption
A1 Total annual electricity usage [18]
A2 Total annual CO2 emission Expert
A3 Total annual discomfort hours [18]
B. Factors relating to designing features
B1 Sound insulation capacity Personal experience
B2 Firefighting capacity Expert
B3 Aesthetic aspect Expert
B4 Impact to Structure Expert
83%
17%
Have joined Have not joined
No Factors Referred source
B5 Durability Expert
B6 Impact to method statement and construction schedule Personal experience
B7 Impact to operation and maintenance Expert
B8 Availability in market Personal experience
B9 Recycling capacity Expert
1.2. Questionnaire Design
Details of the Questionnaire are performed in Appendix 1 1.3. Survey results
Details of the Survey results are performed in Appendix 2 1.4. Analyzing the characteristics of the study sample
The suitability of the survey participants
Table 4.2: Percentage of participants who have ever joined in Green Building projects or energy efficient buildings
No Response Quantity Percentage Graph
1 Have joined 43 83%
2 Have not
joined 9 17%
Total 52 100%
Comment: The majority of survey subjects have ever participated in the process of implementing green buildings or energy efficient buildings, accounting for 82.7%, the rate of never participating accounts for 17.3%, however these people have researched and updated Update knowledge about green building, energy efficient building.
Table 4.3: Years of experience of the survey participants
No Response Quantity Percentage Graph
1 1-5 years 5 10%
2 5-10 years 23 44%
3 More than 10
years 24 46%
Total 52 100%
Comment: The percentage of respondents who have worked in the construction industry for more than 5 years accounts for 90%, showing that the majority of respondents have a lot of experience, which contributes to the respondents' responses.
very reliable.
Expertise of the survey participants Table 4.4: Expertise of the survey participants
No Response Quantity Percentage Graph
1 Project
Manager 4 8%
2 Designer 21 40%
3 Supervisor 8 15%
4 Quantity
Surveyor 19 37%
Total 52 100%
10%
44%
46%
1-5 years 5-10 years
> 10 years
8%
40%
15%
37%
Project Manager Designer Supervisor Quantity Surveyor
Comment: The data shows that the expertise of the survey subjects is very diverse, which contributes to making the research results more multidimensional and objective.
In which, the proportion of architects, engineers and project managers accounts for a total of 48%, showing that the results come from subjects with deep knowledge of the research topic accounting for a high proportion.
Roles of the survey participants Table 4.5: Roles of the survey participants
No Response Quantity Percentage Graph
1 Client 17 33%
2 Project
Manager 7 13%
3 Design
Consultant 8 15%
4 Contractor 12 23%
5 QS Consultant 8 15%
Total 52 100%
Comment: Statistics show that the roles of survey subjects are very diverse, in which the Client and Contractor groups account for a higher proportion, which contributes to the multidimensional and objective response results.
33%
15% 13%
23%
15%
Client
Project Manager Design Consultant Contractor QS Consultant
Reliability of the scale is analyzed by IBM SPSS Statistics 22 Table 4.6: 1st results of reliability testing
No
Scale Mean if Item Deleted
Scale Variance if
Item Deleted
Corrected Item-Total Correlation
Cronbach’s Alpha if
Item Deleted
Cronbach’s Alpha A. Factors relating to energy consumption
A1 173.85 237.897 0.501 0.669
0.713
A2 181.00 202.353 0.602 0.544
A3 178.46 159.391 0.535 0.654
B. Factors relating to designing features
B1 455.87 5046.236 -0.052 0.877
0.859
B2 461.15 4452.094 0.515 0.852
B3 467.62 4435.339 0.436 0.856
B4 474.10 3889.736 0.819 0.823
B5 482.38 3536.633 0.837 0.815
B6 486.19 3584.629 0.764 0.824
B7 490.44 3656.095 0.665 0.836
B8 487.10 4119.696 0.399 0.867
B9 501.46 3701.038 0.763 0.824
As a result of the first test, the variable B1 – Sound insulation capacity is excluded because the total correlation coefficient is -0.052 < 0.3. After removing the variable B1, the second loop gives the following result:
Table 4.7: 2nd results of reliability testing
No
Scale Mean if Item Deleted
Scale Variance if
Item Deleted
Corrected Item-Total Correlation
Cronbach’s Alpha if
Item Deleted
Cronbach’s Alpha A. Factors relating to energy consumption
A1 173.85 237.897 0.501 0.669
0.713
A2 181.00 202.353 0.602 0.544
A3 178.46 159.391 0.535 0.654
B. Factors relating to designing features
B2 378.73 4486.632 0.493 0.877
0.877
B3 385.19 4459.256 0.426 0.881
No
Scale Mean if Item Deleted
Scale Variance if
Item Deleted
Corrected Item-Total Correlation
Cronbach’s Alpha if
Item Deleted
Cronbach’s Alpha
B4 391.67 3910.224 0.811 0.848
B5 399.96 3551.959 0.834 0.839
B6 403.77 3616.220 0.749 0.850
B7 408.02 3643.353 0.681 0.859
B8 404.67 4092.813 0.422 0.888
B9 419.04 3689.253 0.781 0.846
Comment:
The test results show that:
- Corrected Item-Total Correlations are suitable (All ≥ 0.3).
- Cronbach’s Alpha within 0,70 ≤ α ≤ 0.9 shows that the scale is good, meets the requirements of reliability.
1.6. Ranking factors
After evaluating the reliability of the scale, rank the factors based on the average value of the influence according to the response results of the survey subjects. This result will be converted to a 100-point scale to apply to the CBA assessment.
Table 4.8: Ranking factors through Mean values
Rank Variable Description Mean Std.
Deviation
100-point scale converted 1 A1 Total annual electricity usage 92.81 6.937 100 2 A3 Total annual discomfort hours 88.19 9.959 95
3 A2 Total annual CO2 emission 85.65 7.690 90
4 B2 Firefighting capacity 77.13 7.603 85
5 B3 Aesthetic aspect 70.67 8.913 75
6 B4 Impact to Structure 64.19 10.175 70
7 B5 Durability 55.90 13.268 60
8 B6 Impact to method statement
and construction schedule 52.10 13.767 55
9 B8 Availability on market 51.19 14.010 55
Deviation
converted 10 B7 Impact to operation and
maintenance 47.85 14.499 50
11 B9 Recycling capacity 36.83 12.627 40
1.7. One sample T-Test
Table 4.9: Results of One-sample T-Test
One-Sample T-Test Test value = 50 Variable t df
A1 44.500 51 0.000 42.808 40.88 44.74
A2 33.434 51 0.000 35.654 33.51 37.79
A3 27.655 51 0.000 38.192 35.42 40.96
B2 25.736 51 0.000 27.135 25.02 29.25
B3 16.726 51 0.000 20.673 18.19 23.15
B4 10.058 51 0.000 14.192 11.36 17.03
B5 3.209 51 0.002 5.904 2.21 9.60
B6 1.098 51 0.277 2.096 -1.74 5.93
B7 -1.071 51 0.289 -2.154 -6.19 1.88
B8 .614 51 0.542 1.192 -2.71 5.09
B9 -7.523 51 0.000 -13.173 -16.69 -9.66
Comment:
The variables A1, A2, A3, B2, B3, B4, B5 and B9 have Sig. values (2-tailed) ≤ α = 0.05.
Therefore, rejecting H0 and accepting H1, that is, the average influence of all survey subjects on the influence of factors other than 50 (the mean value of the mean influence).
Variables B6, B7 and B8 have Sig values. (2-tailed) ≥ α = 0.05. Therefore, accepting H0 means accepting the assumption that the average influence of all survey subjects on the influence of the factors is equal to 50 (the mean value of the average influence).
1.8. Multi-sample testing
Mean difference analysis for the experience of the respondents Table 4.10: Mean difference analysis for the experience of the respondents
Test of Homogeneity of Variances
Var. Description Levene
Statistic df1 df2 Sig.
A1 Total annual electricity usage 0.369 2 49 0.694
A2 Total annual CO2 emission 0.910 2 49 0.409
A3 Total annual discomfort hours 3.645 2 49 0.033
B2 Firefighting capacity 0.039 2 49 0.962
B3 Aesthetic aspect 2.236 2 49 0.118
B4 Impact to Structure 2.818 2 49 0.069
B5 Durability 3.969 2 49 0.025
B6 Impact to method statement and
construction schedule 2.855 2 49 0.067
B7 Impact to operation and
maintenance 00.993 2 49 0.378
B8 Availability on market 3.217 2 49 0.049
B9 Recycling capacity 3.147 2 49 0.052
ANOVA
Var. Description Sum of
Squares df Mean
Square F Sig.
A1
Total annual electricity usage
Between Groups 101.826 2 50.913 1.061 0.354 Groups Within 2352.251 49 48.005
Total 2454.077 51 A2
Total annual CO2
emission
Between Groups 44.958 2 22.479 0.371 0.692 Groups Within 2970.812 49 60.629
Total 3015.769 51 A3
Total annual discomfort hours
Between Groups 279.573 2 139.786 1.433 0.248 Groups Within 4778.504 49 97.520
Total 5058.077 51 B2 Firefighting
capacity
Between Groups 184.816 2 92.408 1.639 0.205 Groups Within 2763.242 49 56.393
Total 2948.058 51
B3 Between Groups 465.980 2 232.990 3.184 0.050
Var. Description Sum of
Squares df Mean
Square F Sig.
Aesthetic aspect
Groups Within 3585.463 49 73.173 Total 4051.442 51
B4 Impact to Structure
Between Groups 717.840 2 358.920 3.855 0.028 Groups Within 4562.237 49 93.107
Total 5280.077 51 B5 Durability
Between Groups 1122.891 2 561.446 3.502 0.038 Groups Within 7855.628 49 160.319
Total 8978.519 51 B6
Impact to method &
construction
Between Groups 1061.152 2 530.576 3.021 0.058 Groups Within 8605.367 49 175.620
Total 9666.519 51 B7
Impact to operation &
maintenance
Between Groups 1526.292 2 763.146 4.067 0.023 Groups Within 9194.477 49 187.642
Total 10720.769 51 B8 Availability
on market
Between Groups 485.948 2 242.974 1.250 0.295 Groups Within 9524.129 49 194.370
Total 10010.077 51 B9 Recycling
capacity
Between Groups 796.664 2 398.332 2.661 0.080 Groups Within 7334.778 49 149.689
Total 8131.442 51
Robust Tests of Equality of Means
Var. Description Welch
Statistic df1 df2 Sig.
A1 Total annual electricity usage 1.214 2 13.230 0.328
A2 Total annual CO2 emission 0.340 2 12.198 0.718
A3 Total annual discomfort hours 0.627 2 10.089 0.554
B2 Firefighting capacity 1.813 2 11.931 0.205
B3 Aesthetic aspect 2.135 2 10.273 0.168
B4 Impact to Structure 2.165 2 10.390 0.164
B5 Durability 1.944 2 10.240 0.192
B6 Impact to method statement and
construction schedule 1.700 2 10.486 0.229
B7 Impact to operation and
maintenance 2.680 2 10.816 0.113
B8 Availability on market 0.481 2 10.238 0.632
Robust Tests of Equality of Means
Var. Description Welch
Statistic df1 df2 Sig.
B9 Recycling capacity 1.716 2 9.785 0.230
Comment:
The number of working years of the survey respondents did not affect the assessment of the influence of the factors: A1, A2, A3, B2, B3, B5, B6, B8, B9.
The number of working years of the survey respondents affected the assessment of the influence of the factors: B4, B7.
Mean difference analysis for the expertise of the respondents Table 4.11: Mean difference analysis for the expertise of the respondents
Test of Homogeneity of Variances
Var. Description Levene
Statistic df1 df2 Sig.
A1 Total annual electricity usage 1.687 3 48 0.182
A2 Total annual CO2 emission 1.897 3 48 0.143
A3 Total annual discomfort hours 0.750 3 48 0.528
B2 Firefighting capacity 0.138 3 48 0.937
B3 Aesthetic aspect 1.140 3 48 0.343
B4 Impact to Structure 3.494 3 48 0.023
B5 Durability 2.797 3 48 0.050
B6 Impact to method statement and
construction schedule 3.523 3 48 0.022
B7 Impact to operation and
maintenance 6.751 3 48 0.001
B8 Availability on market 3.754 3 48 0.017
B9 Recycling capacity 7.714 3 48 0.000
Var. Description Sum of
Squares df Mean
Square F Sig.
A1
Total annual electricity usage
Between Groups 346.886 3 115.629 2.634 0.060 Groups Within 2107.191 48 43.900
Total 2454.077 51 A2
Total annual CO2
emission
Between Groups 89.150 3 29.717 0.487 0.693 Groups Within 2926.620 48 60.971
Total 3015.769 51 A3
Total annual discomfort hours
Between Groups 290.799 3 96.933 0.976 0.412 Groups Within 4767.278 48 99.318
Total 5058.077 51 B2 Firefighting
capacity
Between Groups 217.990 3 72.663 1.278 0.293 Groups Within 2730.068 48 56.876
Total 2948.058 51 B3 Aesthetic
aspect
Between Groups 147.339 3 49.113 0.604 0.616 Groups Within 3904.104 48 81.335
Total 4051.442 51 B4 Impact to
Structure
Between Groups 823.217 3 274.406 2.955 0.042 Groups Within 4456.860 48 92.851
Total 5280.077 51 B5 Durability
Between Groups 1946.039 3 648.680 4.428 0.008 Groups Within 7032.481 48 146.510
Total 8978.519 51 B6
Impact to method &
construction
Between Groups 1340.750 3 446.917 2.577 0.065 Groups Within 8325.769 48 173.454
Total 9666.519 51 B7
Impact to operation &
maintenance
Between Groups 3478.514 3 1159.505 7.685 0.000 Groups Within 7242.256 48 150.880
Total 10720.769 51 B8 Availability
on market
Between Groups 997.125 3 332.375 1.770 0.165 Groups Within 9012.952 48 187.770
Total 10010.077 51 B9 Recycling
capacity
Between Groups 2301.039 3 767.013 6.315 0.001 Groups Within 5830.404 48 121.467
Total 8131.442 51
Robust Tests of Equality of Means
Var. Description Welch
Statistic df1 df2 Sig.
A1 Total annual electricity usage 2.691 3 18.354 0.076 A2 Total annual CO2 emission 0.459 3 16.675 0.714 A3 Total annual discomfort hours 8 3 21.849 0.001
B2 Firefighting capacity 1.064 3 11.383 0.402
Robust Tests of Equality of Means
Var. Description Welch
Statistic df1 df2 Sig.
B3 Aesthetic aspect 0.754 3 12.943 0.54
B4 Impact to Structure 1.535 3 15.925 0.244
B5 Durability 2.332 3 20.929 0.103
B6 Impact to method statement and
construction schedule 1.369 3 16.745 0.287
B7 Impact to operation and
maintenance 2.806 3 10.375 0.092
B8 Availability on market 0.882 3 18.894 0.468
B9 Recycling capacity 4.724 3 13.138 0.019
Comment:
The expertise of the survey respondents did not affect the assessment of the influence of the factors: A1, A2, A3, B2, B3, B4, B6, B7, B8.
The expertise of the survey respondents affected the assessment of the influence of the factors: B5, B9.
Mean difference analysis for the role of the respondents Table 4.12: Mean difference analysis for the role of the respondents
Test of Homogeneity of Variances
Var. Description Levene
Statistic df1 df2 Sig.
A1 Total annual electricity usage 1.009 4 47 0.412
A2 Total annual CO2 emission 3.782 4 47 0.010
A3 Total annual discomfort hours 2.366 4 47 0.066
B2 Firefighting capacity 1.174 4 47 0.334
B3 Aesthetic aspect 2.476 4 47 0.057
B4 Impact to Structure 2.913 4 47 0.031
B5 Durability 5.362 4 47 0.001
B6 Impact to method statement and
construction schedule 3.326 4 47 0.018
Var. Description Levene
Statistic df1 df2 Sig.
B7 Impact to operation and
maintenance 4.454 4 47 0.004
B8 Availability on market 2.738 4 47 0.040
B9 Recycling capacity 10.830 4 47 0.000
ANOVA
Var. Description Sum of
Squares df Mean
Square F Sig.
A1
Total annual electricity usage
Between Groups 175.578 4 43.895 0.905 0.469 Groups Within 2278.499 47 48.479
Total 2454.077 51 A2
Total annual CO2
emission
Between Groups 168.088 4 42.022 0.694 0.600 Groups Within 2847.681 47 60.589
Total 3015.769 51 A3
Total annual discomfort hours
Between Groups 490.318 4 122.579 1.261 0.299 Groups Within 4567.759 47 97.186
Total 5058.077 51 B2 Firefighting
capacity
Between Groups 118.671 4 29.668 0.493 0.741 Groups Within 2829.387 47 60.200
Total 2948.058 51 B3 Aesthetic
aspect
Between Groups 100.253 4 25.063 0.298 0.878 Groups Within 3951.190 47 84.068
Total 4051.442 51 B4 Impact to
Structure
Between Groups 638.649 4 159.662 1.617 0.186 Groups Within 4641.428 47 98.754
Total 5280.077 51 B5 Durability
Between Groups 1382.652 4 345.663 2.139 0.091 Groups Within 7595.868 47 161.614
Total 8978.519 51 B6
Impact to method &
construction
Between Groups 1070.669 4 267.667 1.464 0.228 Groups Within 8595.850 47 182.890
Total 9666.519 51 B7
Impact to operation &
maintenance
Between Groups 2389.348 4 597.337 3.370 0.017 Groups Within 8331.421 47 177.264
Total 10720.769 51 B8 Availability
on market
Between Groups 1568.958 4 392.239 2.184 0.085 Groups Within 8441.119 47 179.598
Total 10010.077 51 B9 Recycling
capacity
Between Groups 1753.764 4 438.441 3.231 0.020 Groups Within 6377.678 47 135.695
Total 8131.442 51
Robust Tests of Equality of Means
Var. Description Welch
Statistic df1 df2 Sig.
A1 Total annual electricity usage 0.958 4 19.163 0.453 A2 Total annual CO2 emission 0.436 4 20.016 0.781 A3 Total annual discomfort hours 1.54 4 18.294 0.232
B2 Firefighting capacity 0.713 4 20.259 0.593
B3 Aesthetic aspect 0.18 4 18.865 0.946
B4 Impact to Structure 1.246 4 19.908 0.324
B5 Durability 1.838 4 21.217 0.159
B6 Impact to method statement and
construction schedule 2.319 4 21.017 0.091
B7 Impact to operation and
maintenance 3.578 4 20.742 0.023
B8 Availability on market 1.715 4 18.43 0.189
B9 Recycling capacity 1.098 4 21.845 0.383
Comment:
The role of the survey respondents did not affect the assessment of the influence of the factors: A1, A2, A3, B2, B3, B4, B5, B6, B8, B9.
The role of the survey respondents affected the assessment of the influence of the factors: B7.
5.1. Simulation of building energy consumption using DesignBuilder 5.1.1. Procedure of building an energy simulation model
Simulation models can be built directly in DesignBuilder software, or during the design process, can take advantage of information models built by architecture, structural, and MEP disciplines to build energy simulation models. By assigning geolocation parameters and selecting weather stations to provide data for analysis. Then proceed to export the energy model with GBxml cloud to import into DesignBuilder software. The designer selects the parameters to be calculated, assigns the input parameter values and runs the simulation to get the results.
Preliminary architecture model
Energy model in GBxml format
Energy model in DesignBuilder
Figure 5.1: Procedure of building an energy simulation model in DesignBuilder
5.1.2. Model information
A typical office area 144 m2, height from floor to roof 3.8m, weather data at Tan Son Hoa station, Ho Chi Minh City.
5.1.3. Defining design variables and input data to DesignBuilder Defining design variables
Referring to the available studies, the ANSI/ASHRAE/IES Standard: Energy Standard for Buildings Except Low-Rise Residential Buildings, LEED standard, the author has determined the variable of the parameters used in the simulation model as shown in the table below:
Table 5.1: Define design variables
Item Variables Symbol Unit Reference
1 Ratio of Window on Wall WWR % [18], [33],
[34], [16]
2 Building Orientation BO (o) [33], [34],
[16]
3 Heat transfer rate (U-value) of external Wall
W-U W/(m2.K) [18], [33], [34]
4 Heat transfer rate (U-value) of Glazing
G-U W/(m2.K) [18], [33], [34]
5 Solar Heat Gain Coefficient of Glazing
SHGC [18], [33],
[34]
6 Heat transfer rate (U-value) of Roof R-U W/(m2.K) [18], [33], [34]
Figure 5.2: Input weather data
7 Cooling setting point CSP C [33], [34]
8 Cooling coefficient of performance CoP [33], [34], [16]
Explanation of technical terms:
- U-Value is the glazing parameter that characterizes the heat transfer through the central part of the glazing, i.e. without edge effects, and expresses the steady-state density of heat transfer rate per temperature difference between the environmental temperatures on each side. US Standard units are Btu/hrãft²ãF and SI / Metric units are W/m2 K.
- Solar Heat Gain Coefficient (SHGC) is the sum of the solar direct transmittance and the secondary heat transfer factor of the glazing towards the inside, the latter resulting from heat transfer by convection and longwave IR-radiation of that part of the incident solar radiation which has been absorbed by the glazing.
- The coefficient of performance or COP (sometimes CP or CoP) of a heat pump, refrigerator or air conditioning system is a ratio of useful heating or cooling provided to work (energy) required.
Input design variables to DesignBuilder:
Based on the author's experience, the values of the design variables mentioned above are determined in detail as follows:
Table 5.2: Design variables data
Item Variables Symbol Options Structure Description 1 Ratio of
Window on Wall
WWR (%)
30 – 60 (in steps of 5%)
2 Building Orientation
BO (o) 0 – 350 (in steps of 10o)
3 Heat transfer rate (U-
W-U
(W/(m2.K))
Hollow brick wall – U=1.743
- Outer layer: mortar 25mm thk.
Item Variables Symbol Options Structure Description value) of
external Wall
- Middle layer: 2 layers of burned clay brick – 180mm thk.
- Inner layer: mortar 15mm thk
AAC block wall – U = 0.957
- Outer layer: mortar 15mm thk.
- Middle layer: 1 layer of Autoclaved Aerated Concrete 200mm thk.
- Inner layer: mortar 15mm thk.
Concrete block wall – U = 0.983
- Outer layer: mortar 25mm thk.
- Middle layer: 1 layer of Aggregate concrete block 190mm thk.
- Inner layer: mortar 15mm thk.
4 Heat transfer rate (U- value) of Glazing
G-U
(W/(m2.K))
Laminated Solar control glass, 13.52mm - U = 5.1
- Solar control glass Tempered 6mm thk.
- PVB 1.52mm thk.
- Clear glass
Tempered 6mm thk.
Laminated Low E glass, 13.52mm - U = 5.1
- Full tempered glass with Low E coating 6mm thk.
- PVB 1.52mm thk.
- Clear glass
Tempered 6mm thk.
Laminated Clear glass, 13.52mm - U = 5.3
- Clear glass
Tempered 6mm thk.
- PVB 1.52mm thk.
- Clear glass
Tempered 6mm thk.
5 Solar Heat Gain
Coefficient of Glazing
SHGC Laminated Solar control glass,
13.52mm - SHGC = 0.45
Laminated Low E glass, 13.52mm - SHGC = 0.43 Laminated Clear glass, 13.52mm – SHGC = 0.82 6 Heat transfer
rate (U- value) of Roof
R-U
(W/(m2.K))
Concrete roof insulated by Rockwool – U = 0.339
- Ceiling tiles: 80mm thk.
- Cement screeding:
20mm thk.
- Rockwool: 50mm thk.
- Cement screeding:
20mm thk.
- Waterproofing layer:
20mm thk
Item Variables Symbol Options Structure Description - Roof concrete:
150mm thk.
Concrete roof insulated by XPS form – U = 0.282
- Ceiling tiles: 80mm thk.
- Cement screeding:
20mm thk.
- XPS foam: 50mm thk.
- Cement screeding:
20mm thk.
- Waterproofing layer:
20mm thk - Roof concrete:
150mm thk.
Concrete roof without insulation – U = 0.535
- Ceiling tiles: 80mm thk.
- Cement screeding:
20mm thk.
- Waterproofing layer:
20mm thk - Roof concrete:
150mm thk.
7 Cooling setting point
CSP (oC) 24 – 26 (in steps of 0.5 degree)
8 Cooling coefficient of performance
CoP 2.5 – 4.5 (in steps of 0.1)
Figure 5.3: Design variables setting in DesignBuilder Define objectives and additional outputs
DesignBuilder software runs simulations based on input variables information and gives optimal results for objective functions. The software allows the input of up to 2 objective functions. The remaining output information if necessary, can be set in Additional Output.
Based on the survey results on the importance of design elements towards sustainable construction, the author chooses two objective functions as follows:
1. Minimize electricity usage 2. Minimize Discomfort hours
Remaining factor – CO2 emission will be included in Additional output.
Figure 5.4: Objectives and Outputs setting
5.1.4. Simulation results:
After entering the design parameter values, the model is run to generate a complete data set of results for each parameter set and find the optimal parameter sets of the objective functions as follows:
Table 5.3: Simulation results in DesignBuilder
No. WW
%
CST
(°C) CoP BO
(°) Glazing E-Wall Roof Electricity (kWh)
Discomfort
(hr) CO2 (kg)
1 50 24 4.2 0 Glazing 1 Wall 3 Roof 2 33,047.0 51.9 20,026.5
2 45 24 3.7 110 Glazing 3 Wall 3 Roof 3 35,039.0 148.6 21,233.6 3 55 25 4.5 230 Glazing 2 Wall 1 Roof 1 32,121.5 1,176.6 19,465.6 4 60 25 4.1 340 Glazing 3 Wall 2 Roof 3 32,932.3 1,097.8 19,957.0 5 55 26 3.9 220 Glazing 2 Wall 2 Roof 2 32,418.1 2,008.9 19,645.3 6 45 25 2.8 300 Glazing 1 Wall 1 Roof 2 36,931.1 1,140.6 22,380.2 7 55 25.5 4.3 270 Glazing 1 Wall 2 Roof 1 31,856.2 1,597.0 19,304.8 8 45 25 3.7 300 Glazing 2 Wall 2 Roof 2 33,529.0 852.5 20,318.5 9 45 25 4.1 160 Glazing 3 Wall 3 Roof 1 33,209.9 1,187.8 20,125.2 10 40 24 3.9 130 Glazing 1 Wall 2 Roof 1 33,976.1 48.8 20,589.5 11 60 24.5 4.1 170 Glazing 1 Wall 3 Roof 3 33,146.2 236.3 20,086.6 12 45 25 3.3 220 Glazing 2 Wall 2 Roof 1 34,853.8 925.3 21,121.4 13 35 25.5 3.9 330 Glazing 1 Wall 1 Roof 2 32,860.1 1,653.4 19,913.2 14 60 24.5 3.7 0 Glazing 2 Wall 3 Roof 3 33,964.3 219.5 20,582.4 15 50 25 2.6 340 Glazing 3 Wall 2 Roof 2 38,102.5 1,013.9 23,090.1 16 45 25 4.5 190 Glazing 2 Wall 3 Roof 1 31,933.8 912.5 19,351.8 17 45 24 4.4 270 Glazing 1 Wall 2 Roof 3 32,722.3 58.4 19,829.7 18 30 26 2.5 210 Glazing 3 Wall 1 Roof 2 38,051.9 2,107.1 23,059.4 19 40 24 3 140 Glazing 3 Wall 1 Roof 2 37,905.9 234.9 22,970.9 20 60 26 2.7 120 Glazing 2 Wall 2 Roof 2 36,313.5 2,044.6 22,006.0 21 50 24 4.2 130 Glazing 1 Wall 3 Roof 1 33,252.9 49.4 20,151.2 22 50 24.5 3.9 310 Glazing 1 Wall 2 Roof 1 33,403.0 205.8 20,242.2 23 30 26 4.4 130 Glazing 1 Wall 2 Roof 1 31,513.2 2,048.0 19,097.0 24 45 26 3.8 330 Glazing 3 Wall 2 Roof 2 32,868.3 2,008.5 19,918.2 25 55 26 3.9 180 Glazing 1 Wall 3 Roof 2 32,438.1 2,046.6 19,657.5 26 35 24.5 4.4 250 Glazing 1 Wall 1 Roof 1 32,619.9 437.3 19,767.7
… … … … … … … … … … …
3332 60 25 2.5 50 Glazing 1 Wall 2 Roof 2 38,300.1 892.6 23,209.8 3333 30 24.5 3.5 210 Glazing 2 Wall 2 Roof 2 34,640.1 208.0 20,991.9 3334 60 24 2.5 170 Glazing 3 Wall 2 Roof 2 40,327.9 123.8 24,438.7 3335 60 24 4.5 290 Glazing 2 Wall 2 Roof 2 32,452.5 54.6 19,666.2 3336 60 24 2.7 50 Glazing 2 Wall 2 Roof 3 38,397.8 56.6 23,269.0 3337 50 24 4.2 30 Glazing 2 Wall 2 Roof 2 33,112.3 51.9 20,066.0 3338 45 24 2.5 70 Glazing 2 Wall 2 Roof 2 39,468.5 48.4 23,917.9 3339 55 24 3.5 110 Glazing 1 Wall 3 Roof 2 35,088.0 48.8 21,263.3 3340 55 24 4.4 120 Glazing 2 Wall 3 Roof 2 32,809.7 49.3 19,882.7 3341 60 26 4.1 90 Glazing 1 Wall 2 Roof 2 31,999.0 2,038.0 19,391.4 3342 40 24 2.5 20 Glazing 2 Wall 2 Roof 2 39,363.5 52.1 23,854.3 3343 45 25 3.7 20 Glazing 1 Wall 3 Roof 2 33,553.8 854.4 20,333.6 3344 30 24 2.7 20 Glazing 2 Wall 2 Roof 2 38,215.3 52.1 23,158.5 3345 45 25.5 4.3 350 Glazing 1 Wall 2 Roof 2 31,821.1 1,568.9 19,283.6
Algorithm by Python Programming Language.
5.2.1. Procedure of creating an energy prediction model
The first step of creating a prediction model is transforming qualitative variables such as Glazing, External Wall and Flat roof into quantitative variables. As described in Table 5.1 and 5.2, Glazing types have two quantitative variables (SHGC and U-value), External wall and Flat roof have one quantitative variable (U-value).
The model is developed on Spyder editor with Python programming language version 3.10 using tools available in Scitkit-learn library for Random Forest algorithm as below procedure:
Table 5.4: Procedure of creating a prediction model for energy consumption
Step 1: Import libraries
Step 2: Import and split data
Step 3: Train RF model
Step 4: Evaluate accuracy of model after training