A New Framework for Residential Daylight Performance Evaluation

Abstract

Current climate-based daylighting metrics have limited applicability for residential use cases and fail to highlight relevant aspects of natural light in residential spaces, such as diurnal and seasonal availability of daylight and access to direct sunlight. This paper proposes a new climate-based, annual evaluation framework that quantifies daylight autonomy and access to direct light in diurnal and seasonal bins for temperate and cold climates. Spatial maps, as well as apartment scores, can be computed. Rigorous testing at various architectural scales highlights the usefulness and sensitivity of the introduced framework.

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A New Framework for Residential Daylight Performance Evaluation
Timur Dogan, Ye Chan Park
tkdogan@cornell.edu, ycp4@cornell.edu
Environmental Systems Lab, Cornell, Ithaca, New York, USA
Abstract
Current climate-based daylighting metrics have limited
applicability for residential use cases and fail to highlight
relevant aspects of natural light in residential spaces, such
as diurnal and seasonal availability of daylight and access
to direct sunlight. This paper proposes a new climate-
based, annual evaluation framework that quantifies
daylight autonomy and access to direct light in diurnal and
seasonal bins for temperate and cold climates. Spatial
maps, as well as apartment scores, can be computed.
Rigorous testing at various architectural scales highlights
the usefulness and sensitivity of the introduced
framework.
Introduction
Daylight is a valuable natural resource that has been
linked to quality of space (Corrodi & Spechtenhauser,
2008) (Rockcastle & Andersen, 2013) (Gherri, 2015),
occupant health and well-being (Webb, 2006) (Lockley,
2009), and energy conservation by offsetting electric
lighting and its impact on heating and cooling loads (Li &
Lam, 2000) (Athienitis & Tzempelikos, 2002) (Franzetti,
Fraisse, & Achard, 2004) (Sabry, Sherif, Gadelhak, &
Aly, 2014) (Altan, Mohelnikova, & Hofman, 2015).
Furthermore, daylighting is an essential connector
between the interior and exterior of a space and provides
“psychological and physiological benefits not obtainable
with electric lighting or windowless buildings” (Aghemo
& Pellegrino, 1997) (Robbins, 1986) (Tregenza &
Wilson, 2013). Consequently, researchers consider
daylighting at various scales, ranging from daylight-
enhancing façade components (Raphael, 2011) (Dogan &
Stec, 2016) to urban studies (Compagnon, 2004)
(Strømann-Andersen & Sattrup, 2011) (Dogan, Reinhart,
& Michalatos, 2012), and have developed a variety of
Daylight Performance Metrics (DPMs) that aim to
quantify the different aspects of natural light (Reinhart,
Mardaljevic, & Rogers, 2006).
However, a search through the literature reveals that much
of current daylighting research is primarily focused on
office spaces, whereas residential architecture is rarely
considered. A keyword search across academic search
engines reveals that out of 6865 publications, 65% focus
on office spaces while only 35% focus on residential
architecture. When narrowed down to climate-based
metrics (535 papers), the divide between office and
residential spaces increases to 73% versus 27%.
Figure 1: Room orientation related to the movement of
the sun throughout the day (Northern Hemisphere)
(Neufert & Neufert, 2012)
Similarly, many rating systems for sustainable
architecture, such as LEED (Leadership in Energy and
Environmental Design) (U.S. Green Building Council,
2013a) (U.S. Green Building Council, 2013b), treat the
issue of daylighting in residential architecture as of “no or
subordinated relevance” (Mötzl & Fellner, 2011). A
notable example is the LEED Version 4 for Home Design
and Construction (U.S. Green Building Council, 2013b),
which does not contain any direct guidelines for
daylighting. This is somewhat surprising, especially since
residential construction often represents by far the largest
market segment. For example, residential buildings were
responsible for 31% of the US construction volume versus
41% for all other buildings (FMI Corporation, 2015).
In contrast, daylighting and access to direct sunlight play
prominent roles in the design of residential architecture.
Architectural design manuals, such as the Neufert
Architects' Data (Neufert & Neufert, 2012) or the guide
on Lighting for Communal Residential Buildings
(Chartered Institution of Building Services Engineers,
CIBSE, 2013), emphasize that the layout of a floor plan
and the orientation of a space should be closely linked to
the “movement of the sun”. In 1936, Neufert suggested
that certain domestic programs and room types should be

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placed in specific cardinal directions so that natural light
is most accessible during the timeframes a space is used
frequently (Figure 1). Aside from intensity of daylight and
its impact on thermal comfort, seasonal heating and
cooling loads as well as qualitative aspects of daylight,
such as the steadier, diffuse northern-light and access to
direct sunlight, are considered. In some countries, access
to direct light is even mandated by code or manifested in
norms that require direct solar exposure on the building
envelope over a minimum period of time, such as the DIN
5034-1 (German Institute for Standardization, 1999).
This leads to the question of whether current DPMs are
well suited for the evaluation of residential spaces. The
most obvious limitation is the absence of a climate-based
daylighting metric that checks for adequate direct light
access. In the office-use case, the aim often is to minimize
direct light exposure due to potential glare risks. Metrics
such as the Annual Sunlight Exposure (aSE) are designed
to raise warning flags if a space exceeds a certain number
of hours of direct light exposure (IESNA, 2012).
By simply reverting this metric, one could begin to predict
how well a certain space can access direct light. An
assessment of the accumulated annual presence of direct
light, however, seems insufficient, as direct light is
subject to diurnal and seasonal fluctuations that architects
often consider while arranging residential floor plan
layouts. For example, a bedroom would preferably face
east to make use of the morning sun, whereas a dining
space would face west to receive the late afternoon or
evening sun. Further, an apartment which has access to
both morning and evening sunlight is arguably of higher
quality than one which only receives direct light during
one specific time of the day.
Similarly, seasonal variation in daylight sufficiency is of
interest for the occupant. While seasonal fluctuation is
difficult to avoid, especially in climates in higher
latitudes, seasonal timeframes during which daylight
availability drops significantly or is even entirely absent
will likely negatively affect the occupant’s satisfaction
with a space. This should be accounted for by a residential
daylighting metric. However, the diurnal and seasonal
details are very difficult to detect with currently available
DPMs, as hourly results are often integrated over an entire
year.
To overcome the previously mentioned shortcomings of
DPMs for the residential use case, this paper proposes a
new climate-based analysis framework with the following
improvements:
• Daylight sufficiency, as well as access to direct
sunlight, is monitored.
• Diurnal and seasonal analysis bins are used to
detect whether daylight sufficiency and sunlight
access fluctuate drastically.
• Results can be visualized at different levels of
detail. Spatial maps provide useful feedback for
the interior, while overall apartment scores are
employed at the building and urban scale.
Methodology
A novel Residential Daylight Evaluation Framework that
allows a daylit residential space to be evaluated over a
one-year period is introduced. It consists of three sub-
metrics: The Residential Daylight Autonomy (RDA), the
Direct Light Access (DLA), and a summary score called
the Residential Daylight Score (RDS). The scope of the
new framework is to provide a meaningful insight of how
well a residential space can access and make use of natural
daylight in its local context. It is intended to help quantify
daylight autonomy and sunlight access of existing
buildings and to inform the residential building design
process.
Residential Daylight Autonomy
The RDA is based on the concept of the spatial Daylight
Autonomy (sDA) (Illuminating Engineering Society of
North America, 2012). Spatial Daylight Autonomy is
defined as the percentage of a building floor area under
evaluation that meets a given illuminance threshold for a
specified fraction of the occupied time. Like the sDA, the
RDA aims to evaluate daylight sufficiency that would
allow occupants to perform anticipated viewing tasks
without supplementary electrical lighting.
While a variety of illuminance targets may be of interest
to measure sufficiency in different scenarios, the authors
suggest a target illuminance of 300lux that is measured on
a sensor grid with a spacing of 0.5m to 0.8m at desk height
of 0.8m. This is regarded as adequate for general seeing
tasks in residential settings (Illuminating Engineering
Society of North America & Rea, 2000) and is used in
many other standards. The fraction of time in which the
illuminance threshold must be met or exceeded is set to
50% of the analysis period. This temporal threshold is
adopted from the sDA and is based on correlations with
occupant preferences in office and classroom settings
(Heschong Mahone Group, Inc., n.d.). It should be noted
that further supporting research conducted in residential
spaces across different locations and cultures would be
beneficial.
The major difference between the sDA and the RDA is
the analysis period. Daylight availability is most relevant
at times when it can be witnessed. Therefore, climate-
based DPMs implement occupancy schedules to include
only the hours of interest in the analysis. In the office case,
most modelers would probably agree that a 9:00-5:00
schedule is a generally adequate analysis period. For
residential spaces, however, the choice of the analysis
period seems less straightforward. The diversity of
activities observed in residential architecture, as well as
social, cultural, and personal factors that determine how
often and how long each space is occupied, makes
formulating accurate and universal occupancy schedules
a difficult task. In addition, the departure of modern
design paradigms from mono-functional room types
towards multifunctional spaces with overlapping
programs further complicate the task.
To avoid these issues altogether, the introduced metric
proposes to use three diurnal timeframes of interest

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(Morning, Noon, Evening) between sunrise and sunset
(Table 1). The motivation behind excluding all hours
during which the sun is below the horizon is to focus the
analysis on building-specific performance implications
and to exclude potential location- and climate-specific
disadvantages. Based on these timeframes, three diurnal
analysis bins are set up.
To detect significant seasonal fluctuations, these three
bins are further subdivided into Spring, Summer, Fall and
Winter timeframes, resulting in a total of 12 analysis bins
(Table 1). The seasonal timeframes are collections of
three months which center around the equinoxes and the
solstices.
Table 1: Diurnal and seasonal analysis timeframes that
produce the 12 proposed analysis bins
Morning
Noon
Evening
Sunrise-11
11-15
15-Sunset
Spring
Summer
Fall
Winter
Feb/07-
May/06
May/07-
Aug/06
Aug/07-
Nov/06
Nov/07-
Feb/06
As a result, the RDA yields the percentage of floor area
that is daylit for each analysis bin. At the sensor level, one
can determine if the sensor is daylit in the morning, noon,
or afternoon over the whole year or only for a specific
season. To plot this information within one figure, RDM
implements an RGB-color scheme (Figure 2 A). It yields
7 discrete colors that are either the base colors blue, green,
or red (representing the morning, noon, and evening
respectively) or the mixed colors cyan, yellow, magenta,
or white (representing the morning and noon, noon and
evening, morning and evening, or all day respectively).
An example that applies this color scheme to spatially
visualize RDA[300lx,50%time] is shown in Figure 2 B.
The East-West oriented apartment’s floor area near the
façade fulfills the daylighting requirements all day, as
indicated by the color white. Deeper regions marked in
cyan are well lit in the morning and around noon. In the
core region, black indicates that these areas are never
daylit. However, some blue can be found around the core,
as some morning light reaches deep into the apartment.
The annual result can be further broken down into four
plots that show the seasonal performance of the space
(Figure 3).
Direct Light Access
Direct Light Access (DLA) evaluates the unit’s access to
direct light. In most existing norms and building codes,
such as the DIN 5034-1, access to direct light must be
demonstrated on at least one window of a residential unit
Figure 2: Framework Result Overview: (A) RGB color scheme, (B) Spatial plot of annual RDA300lx,50%time for one
apartment unit, (C) Spatial plot of annual DLA on the apartment unit, (D) Daylight performance score card
Figure 3: Seasonal RDA300lx,50%time and DLA plots

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or specific days and duration (Jan 17 | 1h, Mar 21 | 4h)
(German Institute for Standardization, 1999). However,
such analysis is purely geometric and does not include
climate conditions. The DLA is evaluated on the same
floor plan sensor-grid as that used by the RDA and
proposes to conduct the geometric sunlight exposure test
for every hour of the year when direct normal radiation is
available in the weather data file of a specific location.
Further, the same 12 temporal analysis bins are used to
monitor diurnal and seasonal fluctuations. Within each
bin, the average daily sun hours are computed for each
sensor.
To visualize the DLA spatially, the same RGB-color
scheme can be employed to highlight regions on the floor
plan that are exposed to direct sunlight. The spatial map
reveals clearly where direct sunlight is experienced more
often, as well as during which time of the day (Figure 2
C). Like the RDA, the annual result can be broken down
into four seasonal plots (Figure 3). This capability to
isolate seasonal sun lighting characteristics of the
residential unit is especially crucial for the DLA, as direct
sunlight is again intrinsically subject to temporal
fluctuation.
To summarize the apartment’s DLA performance, a
consideration of all sensors is not as meaningful since we
do not expect every sensor in the apartment grid to be
exposed to direct sunlight. It is more interesting to report
the number of hours during which the apartment receives
direct sunlight for each analysis bin. Therefore, the DLA
monitors the 8 best-performing sensors and reports their
average direct light exposure in hours per day for each of
the 12 time bins. The choice of 8 sensors ensures that a
usable area (~2 to 5m2) within the apartment is metered.
Residential Daylight Score
The RDA and DLA sub-metrics are necessary to capture
the different characteristics of daylight in residential
spaces. The Residential Daylight Score (RDS) is
introduced as an all-in-one, easy-to-understand point
summary of the previous two sub-metrics to facilitate
succinct comparisons of daylighting performance per unit
at building or urban scale.
The RDS awards one point for every analysis bin given in
Table 1. To score a full point, a certain performance
threshold must be met; however, partial credit is given.
For RDA, the target percentage of the floor area that is
considered daylit is set to 60%. If this criterion is met for
the morning, noon and evening across all four seasons, a
maximum score of 12 points is awarded. The
methodology is similar for DLA: A point is awarded when
at least eight sensors in the apartment receive an average
of 2 hours of direct sunlight per day for each diurnal and
seasonal bin. Therefore, the maximum score for DLA is
also 12 points. Summing both scores allows one to
express the RDS as a single score of maximum 24 points
per unit. This single score can then be used to compare the
overall daylight performance of apartments or dwellings.
Figure 6 shows how this comparison can be visualized in
a 3D model using a false-color scheme. Further, a
Figure 4: Flow chart to summarize flow of data,
thresholds and visualization schemes
score-card with two point matrices per unit is given
(Figure 2D). The score-card identifies the seasonal and
diurnal timeframes during which daylight sufficiency is
achieved and access to direct light is available and
conversely, when underperformance occurs. A summary
of the flow of information and the combination of the sub-
metrics in the framework is given in Figure 4.
Testing the framework
To test the proposed framework, five residential building
examples were chosen. Buildings of different typological
nature and contextual situation were selected to test a
cross section of contemporary residential architecture
(Figure 5) (Schneider & Heckmann, 2011).
In total, 475 apartments were analyzed. Each apartment
was modeled with a fair amount of detail, including
partition walls, window geometry, overhangs and
balconies. All models were simulated unfurnished, and
the surface properties were assumed as given in Table 2.

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Figure 5: Five formal typologies of contemporary
residential projects and chosen samples
Figure 6: RDS (RDA[300lx, 50%time, 60%area ] + DLA[8sensors,
2h]) for all apartments, shown as false-color
visualization. The right column of the figure provides the
RDS score-card for the highest & lowest-scoring units.

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The apartment buildings were modeled in Rhino and then
linked into Grasshopper. Simulations were conducted
with DIVA (DIVA, 2016) and RADIANCE (Ward, 2016)
in order to generate climate-specific, hourly illuminance
data for each sensor and unit. This hourly illuminance
data was post-processed with a custom script to compute
the RDEF indices. The simulation parameters were kept
consistent across all example buildings and are provided
in Table 3.
Table 2: Surface reflectance and window transmittance
Material
Reflectance / Transmittance
Ceiling
70%
Floor
20%
Interior Wall
70%
White Exterior Wall
70%
Brick Exterior Wall
10-20%
Window (single-pane)
88%
Other facades
35%
Outside ground
20%
Table 3: Radiance parameters:
aa .15 ab 5 ad 2048 ar 512 as 1024
Results
Figure 6 shows the false-color scheme for the composite
RDS, consisting of RDA[300lx, 50%time, 60%area] and
DLA[8sensors, 2h] for all residential units of the five example
buildings. Alongside, the score-cards for the highest- and
lowest-performing apartments in each building are given.
Units in the building A that are located in the upper floors
perform best, as access to direct sunlight and diffuse
daylight availability in the winter are significantly better
than for apartments in the lower floors. The lower units in
the middle bar suffer from contextual shading. Similar
observations can be made for the other four examples.
For more detailed result analysis, the sub-metrics RDA
and DLA are mapped onto the floor plans, as shown in
Figure 7. Figure 7 A compares the performance of the
lower and upper floors of building B. The penetration
depth of daylight, especially that of direct sunlight, is
much deeper for the upper floor. Further, the upper floor’s
eastern side receives direct light in the morning hours,
whereas the lower units facing the courtyard do not.
Figure 7 B shows the fifth floor of building E and
compares the seasonal differences of summer and winter
daylight.
Figure 8 A and Figure 8 B provide an overview of the
RDA and DLA score distribution of all 475 simulated
units. The RDA scores concentrate in the upper half of the
score range. The DLA scores are spread wider and range
from 3 to the maximum of 12.
Discussion
The results presented in the previous section show that it
is feasible to compute residential daylight performance
indices that provide modelers with new insights into
daylight autonomy and availability of direct sunlight
during diurnal and seasonal periods. Analysis bins that
span these timeframes facilitate the detection of
significant fluctuations in performance over time and
allow modelers to optimize residential architecture to
provide adequate daylighting and good access to direct
sunlight year-round. Further, the metric aims to provide
useful information for the expert and non-expert user and
hence offers different levels of detail. The combined score
and the comparative 3D false-color visualizations in
Figure 6 clearly show qualitative differences between
apartment units within one building. To facilitate result
interpretation, the score matrix indicates when and in
which discipline the apartment unit excels or has deficits.
The spatial daylight performance maps that are
superimposed on the floor plan provide detailed design
feedback. They may provide guidance for laying out floor
plans and situating rooms and program as well as placing
windows, walls and furniture. The DLA could facilitate
the design of sunspaces or aid in the placement of light-
sensitive objects.
Urban design application:
Global trends such as urbanization and population growth
will require the construction of new cities and densify
existing ones around the globe. Given that space is a
constrained resource, new planning paradigms tend to
promote living and working in high-density urban areas.
However, increased urban density also leads to a conflict
between space-use efficiency and daylight access. This
conflict is especially relevant to residential architecture,
as adequate access to daylight is directly associated with
livability and quality of life in a dwelling. To manage this
conflict, cities have traditionally relied on zoning
guidelines that utilize geometric ratios or section-based
geometric evaluation techniques. This practice seems
antiquated in times were digital daylight simulation tools
are available and accessible (Saratsis, Dogan, & Reinhart,
2016). In this context, the proposed analysis framework
may be a useful tool to optimize high-density urban
design proposals for daylight sufficiency or may be used
by city planners to establish evidence-based daylight
zoning laws for residential neighborhoods.
However, several questions arise concerning the urban
application of the introduced framework. Significant
impact on daylight performance is expected from
decisions that are made during the early, massing model
design phase, in which architects and planners determine
the proportions of building volumes, spacing, and street
widths. In this phase, many aspects of the building
architecture, such as the floor plan and the design of the
façade, are unknown. This is a challenge for daylight
modelers, since both aspects have a significant effect on
how daylight penetrates a building. To provide some
guidance in these situations, the authors repeated the
simulations for the previously described 475 apartments
but removed all partition walls within a unit. On average,
this yields a 20% increase in RDA and DLA scores, as
shown in Table 4. Results obtained by such simulations

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should be understood as maximum potential, since
simulations further down the design process will likely
report lower performances as partitions within units are
introduced.
Table 4: Change in the average RDA and DLA scores:
With vs. without interior partitions
Partitions
No Partitions
Change [%]
RDA
8.5
10.1
19%
DLA
7.5
9.0
20%
RDS
16.0
19.0
19%
Another approach is to increase the target thresholds to
provide modelers with a more realistic prediction of the
performance levels that can be expected once the floor
plan layout is introduced. One methodology to achieve
this could be to “match” the average scores with and
without partitions and walls. Increasing the area threshold
for RDA from 60% to 100%, as well as raising the direct
sunlight exposure hours from 2 to 2.5, achieves this.
Figure 8 C shows a histogram of RDS for the detailed
model geometry and the adjusted RDS for geometry
without interior subdivisions.
Oversupply
The RDEF is a purely supply-focused analysis metric and
does not capture any notion of oversupply. A critical
reader may state that a new daylight assessment
framework should be able to inform modelers in both
over- and undersupply cases. However, a clear definition
of an upper illuminance threshold that indicates
discomfort remains difficult to establish due to user
subjectivity (IESNA, 2012) (Van Den Wymelenberg &
Figure 7: (A) Spatial plot of RDA[300lx,50%time] and DLA for Building (6 B) Fl. 1 and Fl. 7, annual. (B) Spatial plot of
RDA[300lx,50%time] and DLA for Building (6 E) Fl. 5, summer and winter.
Figure 8: (A) RDA score distribution for all units, (B) DLA score distribution for all units, (C) RDS score distribution
for detailed simulations (with interior partitions) and urban simulations (no interior partitions) using the targets of
60% area / 2hr and 100% area / 2.5 hr respectively.

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Inanici, 2009). Hence, there is “considerable uncertainty”
regarding upper thresholds for “both non-domestic and
residential buildings” (Mardaljevic, Andersen, Roy, &
Christoffersen, 2011)
Additionally, it is assumed that daylight oversupply can
be mitigated at the façade level with simple measures such
as blinds, curtains or shutters, whereas daylight
undersupply due to morphological properties of a design
cannot be overcome once a design is built.
This assumption, however, leads to the question of
whether it is appropriate for certain climates to place such
a strong emphasis on the availability of direct light. The
current test cases are predominantly located in cold or
temperate European sites. In such climates, it is safe to
assume that most people would perceive access to direct
light as a desirable amenity. In hot and arid climates,
where people tend to favor protection from the sun, it
might not be justified to optimize dwellings for direct
solar access and thereby expose the public outdoor areas,
such as streets and alleys, which would otherwise be
shaded by buildings. In such climates, different targets
need to be defined, and further research should be
conducted to determine them.
Conclusion
This paper introduced a new daylighting analysis
framework for residential architecture. It captures
relevant residential daylighting aspects, such as diurnal
and seasonal availability, as well as access to direct light;
furthermore, it offers a performance score that allows
modelers to compare performance across multiple
apartments. The framework provides several levels of
detail that range from a simple score for performance
comparison to spatial plots that allow modelers to
understand and optimize the daylighting characteristics of
a space.
Acknowledgement
The authors would like to thank the Cornell University
David R. Atkinson Center for a Sustainable Future for
funding this research as well as NVIDIA for supporting
the project with a hardware grant.
References
Aghemo, C., & Pellegrino, A. (1997). Indoor daylighting:
assessment of the performances of different window
options. In Proceedings of the Clima 2000
Conference (Vol. 30). Napoli. Retrieved from
http://www.inive.org/members_area/medias/
pdf/inive/clima2000/1997/p100.pdf
Altan, H., Mohelnikova, J., & Hofman, P. (2015). 6th
International Building Physics Conference, IBPC
2015Thermal and Daylight Evaluation of Building
Zones. Energy Procedia, 78, 2784–2789.
https://doi.org/10.1016/j.egypro.2015.11.626
Athienitis, A. K., & Tzempelikos, A. (2002). A
methodology for simulation of daylight room
illuminance distribution and light dimming for a
room with a controlled shading device. Solar Energy,
72(4), 271–281. https://doi.org/10.1016/ S0038-
092X(02)00016-6
Barnaby, C. S., & Crawley, D. B. (2011). Weather data
for building performance simulation. Building
Performance Simulation for Design and Operation,
37–55.
Chartered Institution of Building Services Engineers,
CIBSE. (2013). Lighting for Communal Residential
Buildings - LG9.
Compagnon, R. (2004). Solar and daylight availability in
the urban fabric. Energy and Buildings, 36(4), 321–
328.
Corrodi, M., & Spechtenhauser, K. (2008). Illuminating:
Natural Light in Residential Architecture. Basel :
London: Birkhäuser GmbH.
DIVA. (2016). (Version 4). Solemma LLC. Retrieved
from http://solemma.net
Dogan, T., Reinhart, C., & Michalatos, P. (2012). Urban
daylight simulation: Calculating the daylit area of
urban designs. Proceedings of SimBuild. Retrieved
from
https://stuff.mit.edu/afs/sipb/user/mkgray/bar/afs/ath
ena/dept/cron/project/sustainabledesignlab/publicati
ons/SimBuild2012_UrbanDaylightSimulation.pdf
Dogan, T., & Stec, P. (2016). Prototyping a façade-
mounted, dynamic, dual-axis daylight redirection
system. Lighting Research and Technology.
FMI Corporation. (2015). U.S. Markets Construction
Overview 2016. FMI Corporation.
Franzetti, C., Fraisse, G., & Achard, G. (2004). Influence
of the coupling between daylight and artificial
lighting on thermal loads in office buildings. Energy
and Buildings, 36(2), 117–126. https://doi.org/
10.1016/j.enbuild.2003.10.005
German Institute for Standardization, DIN. (1999). DIN
5034-1 Daylight in interiors - Part 1: general
requirements.
Gherri, B. (2015). Assessment of Daylight Performance in
Buildings: Methods and Design Strategies. WIT
Press.
Heschong Mahone Group, Inc. (n.d.). DAYLIGHT
METRICS PIER Daylighting Plus Research
Program. Heschong Mahone Group, Inc.
Illuminating Engineering Society of North America,
IESNA. (2012). LM-83-12 IES Spatial Daylight
Autonomy (sDA) and Annual Sunlight Exposure
(ASE). New York, NY, USA: IESNA Lighting
Measurement.
Illuminating Engineering Society of North America,
IESNA & Rea, M. S. (Eds.). (2000). IESNA Lighting
Handbook (9 edition). Illuminating Engineering.
Li, D. H. W., & Lam, J. C. (2000). Measurements of solar
radiation and illuminance on vertical surfaces and
daylighting implications. Renewable Energy, 20(4),

PREPRINT Proceedings of the 15th IBPSA Conference
San Francisco, CA, USA, Aug. 7-9, 2017
178
389–404. https://doi.org/10.1016/S0960-
1481(99)00126-3
Lockley, S. W. (2009). Circadian Rhythms: Influence of
Light in Humans A2 - Squire, Larry R. In
Encyclopedia of Neuroscience (pp. 971–988).
Oxford: Academic Press. Retrieved from http://
www.sciencedirect.com/science/article/pii/B978008
0450469016193
Mardaljevic, J., Andersen, M., Roy, N., & Christoffersen,
J. (2011). Daylighting metrics for residential
buildings. In Proceedings of the 27th Session of the
CIE. Retrieved from http://
infoscience.epfl.ch/record/166212
Mötzl, H., & Fellner, M. (2011). Environmental and
health related criteria for buildings. Austrian Institute
for Healthy and Ecological Building, by Request
from ANEC-Raising Standards for Consumers, Wien.
Retrieved from http://
www.ibo.at/documents/EnvironmentalCriteriaBuildi
ng_IBOforANEC_31march2011_000.pdf
Neufert, E., & Neufert, P. (2012). Neufert Architects’
Data, Fourth Edition (4 edition). Chichester, West
Sussex, UK ; Ames, Iowa: Wiley-Blackwell.
Raphael, B. (2011). Active control of daylighting features
in buildings. Computer-Aided Civil and
Infrastructure Engineering, 26(5), 393–405.
Reinhart, C. F., Mardaljevic, J., & Rogers, Z. (2006).
Dynamic daylight performance metrics for
sustainable building design. Leukos, 3(1), 7–31.
Robbins, C. L. (1986). Daylighting: design and analysis.
New York: Van Nostrand Reinhold Company.
Rockcastle, S., & Andersen, M. (2013). Celebrating
contrast and daylight variability in contemporary
architectural design: A typological approach.
Proceedings to LUX EUROPA, Krakow, Poland.
Retrieved from http://infoscience.epfl.ch/record/
186396/files/LUX%20EUROPA%202013_Rockcas
tle%20%20Andersen_FINAL.pdf
Sabry, H., Sherif, A., Gadelhak, M., & Aly, M. (2014).
Balancing the daylighting and energy performance of
solar screens in residential desert buildings:
Examination of screen axial rotation and opening
aspect ratio. Solar Energy, 103, 364–377.
https://doi.org/10.1016/j.solener.2014.02.025
Saratsis, E., Dogan, T., & Reinhart, C. F. (2016).
Simulation-based daylighting analysis procedure for
developing urban zoning rules. Building Research &
Information, 0(0), 1–14. https://doi.org/10.1080/
09613218.2016.1159850
Schneider F, & Heckmann O (2011). Floor Plan Manual
(4 Revised and Extended Edition). Birkhauser.
Strømann-Andersen, J., & Sattrup, P. A. (2011). The
urban canyon and building energy use: Urban density
versus daylight and passive solar gains. Energy and
Buildings, 43(8).
Tregenza, P., & Wilson, M. (2013). Daylighting:
Architecture and Lighting Design (1 edition).
Routledge.
U.S. Green Building Council. (2013a). LEED v4 for
Building Design and Construction.
U.S. Green Building Council. (2013b). LEED v4 for
Homes Design and Construction.
Van Den Wymelenberg, K., & Inanici, M. (2009). A study
of luminance distribution patterns and occupant
preference in daylit offices. Proceedings of the
Passive and Low Energy Architecture PLEA.
Retrieved from http://faculty.washington.edu/
inanici/Publications/Luminance_Preference_030620
09.pdf
Ward, G. (2016). Radiance (Version 5). Retrieved from
https://www.radiance-online.org/
Webb, A. R. (2006). Considerations for lighting in the
built environment: Non-visual effects of light.
Energy and Buildings, 38(7), 721–727.