Optimization and Prediction of Different Building Forms for Thermal Energy Performance in the Hot Climate of Cairo Using Genetic Algorithm and Machine Learning
Abstract
:1. Introduction
2. Workflow
2.1. Base Case
2.2. Model Architectural Building Form
2.3. Run Thermal Energy Simulation
2.4. Optimization and Machine Learning
3. Results
3.1. The First Building Form Family: Polygon Shape
3.2. The Second Building Form Family: Pixels
3.3. The Third Building Form Family: Letters Shape
3.4. The Fourth Building Form Family: Round Shape
3.5. Scatterplots and Machine Learning of the Round Form
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Form Dynamic Parameters | Building Rotation | |||
No. of parameters | Initial form Dimensions | 6 | 4 | 1 |
Attributes for each parameter | The dimensions are in meters and are regarded as 1 in the values. | 1 (base case), 1.5, 2, 2.5, and 3. Values are in percentages (%). | 1 (base case),1.5, and 2. Values are in percentages (%). | 0 (base case), 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2. Angles are in radians. |
Form Dynamic Parameters | Building Expansion along One Axis | Floor Expansion along the Other Axis | Building Rotation | |
No. of parameters | Initial form Dimensions | 1 | 3 | 1 |
Attributes for each parameter | The dimensions are in meters and are regarded as 1 in the values. | 1 (base case), 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2. Values are in percentages (%). | 1 (base case), 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2. Values are in percentages (%). | 0 (base case), 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2. Angles are in radians. |
Form Dynamic Parameters | Polygon Sides | Floor Expansion | Building Rotation | |
No. of parameters | Initial form Dimensions | 1 | 3 | 1 |
Attributes for each parameter | The dimensions are in meters and are regarded as 1 in the values. | 5, 6, 7, 8, 9, or 10. | 1 (base case), 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2. Values are in percentages. | 0 (base case), 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2. Angles are in radians. |
Form Dynamic Parameters | Plan | Elevation | |
No. of parameters | Initial form Dimensions | 27 | |
Attributes for each parameter | The dimensions are in meters and are regarded as 1 in the values. | 0 (base case), 4 m in the x, y, or z directions. |
Static Parameters | Values (Cairo) |
---|---|
Floors number | 3 |
Letters form thermal zones no. | 18 |
Pixels form thermal zones no. | 27 |
Circular forms thermal zones no. | 3 |
Pentagonal forms thermal zones no. | 3 |
Floor Height in all forms | 3 m |
Building Height | 9 m, but it varies for the pixel’s family. |
Windows | No |
Roof shape of each family | Flat except for the pixel’s family |
Interior & exposed floors U-value | 1.449209 W/m2-K |
External walls | CBECS 1980–2004 Exterior Wall MASS, Climate Zone 2B |
External walls U-value | 3.573262 W/m2-K |
Window | ASHRAE 189.1–2009 EXTWINDOW CLIMATEZONE 2B |
Glazing U-value | 13.833333 W/m2-K |
Roof | CBECS 1980–2004 EXTROOF IEAD CLIMATEZONE 2B |
Roof U-value | 0.274975 W/m2-K |
Interior walls U-value | 2.58 W/m2-K |
Interior & exposed floors U-value | 1.449209 W/m2-K |
Equipment load per area | 7.64 W/m2 |
Infiltration rate per area | 0.0002 m3/s m2 |
Number of people per area | 0.0565 ppl/m2 |
Ventilation per area | 0.0003 m3/s m2 |
Ventilation per person | 0.0024 m3/s |
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Khalil, A.; Lila, A.M.H.; Ashraf, N. Optimization and Prediction of Different Building Forms for Thermal Energy Performance in the Hot Climate of Cairo Using Genetic Algorithm and Machine Learning. Computation 2023, 11, 192. https://doi.org/10.3390/computation11100192
Khalil A, Lila AMH, Ashraf N. Optimization and Prediction of Different Building Forms for Thermal Energy Performance in the Hot Climate of Cairo Using Genetic Algorithm and Machine Learning. Computation. 2023; 11(10):192. https://doi.org/10.3390/computation11100192
Chicago/Turabian StyleKhalil, Amany, Anas M. Hosney Lila, and Nouran Ashraf. 2023. "Optimization and Prediction of Different Building Forms for Thermal Energy Performance in the Hot Climate of Cairo Using Genetic Algorithm and Machine Learning" Computation 11, no. 10: 192. https://doi.org/10.3390/computation11100192