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TOWARDS LIGHTING OPTIMIZATION A PERFORMANCE-BASED APPROACH
INÊS PEREIRASUPERVISOR | ANTÓNIO LEITÃO
MOTIVATION
KOLDING CAMPUS
HENNING LARSEN ARCHITECTS
https://www.archdaily.com/590576/sdu-campus-kolding-henning-larsen-architects
ANALYSIS TOOLS
Radiance
ANALYSIS TOOLS
ANALYTICAL MODEL
ANALYTICAL MODEL
3D model
Castelo-Branco, R., & Leitão, A. (2017). Integrated Algorithmic Design: A Single-Script Approach for Multiple Design Tasks. (35th eCAADe)
ANALYTICAL MODEL
analyticalmodel
3D model
Castelo-Branco, R., & Leitão, A. (2017). Integrated Algorithmic Design: A Single-Script Approach for Multiple Design Tasks. (35th eCAADe)
ANALYTICAL MODEL
3D model lightingsimulation
Castelo-Branco, R., & Leitão, A. (2017). Integrated Algorithmic Design: A Single-Script Approach for Multiple Design Tasks. (35th eCAADe)
LONDON CITY HALL
FOSTER + PARTNERS
Photo credit: Josh Perrett
PERFORMANCE-BASED
DESIGN
ALGORITHMIC
DESIGN
HEYDAR ALIYEV CENTER
ZAHA HADID ARCHITECTS
https://www.bayut.com/mybayut/modern-architecture-spotlight-zaha-hadid-architect/
AND ANALYSIS
Castelo-Branco, R., & Leitão, A. (2017). Integrated Algorithmic Design: A Single-Script Approach for Multiple Design Tasks. (35th eCAADe)
PARAMETRIC MODEL
ALGORITHIMIC DESIGNAND ANALYSIS
Castelo-Branco, R., & Leitão, A. (2017). Integrated Algorithmic Design: A Single-Script Approach for Multiple Design Tasks. (35th eCAADe)
ANALYTICAL MODEL + ANALYSIS
ALGORITHIMIC DESIGNAND ANALYSIS
KOSHINO HOUSE
TADAO ANDO
OBJECTIVES
https://www.artstation.com/artwork/Zb3V1
OBJECTIVES
a
b
parametricmodel
OBJECTIVES
a
b
parametricmodel
analysistools
OBJECTIVES
a
b
optimizationprocesses
parametricmodel
analysistools
OBJECTIVES
a
b
optimizationprocesses
parametricmodel
analysistools
performance-baseddesign solution
OBJECTIVES
a
b
optimizationprocesses
parametricmodel
analysistools
performance-baseddesign solution
https://www.freepik.com/free-photo/business-team-working-office-worker-concept_2862206.htm
METHODOLOGY
METHODOLOGYSENSITIVITY ANALYSIS
METHODOLOGYSENSITIVITY ANALYSIS
establishcorrelations
METHODOLOGYSENSITIVITY ANALYSIS
establishcorrelations
inform decisions
METHODOLOGYSENSITIVITY ANALYSIS
reducedesign space
establishcorrelations
inform decisions
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
architect
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
a
b
parametricmodel
architect
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
analysis 1
analysis 2
analysis …
a
b
parametricmodel
architect
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
analysis 1
analysis 2
analysis … optimizationalgorithm
a
b
parametricmodel
architect
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
analysis 1
analysis 2
analysis … optimizationalgorithm
a
b
parametricmodel
architect
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
analysis 1
analysis 2
analysis … optimizationalgorithm
a
b
parametricmodel
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
analysis 1
analysis 2
analysis … optimizationalgorithm
a
b
parametricmodel
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
analysis 1
analysis 2
analysis … optimizationalgorithm
a
b
parametricmodel
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
analysis 1
analysis 2
analysis … optimizationalgorithm
a
b
parametricmodel
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
analysis 1
analysis 2
analysis … optimizationalgorithm
a
b
parametricmodel
results
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
analysis 1
analysis 2
analysis … optimizationalgorithm
a
b
parametricmodel
architect
results
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
analysis 1
analysis 2
analysis … optimizationalgorithm
a
b
parametricmodel
architect
visualization
METHODOLOGYMULTI-OBJECTIVE WORKFLOW
analysis 1
analysis 2
analysis … optimizationalgorithm
a
b
parametricmodel
architect
visualization
evaluatesolutions
CASE STUDY
BLACK PAVILION
RENOVATION BYATELIER DOS REMÉDIOS
http://www.museudelisboa.pt/informacoes.html
CASE STUDY
CASE STUDY
CASE STUDY
VARIABLE PARAMETERS
CASE STUDY
skylight width
skylight height
skylight length
VARIABLE PARAMETERS
skylight and curtain-wall material
DAYLIGHT
OPTIMIZATION
DAYLIGHT METRICSPATIAL USEFUL DAYLIGHT ILLUMINANCE [SUDI]
DAYLIGHT METRICSPATIAL USEFUL DAYLIGHT ILLUMINANCE [SUDI]
climate-based
DAYLIGHT METRICSPATIAL USEFUL DAYLIGHT ILLUMINANCE [SUDI]
annualclimate-based
DAYLIGHT METRICSPATIAL USEFUL DAYLIGHT ILLUMINANCE [SUDI]
occupancyschedule
annualclimate-based
DAYLIGHT REQUIREMENTSILLUMINANCE VALUES
DAYLIGHT REQUIREMENTSILLUMINANCE VALUES
categorymaterial
classificationexample of materials
lighting illuminance
limiting annual exposure
I insensitivemetal, stone, glass,
ceramicno limit no limit
II low sensitivitycanvas, frescos, wood, leather
200 lux 600 000 lux h/yr
IIImedium
sensitivitywatercolor, pastel,
various paper50 lux 150 000 lux h/yr
IV high sensitivitysilk, newspaper,
sensitive pigments50 lux 15 000 lux h/yr
source: CIE TC 3-22 ”Museum lighting and protection against radiation damage”
DAYLIGHT REQUIREMENTSILLUMINANCE VALUES
from 0 lux to 220 lux
SUDIILLUMINANCE RANGE
categorymaterial
classificationexample of materials
lighting illuminance
limiting annual exposure
I insensitivemetal, stone, glass,
ceramicno limit no limit
II low sensitivitycanvas, frescos, wood, leather
200 lux 600 000 lux h/yr
IIImedium
sensitivitywatercolor, pastel,
various paper50 lux 150 000 lux h/yr
IV high sensitivitysilk, newspaper,
sensitive pigments50 lux 15 000 lux h/yr
source: CIE TC 3-22 ”Museum lighting and protection against radiation damage”
DAYLIGHT REQUIREMENTSILLUMINANCE VALUES
from 0 lux to 220 lux
SUDIILLUMINANCE RANGE
CURRENT SUDI
70 %
categorymaterial
classificationexample of materials
lighting illuminance
limiting annual exposure
I insensitivemetal, stone, glass,
ceramicno limit no limit
II low sensitivitycanvas, frescos, wood, leather
200 lux 600 000 lux h/yr
IIImedium
sensitivitywatercolor, pastel,
various paper50 lux 150 000 lux h/yr
IV high sensitivitysilk, newspaper,
sensitive pigments50 lux 15 000 lux h/yr
source: CIE TC 3-22 ”Museum lighting and protection against radiation damage”
SENSITIVITY ANALYSISTRANSLUCENT PANEL 25%
SENSITIVITY ANALYSIS
height = 4.3 m height = 1.5 m
TRANSLUCENT PANEL 25%
100
80
60
40
20
0
sUDI [%]
SENSITIVITY ANALYSIS
100
80
60
40
20
0
sUDI [%]
height = 4.3 m height = 1.5 m
TRANSLUCENT PANEL 25%
SENSITIVITY ANALYSIS
100
80
60
40
20
0
sUDI [%]
height = 4.3 m height = 1.5 m
TRANSLUCENT PANEL 25%
SENSITIVITY ANALYSISHEIGHT = 1.5 M
SENSITIVITY ANALYSIS
translucent panel 15% translucent panel 45% translucent panel 65%
HEIGHT = 1.5 M
100
80
60
40
20
0
sUDI [%]
SENSITIVITY ANALYSIS
translucent panel 15% translucent panel 45% translucent panel 65%
HEIGHT = 1.5 M
100
80
60
40
20
0
sUDI [%]
SENSITIVITY ANALYSIS
translucent panel 15% translucent panel 45% translucent panel 65%
HEIGHT = 1.5 M
100
80
60
40
20
0
sUDI [%]
sUDI [-1 lux , 55 lux]
SENSITIVITY ANALYSISRESULTS DISCUSSION
SENSITIVITY ANALYSISRESULTS DISCUSSION
IMPROVE DAYLIGHTCONDITIONS
SENSITIVITY ANALYSISRESULTS DISCUSSION
translucent panel 25%
height = 1.5 m
higher area
IMPROVE DAYLIGHTCONDITIONS
SENSITIVITY ANALYSISRESULTS DISCUSSION
translucent panel 25%
height = 1.5 m
higher area
IMPROVE DAYLIGHTCONDITIONS
CONSTRAINTS FOR THEMULTI-OBJECTIVE WORKFLOW
SENSITIVITY ANALYSISRESULTS DISCUSSION
translucent panel 25%
height = 1.5 m
higher area
IMPROVE DAYLIGHTCONDITIONS
width [1.5 m , 4.0 m]
fix height at 1.5 m
length [6.5 m , 17.5 m]
CONSTRAINTS FOR THEMULTI-OBJECTIVE WORKFLOW
translucent panels 25%, 35%, 45%
MULTI-OBJECTIVE
OPTIMIZATION
OBJECTIVES
OBJECTIVES
OBJECTIVES
OBJECTIVES
OBJECTIVES
maximize
OBJECTIVES
OBJECTIVES
minimize
OBJECTIVES
maximize
minimize
OBJECTIVES
OBJECTIVES
OBJECTIVES
maximize
minimize
OBJECTIVES
OBJECTIVES
maximize
minimize
OBJECTIVES
OBJECTIVES
maximize
minimize
OPTIMIZATION ALGORITHMGENETIC ALGORITHM
create population
OPTIMIZATION ALGORITHMGENETIC ALGORITHM
create population
OPTIMIZATION ALGORITHM
evaluate population
9
7
7
8 65
2
5
9
4
4
1
8
1
3
10
GENETIC ALGORITHM
create population
OPTIMIZATION ALGORITHM
evaluate population
select parents
GENETIC ALGORITHM
9
7
7
8 65
2
5
9
4
4
1
8
1
3
10
create population
OPTIMIZATION ALGORITHM
evaluate population
select parents
create children
GENETIC ALGORITHM
9
7
7
8 65
2
5
9
4
4
1
8
1
3
10
create population
OPTIMIZATION ALGORITHM
evaluate population
select parents
create children
mutate population
GENETIC ALGORITHM
9
7
7
8 65
2
5
9
4
4
1
8
1
3
10
create population
OPTIMIZATION ALGORITHM
evaluate population
select parents
create children
mutate population
GENETIC ALGORITHM
9
7
7
8 65
2
5
9
4
4
1
8
1
3
10
VISUALIZATIONPARETO FRONT
cost [€]
sUD
I[%
]PARETO FRONT
VISUALIZATION
PARETO FRONT
cost [€]
sUD
I[%
]
VISUALIZATION
PARETO FRONT
cost [€]
sUD
I[%
]
summer solstice, 12 pm
winter solstice, 12 pm
VISUALIZATION
PARETO FRONT
cost [€]
sUD
I[%
]
summer solstice, 12 pm
winter solstice, 12 pm
VISUALIZATION
PARETO FRONT
cost [€]
sUD
I[%
]
summer solstice, 12 pm
winter solstice, 12 pm
VISUALIZATION
CONCLUSION
CONCLUSION
importance ofevaluate performance
CONCLUSION
a
b
importance ofevaluate performance
relevance ofparametric models
CONCLUSION
a
b
importance ofevaluate performance
relevance ofparametric models
small scale buildingsalso benefit from AD
FUTURE WORK
FUTURE WORK
test different optimization algorithms
FUTURE WORK
test different optimization algorithms
add moreobjectives
FUTURE WORK
test different optimization algorithms
add moreobjectives
automate the creation of parametric models
a
bA
CONTRIBUTIONS
Optimizing Exhibition Spaces
A Multi-Objective Approach
Pereira, I., Belém, C. and Leitão, A.
(2019)
Proceedings of the 37th eCAADe Conference
Porto University, Portugal
THANK YOUQUESTIONS?
DESIGN SPACE
DESIGN SPACE
a
b
DESIGN SPACE
a
b
DESIGN SPACE
a
b
CONSTRAINTS
[a1 , a2]
[b1 , b2]
a
b
DESIGN SPACE
a
b
CONSTRAINTS
[a1 , a2]
[b1 , b2]
a
b
DESIGN SPACE
a
b
CONSTRAINTS
[a1 , a2]
[b1 , b2]
a
b
DESIGN SPACE
a
b
CONSTRAINTS
[a1 , a2]
[b1 , b2]
a
b
DESIGN SPACE
a
b
CONSTRAINTS
[a1 , a2]
[b1 , b2]
a
b
DESIGN SPACE
a
b
CONSTRAINTS
[a1 , a2]
[b1 , b2]
a
b
CURRENT CONDITIONSPOINT-IN-TIME LUMINANCE
CURRENT CONDITIONS
SUMMERSOLSTICE
POINT-IN-TIME LUMINANCE
9 am
15 pm
12 pm
17 pm
1950
1750
1550
1350
1050
850
650
450
250
50
CD/M2
CURRENT CONDITIONS
WINTERSOLSTICE
POINT-IN-TIME LUMINANCE
9 am
15 pm
12 pm
17 pm
1950
1750
1550
1350
1050
850
650
450
250
50
CD/M2