Feasibility Study on Parametric Optimization of Daylighting in Building Shading Design
Abstract
:1. Introduction
2. Methodology
2.1. Research Procedures and Methods
- To find optimal fitness with manually input parameters adjusted by 30 persons,
- To find optimal fitness data with Galapagos, a component inside Grasshopper that can optimize shape,
- To find efficiency and improvement of optimization in green building facade design through a comparative analysis of manually adjusting input parameters and genetic algorithms.
2.1.1. Base Simulation Model and Input Data
2.1.2. Simulation Program and Basic Concept
2.1.3. Daylight Factor (DF)
- (1)
- DF under 2 is not adequately lit and artificial lighting will be required,
- (2)
- DF between 2 and 5 is adequately lit but artificial lighting may be in use for part of the time,
- (3)
- DF over 5 is well-lit and artificial lighting is generally not required except at dawn and dusk, although glare and solar gain may cause problems.
2.1.4. Louver/Window Type and Input Parameters
2.2. Methods and Procedures of the Experiment
2.2.1. Manual Approach of Adjusting Input Parameters
- Calculate the output (fitness: the ratio of analysis grid surface area having a DF value of 2% to 5%) as operation result.
- Adjust the input parameters appropriately and choose the form of louvers which create a high fitness.
- Repeat at least 10 times for each window/louver type and adjust the input parameters in order to get to the highest possible optimal value.
2.2.2. Genetic Algorithm Optimization Methods Using Galapagos
3. Result
3.1. Manual Approach of Adjusting Input Parameters
3.2. Genetic Algorithm Optimization Methods Using Galapagos
According to Figure 8, the horizontal axis is each parameter, i.e., count, angle, depth. The vertical axis is the number of cases (5 × 5 × 5) for each parameter. The upper 10% results for each type confirm the aggregation parts. It is apparent that the upper 10% fitness parameter combination gives only one kind in the case of the horizontal louver (Type 1) (Figure 8a). On the contrary, the different vertical louver types have diverse upper 10% fitness parameters. The random vertical louver (Figure 8h) provides six kinds of parameter combinations. In summary, the Galapagos results show that the random vertical louver has more design alternatives than the horizontal louver when it is set to find the upper 10% fitness values.
4. Conclusions
- Parametric design technology to create optimal indoor lighting conditions by adjusting shading shapes can help to improve daylighting quality in early design stages.
- Conventional methods which depend on designers’ experience and knowledge can sufficiently be applied with computer simulation techniques in several design types, notably horizontal and vertical louver types, which represent linear relationships in daylight simulation.
- Computer-assisted daylight simulation can help and assist the conventional approach, which can be maximized when dealing with large amounts of data and non-liner algorithms such as in the case of random, Delaunay, and Voronoi.
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
References
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Input Materials | Material Name in DIVA | Material Properties | |
---|---|---|---|
1 | Wall | GenericInteriorWall_50 | This is a purely diffuse reflector with a standard wall reflectivity of 60% |
2 | Ceiling | GenericCeiling_70 | Material for typical ceilings as suggested by IES-LM-83 [25] |
3 | Window | Glazing_DoublePane_Clear_80 | Visual transmittance: 80% Visual transmissivity: 87% |
4 | Floor | GenericFloor_20 | This is a purely diffuse reflector with a standard floor reflectivity of 20% |
Input Parameters | Unit | Range (min.) | Range (max.) | Step Size | Variables | |
---|---|---|---|---|---|---|
1 | Angle (degree) | Horizontal/vertical angle adjustment of the louver | −60 | 60 | 30 | 5 |
2 | Num. (integer) | The number of louvers | 5 | 13 | 2 | 5 |
3 | Depth (10 cm) | Louver depth | 2 | 6 | 1 | 5 |
4 | U Num. (integer) | Number of window panel divisions in the U direction | 5 | 13 | 2 | 5 |
5 | V Num. (integer) | Number of window panel divisions in the V direction | 5 | 13 | 2 | 5 |
6 | Remove (-) | Ratio of remove from the entire window panel | 0.2 (20%) | 0.8 (80%) | 0.15 (15%) | 5 |
7 | Pattern (step) | Number of seed point patterns | 10 | 50 | 10 | 5 |
8 | Random (-) | Random variable for seed points | 1 | 5 | 1 | 5 |
1 | 2 | 3 | 4 | 5 | 6 | ||
---|---|---|---|---|---|---|---|
Input Parameters | Horizontal Louver | Vertical Louver | Random Horizontal Louver | Random Vertical Louver | Delaunay Pattern Screen | Voronoi Pattern Screen | |
1 | Angle | √ | √ | - | - | - | - |
2 | Num. | √ | √ | - | - | - | - |
3 | Depth | √ | √ | - | - | - | - |
4 | U Num. | - | - | √ | √ | - | - |
5 | V Num. | - | - | √ | √ | - | - |
6 | Remove | - | - | √ | √ | √ | √ |
7 | Pattern | - | - | - | - | √ | √ |
8 | Random | - | - | - | - | √ | √ |
No. total variations | 125 | 125 | 125 | 125 | 125 | 125 |
(a) Horizontal louver Parameters: Angle, Num., Depth | (b) Vertical louver Parameters: Angle, Num., Depth |
(c) Random horizontal louver Parameters: U Num., V Num., WWR | (d) Random vertical louver Parameters: U Num., V Num., WWR |
(e) Delaunay pattern screen (Figure A1) * Parameters: WWR, Pattern, Random | (f) Voronoi pattern screen (Figure A2) * Parameters: WWR, Pattern, Random |
Method | Manual Approach (Adjusted Data) | Galapagos (Original Data) | ||||
---|---|---|---|---|---|---|
Fitness Value (%) | Max. of Avg. | Min. of Avg. | Aver. SD | Max. | Min. | |
Louver/WindowType | Parameter Value | |||||
Horizontal (Type 1) | 83 | 32 | 27 | 86 | 0 | |
0/5/5 | ||||||
Vertical (Type 2) | 94 | 76 | 9 | 46 | 26 | |
0/11/6, 0/13/0 | ||||||
Random horizontal (Type 3) | 70 | 67 | 16 | 75 | 20 | |
5/11/0.35 | ||||||
Random vertical (Type 4) | 90 | 87 | 6 | 47 | 36 | |
11/5/0.2 | ||||||
Delaunay (Type 5) | 79 | 61 | 19 | 47 | 6 | |
0.65/30/2 | ||||||
Voronoi (Type 6) | 65 | 55 | 18 | 44 | 0 | |
0.2/10/4, 0.2/10/5 |
Type | Coefficients | Constant | (S.E.) | p-Value |
---|---|---|---|---|
Horizontal R-squared: 0.585 | 5.71 ** | 23.04 | (0.28) | 0.001 |
Vertical R-squared: 0.301 | 1.96 ** | 73.71 | (0.17) | 0.001 |
Random horizontal R-squared: 0.001 | −0.14 | 69.60 | (0.32) | 0.65 |
Random vertical R-squared: 0.008 | 0.19 | 87.57 | (0.12) | 0.12 |
Delaunay R-squared: 0.049 | 1.53 ** | 64.93 | (0.39) | 0.001 |
Voronoi R-squared: 0.006 | −0.47 | 63.10 | (0.36) | 0.19 |
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Lee, K.S.; Han, K.J.; Lee, J.W. Feasibility Study on Parametric Optimization of Daylighting in Building Shading Design. Sustainability 2016, 8, 1220. https://doi.org/10.3390/su8121220
Lee KS, Han KJ, Lee JW. Feasibility Study on Parametric Optimization of Daylighting in Building Shading Design. Sustainability. 2016; 8(12):1220. https://doi.org/10.3390/su8121220
Chicago/Turabian StyleLee, Kyung Sun, Ki Jun Han, and Jae Wook Lee. 2016. "Feasibility Study on Parametric Optimization of Daylighting in Building Shading Design" Sustainability 8, no. 12: 1220. https://doi.org/10.3390/su8121220