Adapting the Residential Daylight Score for Arid, Hot, and Humid Climates

Abstract

Access to direct light is often considered a desirable quality in residential architecture. A novel daylighting performance metric called the Residential Daylight Score (RDS) has been suggested for cold and temperate climates to monitor diurnal and seasonal fluctuations in both daylight and access to direct light. However, direct light can significantly influence heating and cooling loads, and more research that correlates direct light with its thermal contributions is needed to make daylighting recommendations for warmer climates. As of now, residential daylight evaluation remains difficult in arid, hot, and humid climates, in which the effect of direct light on cooling loads is the largest. The authors juxtapose access to direct light with thermal and daylighting contributions across 14 different climate zones. The authors then propose a climate-based schema that considers thermal implications of daylight in residential architecture to adapt the RDS for use in warmer climates.

Full text

Adapting the Residential Daylight Score for Arid, Hot, and Humid Climates
Ye Chan Park1, Timur Dogan1
1Environmental Systems Lab, Cornell University, Ithaca, New York, USA
Abstract
Access to direct light is often considered a desirable
quality in residential architecture. A novel daylighting
performance metric called the Residential Daylight
Score (RDS) has been suggested for cold and temperate
climates to monitor diurnal and seasonal fluctuations in
both daylight and access to direct light. However, direct
light can significantly influence heating and cooling
loads, and more research that correlates direct light with
its thermal contributions is needed to make daylighting
recommendations for warmer climates. As of now,
residential daylight evaluation remains difficult in arid,
hot, and humid climates, in which the effect of direct
light on cooling loads is the largest. The authors
juxtapose access to direct light with thermal and
daylighting contributions across 14 different climate
zones. The authors then propose a climate-based schema
that considers thermal implications of daylight in
residential architecture to adapt the RDS for use in
warmer climates.
Introduction
The benefit of direct light in residential architecture has
been long discussed in the European context. Since the
beginning of the early twentieth century, architectural
design guidelines, such as those by Neufert (Neufert &
Neufert, 2012), have emphasized that domestic programs
should be oriented toward the changing direction of
direct light throughout the day. Several European
countries provide regulations or guidelines to stipulate
that each residential unit have access to direct light
(German Institute for Standardization, DIN., 1999),
which is regarded as an essential qualitative parameter
for passive heating in dense urban neighborhoods with
limited solar exposure on the façade (Strømann-
Andersen & Sattrup, 2011). Even in warmer climates,
access to direct light significantly influences the
efficiency of vitamin D synthesis in the human body.
Studies show that changes in contemporary Middle
Eastern housing design that omit private outdoor spaces,
such as courtyards, can be directly correlated with
Vitamin D deficiencies in the female population
(Fonseca, Tongia, el-Hazmi, & Abu-Aisha, 1984; Alagöl
et al., 2000). Direct light is also needed to create
physiologically and visually stimulating luminous
environments (Andersen, Mardaljevic, & Lockley, 2012;
Rockcastle & Andersen, 2014). As evident through these
benefits, evaluation techniques that acknowledge the
positive effects of direct light in the residential setting
are needed.
Residential Daylight Score
The Residential Daylight Score (RDS) was introduced as
the first residential architecture-specific daylighting
performance metric (DPM) that rewards the incidence of
direct light (Direct Light Access, DLA) and evaluates
general daylight supply (Residential Daylight Autonomy,
RDA) (Dogan & Park, 2017; Dogan & Park, 2018). To
account for seasonal and diurnal fluctuations in daylight,
both the RDA and the DLA are evaluated in 12
timeframes (4 seasons and morning, noon, and evening).
For each timeframe, the RDA evaluates whether a
residential unit receives sufficient daylight (≥300lx) for
50% or more of daytime hours, based on thresholds for
the Spatial Daylight Autonomy (sDA) (IESNA, 2012).
The DLA evaluates the duration of direct sunlight access
in a residential unit for each of the 12 timeframes.
Finally, a composite RDS score is computed, with a
maximum of 24 points (Figure 1). As the RDS assumes
the presence of direct light in the interior to be
beneficial, it was recommended for use only in cold and
temperate climates, where passive solar heating is
desirable and overheating in summer months is less of a
concern.
Direct Light, Cooling Loads, and Glare
Because of its potential to increase cooling loads during
warm months and impact occupant comfort (Utzinger &
Wasley, 1997; Bennet & O’Brien, 2017), expectations
and occupant response to direct light will differ in
warmer climates. Intuitively, the value of direct light on
an autumn day in Anchorage is much higher than that on
the same day in Miami.
However, much of the research addressing the negative
impacts of direct light have focused on completely
blocking out direct light from the interior (typically
Figure 1: Residential Daylight Score

office spaces) to reduce visual glare, either with view-
based glare probability metrics (Wienold &
Christoffersen, 2006; Wienold, 2009; Jakubiec &
Reinhart, 2012) or floor-plan-based oversupply metrics
(Nabil & Mardaljevic, 2005; Mardaljevic, Andersen,
Roy, & Christoffersen, 2011; IESNA, 2012). However,
glare may be a less serious problem in the residential
context, as occupants have an increased ability to change
their orientation and posture and to control the shading
in their environments (Jakubiec & Reinhart, 2012; Xue,
Mak, & Cheung, 2014). In many cases, overheating due
to direct light is a larger concern to residents than visual
glare (Bennet, O’Brien, & Gunay, 2014).
Meanwhile, other studies have acknowledged the
importance of balancing daylight (and more specifically,
direct light) with its thermal impacts, and various
proposals have been made to present the two kinds of
data side-by-side (De Groot, Zonneveldt, Paule, & SA,
2003; Franzetti, Fraisse, & Achard, 2004; Hviid,
Nielsen, & Svendsen, 2008). Reinhart and Wienold have
proposed a “daylighting dashboard” that separately
evaluates the daylight autonomy, occupant comfort
(visual glare and view), and energy costs of space,
modeled both with and without blinds (2011). Strømann-
Andersen and Sattrup have noted that increased urban
density moderately increases heating energy loads due to
decreased exposure to direct light (2011). Kleindienst
and Andersen have proposed three side-by-side, goal-
based metrics that evaluate illuminance, glare, and solar
heat gain (2012). Other studies address the effects of
different kinds of shading schedules and thresholds on
the artificial lighting, cooling, and heating energy loads
of interior space in a specific climate (Nielsen,
Svendsen, & Jensen, 2011; Shen & Tzempelikos, 2012).
Many of these studies and metrics acknowledge that
daylighting performance must be understood not only in
terms of providing sufficient daylight supply but also in
the increase in cooling loads due to passive solar heat
gain. However, a broader consensus on how to evaluate
direct light in dwelling units with respect to its thermal
impact still needs to be found.
Exhaustively defining the relationship between daylight
performance and thermal implications is a difficult task,
due to a large number of parametric possibilities in
defining the experimental space, its construction and
fenestration type, and other geometric variables.
Nevertheless, the authors propose an energy simulation-
based framework to study this relationship across 14
major climate zones and to inform a proof-of-concept
adaptation of the DLA so that direct light in an
apartment is considered beneficial only when it either
decreases heating energy loads or does not increase
cooling loads.
Methods
The authors indicate 14 climate zones defined by
ASHRAE 90.1 2013 (ASHRAE, 2014) (Table 1). For
each climate zone, example cities according to ASHRAE
definitions and their corresponding latitudes are listed.
Table 1: ASHRAE climate zones
Zone
Climate
Zone Name
Example Cities
Latitude
1A
Very Hot-
Humid
Miami
25°47′N
Singapore
1°17′N
2A
Hot-Humid
Houston
29°46′N
Hong Kong
22°17′N
2B
Hot-Dry
Phoenix
33°27′N
Cairo
30°3′N
3A
Warm-Humid
Atlanta
33°45′N
Tel Aviv
32°04′N
3B
Warm-Dry
Los Angeles
34°03′N
Mexico City
19°26′N
3C
Warm-Marine
San Francisco
37°47′N
Cape Town
33°56′S
4A
Mixed-Humid
New York
40°40′N
Seoul
37°34′N
4B
Mixed-Dry
Albuquerque
35°07′N
4C
Mixed-
Marine
Seattle
47°37′N
5A
Cool-Humid
Chicago
41°53′N
Munich
48°08′N
5B
Cool-Dry
Denver
39°44′N
6A
Cold-Humid
Minneapolis
44°59′N
Moscow
46°44′N
6B
Cold-Dry
Helena
46°36′N
7
Very Cold
Anchorage
61°13′N
The authors create a shoebox with the dimensions of 6m
x 10m x 3m and a floor area of 60m2 as the test space.
The shoebox has a single South-facing window. All
surfaces, except the façade, are adiabatic. A fully
conditioned space with a low air change rate (ACH =
0.3) and no natural ventilation is assumed. While the
lack of natural ventilation may result in an over-
estimation of cooling loads, the creation of a nearly
airtight design space allows the calculation of pure (or
“theoretical”) heating and cooling loads solely based on
climate, construction, and geometry. Potential cooling by
natural ventilation would thus not be considered in this
study.
For all climates, 4 levels of window-to-wall ratios
(WWRs) are tested: 10%, 30%, 50%, and 70%.
For all WWRs, both “heavy” (concrete/masonry) and
“light” (metal/timber frame) constructions are employed
to account for differences in thermal loads. For each
construction type, ASHRAE-defined U-values are used
(ASHRAE, 2014) (Table 2).
4 shading types are then employed for all construction
types. There is currently a lack of consensus on
simulating residential blind usage. Therefore, sDA
specifications for blind usage as specified by LM-83
(IESNA, 2012) were employed as a proxy. For a floor
area of 60m2, interior blinds must be drawn for all hours
during which 5 or more of the floor grid sensors receive

1000lx or more of direct light. The 4 shading types
(Table 3) include: An external shade that completely
blocks the incidence of direct light in the interior; no
shading whatsoever; interior blinds according to the sDA
protocol; and a 1m balcony extrusion (a common feature
in residential architecture) with interior blinds according
to the sDA protocol.
Table 2: Heavy mass vs. light mass construction
Mass
Type
Constr.
(exterior to
interior)
Façade
U-val.
(W/m2K)
Façade
Heat
Capacity
(kJ/m2K)
Glazing
Specification
Heavy
mass
20mm
Gypsum /
20-109mm
Foam Glass
Insulation /
300mm
Concrete /
20mm
Gypsum
U-0.404-
0.857
731.4-738.6
U-1.820-
2.840,
Tvis 0.65,
SHGC 0.27
Light
mass
20mm
Gypsum /
83-205mm
Foam Glass
Insulation /
20mm
Gypsum
U-0.248-
0.533
52.1-64.0
U-1.820-
2.840,
Tvis 0.65,
SHGC 0.27
Table 3: Shading types
Shading Type
Shading Condition
Type A
External shade that completely blocks
direct light
Type B
No shading
Type C
Dynamic interior blinds (sDA protocol)
Type D
1m extrusion above window + Dynamic
interior blinds (sDA protocol)
In total, 448 shoeboxes (14 climate zones x 4WWRs x 2
constructions x 4 shading types) are created. The authors
acknowledge that this is only a preliminary set of
variables; an expansion of variables to include other
orientations and glazing layouts is eventually necessary.
The 448 shoeboxes are tested for percentage of daylit
floor area (defined by the sDA and RDA), heating
energy loads, theoretical cooling energy loads, and
electric lighting energy loads. An electric heat-pump
with a coefficient of performance (COP) of 3 is
assummed for heating and cooling loads. DIVA4 is used
for daylighting simulations (Solemma LLC., 2016),
while Archsim is used for thermal simulations (Dogan,
2013). A summary of the workflow is found in Figure 2.
Daylighting and energy loads are summarized using the
12 time-bins from the RDS (Table 4) to facilitate a better
understanding of seasonal and diurnal differences.
Schedules for apartment occupancy, heating and cooling,
electrical lighting, and equipment follow those of the
U.S. Department of Energy (DOE) Reference Buildings,
Midrise Apartment, v. 1.4 7.2 (Department of Energy,
2004). All geometry, construction, and simulation
parameters are provided in the Appendix.
Table 4: Diurnal and seasonal analysis timeframes that
produce the 12 time-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
Results
Figure 3 shows the impact of direct light on daylighting,
heating loads, cooling loads, and electric lights loads
through a comparison between no direct light in the
interior (Shading Type A) to full exposure to direct light,
with no exterior shading or interior blinds (Shading Type
B).
Figure 4 shows a close-up of Heavy Mass: 50% WWR:
Heating Load Changes: Winter as an example for how
the chart is to be read: As the climate zones become
colder, direct light further reduces heating loads
(decrease shown in green), particularly during morning
hours. The biggest reduction in this condition is seen
during morning hours in the Cool-Dry climate (Denver,
39°44′N), with a decrease in heating loads by
Figure 2: Workflow: Shoebox combinations and testing metrics

Figure 3: Changes in daylit floor area, heating loads, cooling loads, and artificial lighting loads by fully admitting
direct light (i.e. transition from Shading Type A to Shading Type B).

3.8/kWh/m2/year. In Very Cold climates (Anchorage,
61°13′N), however, there is a smaller reduction of
heating loads, as thermal losses through the glazing are
larger than thermal gains by direct light. The results in
Figure 3 confirm a number of intuitive assumptions:
First, admitting direct light into the interior dramatically
increases the daylit floor area, particularly during noon
hours (given the southern orientation of the shoebox).
For example, a shoebox with a 30% WWR in a Cool-
Humid climate (Chicago; 41°53′N) will see an increase
of more than 50% points in daylit areas during noon
hours across the year (Figure 3-a); that is, during spring
noon hours, there is an increase from 10% of the floor
area being daylit to 64% of the floor area being daylit
(thus +54% points), and so forth. During morning and
evening hours throughout the year in that climate, there
will be moderate increases in the daylit area, except for
winter evenings, during which there is an increase of
only 15% points (Figure 3-a). The increase in daylit floor
area will also slightly reduce artificial lighting loads,
particularly during summer evenings and to a lesser
degree in spring and fall evenings.
Second, direct light in the interior will decrease heating
loads, particularly during spring and winter months in
mixed to very cold climates. Increased WWR further
reduces heating loads due to increased admittance of
direct light in the interior. Similar trends, although
slightly diminished, are observed in light-mass
construction as well. For example, a shoebox with heavy
construction and 50% WWR in a Mixed-Humid climate
(New York, 40°40′N) sees heating loads reduced by 2.5,
1.0, and 1.4 kWh/m2/year respectively during winter
morning, noon, and evening (Figure 3-b). In a light
construction of the same parameters, the heating loads
are reduced by 1.9, 0.9, and 1.1 kWh/m2/year
respectively during the same time-bins (Figure 3-c).
Third, direct light in the interior will increase theoretical
cooling loads throughout all climates, although during
different seasons. Very hot to mixed climates will see
heightened increases in cooling loads in winter months,
during which solar altitude is lower. For example, a
shoebox with heavy construction and 70% WWR in a
Hot-Dry climate (Phoenix, 33°27′N) sees direct light
increase theoretical cooling loads by roughly 3 to 5
kWh/m2/year during winter noon and evening (Figure 3-
e). Direct light will cause less increase in summer
months when solar altitude is high: 0.2, 0.3, and 0.1
kWh/m2/year respectively during a summer morning,
noon, and evening (Figure 3-d). In cool to very cold
climates, the increase in cooling loads is mostly limited
to the summer and fall. Similar trends are observed in
light-mass construction, with the notable difference that
increases in cooling loads are shifted back toward the
noon and evening hours across all seasons.
Discussion
Annual Changes in Energy Loads: Cost vs. Benefit
Figure 5 shows changes in annual loads in “heavy mass”
shoeboxes as a result of the transition from no direct
light (Shading Type A) to varying degrees of direct light
(Shading Types B,C,D). The total energy loads represent
the overall benefit or cost of direct light in the interior.
Across all WWRs, direct light in the interior will
increase total energy demand in very hot to warm
climates, despite decreases in electric lighting loads. An
increase in WWR will again intensify this trend. Direct
light becomes a benefit in most mixed to very cold
climates, due to a combined decrease in heating and
electric lighting loads. For conditions with no shading
whatsoever (shading type B), the most increase in total
Figure 4: Close-up: Changes in heating loads as a result
of admitting direct light (Shading Type A to B) for Heavy
Mass: 50% WWR: Heating Load Changes: Winter
Figure 5: Annual changes in heating, cooling, and
electric lighting loads as a result of admitting direct
light (Shading Type A to Types B,C,D)

energy loads is in 70% WWR, Hot-Dry climates (more
than 20 kWh/m2/year; Figure 5-a), while the largest
reduction is found in 70% WWR, Cold-Humid climates
(around 10 kWh/m2/year; Figure 5-b). However, interior
blinds (Shading Type C) will increase total energy loads
in most climates (even mixed climates), as electric loads
decrease only slightly (or even slightly increase). The
said shoeboxes will see a respective increase of nearly
25 kWh/m2/year (Figure 5-c) and a decrease of only 5
kWh/m2/year (Figure 5-d) with Shading Type C.
Of course, simply examining the total annual increase or
decrease in energy loads is a binary approach to the
problem. Thus, seasonal and diurnal analysis is still
necessary to understand when specifically direct light is
thermally harmful.
Adapting the Direct Light Access (DLA) Score
Using this information, the authors select 6 residential
projects across various climates (Figure 6) from an
existing set of building models, taking 20 apartment
units from each project, to assess the impact of energy
demand consideration in the calculation of the Direct
Light Access (DLA) score.
As of the current RDS, the DLA gives a full score for a
time-bin only if there is a daily average of 2 or more
hours of direct light in the interior during that time-bin.
Partial scores are possible, in the case that the 2 average
daily hours are not met. As there are 12 time-bins, the
maximum DLA score is 12. As an initial exploratory
adaptation, the authors suggest that all time-bins in
which direct light increases cooling loads and does not
reduce heating loads be excluded from the DLA
analysis. Thus, all time-bins during which direct light is
not useful for thermal purposes would be excluded from
the DLA score.
Figure 7 shows the DLA analysis of apartment units in
each of the 6 residential projects. The analysis is across
all 12 time-bins, and the y-axis reports the average daily
hours of direct light for each time-bin. All time-bins that
are considered inappropriate for DLA evaluation – i.e.
time-bins in which direct light will negatively impact
Figure 6: Six residential projects for DLA adaptation
Figure 7: DLA analysis of apartment units in each of 6
residential projects. Y-axis reports average daily hours of
direct light per time-bin. All time-bins that are considered
inappropriate for DLA evaluation are shaded out.
Proj. A: Hot-Humid; Heavy Mass; 30% WWR
Proj. B: Warm-Humid; Heavy Mass; 30% WWR
Proj. C: Warm-Dry; Heavy Mass; 30% WWR
Proj. D. Warm-Dry; Light Mass; 10% WWR
Proj. E. Mixed-Humid; Heavy Mass; 50% WWR
Proj. F. Cool-Humid; Heavy Mass; 70% WWR
Daily Hours
Daily Hours
Daily Hours
Daily Hours
Daily Hours
Daily Hours

cooling loads and will not reduce heating loads – are
shaded out. As a result, for example, none of the time-
bins in the Hot-Humid climate (given Heavy Mass, 30%
WWR) will be appropriate for DLA analysis. That is,
direct light in the interior will always be harmful in that
climate. In such case, exterior balconies would be
recommended, as direct light is still vital for various
physiological benefits (Fonseca, Tongia, el-Hazmi, &
Abu-Aisha, 1984; Andersen, Mardaljevic, & Lockley,
2012). In Mixed-Humid and Cool-Humid climates, on
the other hand, only the summer and fall time-bins
would be inappropriate for DLA analysis.
Future Steps
The current workflow is a proof-of-concept that is based
on simulation data of South-facing shoeboxes. Other
orientations and room configurations (such as corner
spaces with two facades) need further consideration.
East- or West-facing spaces, for example, would likely
result in higher cooling loads in the morning and the
evening. Further, modeling blind use in residential
architecture remains challenging and requires more
research as to whether either the LM-83 blind model or a
thermally-driven model is adequate. Possibly, an
altogether new model that also considers views and
privacy concerns, for example, may be needed.
Conclusion
While direct light access is widely regarded as a
qualitative benefit, consequential heat gains may be
undesirable in warmer climates. In order to understand
the presence of direct light and its impact on heating,
cooling, and electric lighting energy loads, this study
establishes an EnergyPlus- and Radiance-based
simulation framework to identify diurnal and seasonal
timeframes during which direct light is either a benefit or
a cost in terms of energy loads. The study analysed 448
shoeboxes of varying WWRs, construction types, and
shading conditions across 14 climate zones. It is
proposed to utilize the presented framework to adapt the
DLA so that direct light is rewarded only when there are
no negative thermal implications, expanding the
applicability of the RDS to a larger variety of climates.
Acknowledgments
The authors would like to thank the Cornell University
Atkinson Center and the Hunter Rawlings Presidential
Research Scholars program for funding this research.
References
90.1-2013 User’s Manual: I-P and SI. (2014). ASHRAE.
Alagöl, F., Shihadeh, Y., Boztepe, H., Tanakol, R.,
Yarman, S., Azizlerli, H., & Sandalci, Ö. (2000).
Sunlight exposure and vitamin D deficiency in
Turkish women. Journal of Endocrinological
Investigation, 23(3), 173–177.
Andersen, M., Mardaljevic, J., & Lockley, S. (2012). A
framework for predicting the non-visual effects of
daylight – Part I: photobiology- based model.
Lighting Research & Technology, 44(1), 37–53.
https://doi.org/10.1177/1477153511435961
Bennet, I. E., & O’Brien, W. (2017). Field study of
thermal comfort and occupant satisfaction in
Canadian condominiums. Architectural Science
Review, 60(1), 27–39.
Bennet, I., O’Brien, W., & Gunay, H. B. (2014). Effect
of window blind use in residential buildings:
Observation and simulation study. Proceedings of
eSIM2014, Ottawa, Canada, 89.
De Groot, E., Zonneveldt, L., Paule, B., & SA, E.
(2003). Dial Europe: A decision support tool for
early lighting design. In Proceedings of the 8th
International IBPSA Conference, Eindhoven, The
Netherlands, August (pp. 11–14).
Department of Energy. (2004). Commercial Reference
Buildings. Retrieved January 31, 2019, from
https://www.energy.gov/eere/buildings/commercial-
reference-buildings
Dogan, T. (2013). Archsim. Retrieved from
https://www.solemma.com/
Dogan, T., & Park, Y. (2018). A critical review of
daylighting metrics for residential architecture and a
new metric for cold and temperate climates. Lighting
Research & Technology, 1477153518755561.
https://doi.org/10.1177/1477153518755561
Dogan, T., & Park, Y. C. (2017). A New Framework for
Residential Daylight Performance Evaluation. In BS
2017, San Francisco, USA.
Fonseca, V., Tongia, R., el-Hazmi, M., & Abu-Aisha, H.
(1984). Exposure to sunlight and vitamin D
deficiency in Saudi Arabian women. Postgraduate
Medical Journal, 60(707), 589–591.
https://doi.org/10.1136/pgmj.60.707.589
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.
German Institute for Standardization, DIN. (1999). DIN
5034-1 Daylight in interiors - Part 1: general
requirements.
Hviid, C. A., Nielsen, T. R., & Svendsen, S. (2008).
Simple tool to evaluate the impact of daylight on
building energy consumption. Solar Energy, 82(9),
787–798.
IESNA, I. (2012). LM-83-12 IES Spatial Daylight
Autonomy (sDA) and Annual Sunlight Exposure
(ASE). New York, NY, USA: IESNA Lighting
Measurement.
Jakubiec, J. A., & Reinhart, C. F. (2012). The “adaptive
zone”–A concept for assessing discomfort glare
throughout daylit spaces. Lighting Research &
Technology, 44(2), 149–170.
Kleindienst, S., & Andersen, M. (2012). Comprehensive
annual daylight design through a goal-based
approach. Building Research & Information, 40(2),
154–173.

Mardaljevic, J., Andersen, M., Roy, N., &
Christoffersen, J. (2011). Daylighting Metrics for
Residential Buildings. Proceedings of the 27th
Session of the CIE. Retrieved from
https://infoscience.epfl.ch/record/166212
Nabil, A., & Mardaljevic, J. (2005). Useful daylight
illuminance: a new paradigm for assessing daylight
in buildings. Lighting Research & Technology,
37(1), 41–57.
https://doi.org/10.1191/1365782805li128oa
Neufert, E., & Neufert, P. (2012). Neufert Architects’
Data, Fourth Edition (4 edition). Chichester, West
Sussex, UK ; Ames, Iowa: Wiley-Blackwell.
Nielsen, M. V., Svendsen, S., & Jensen, L. B. (2011).
Quantifying the potential of automated dynamic solar
shading in office buildings through integrated
simulations of energy and daylight. Solar Energy,
85(5), 757–768.
Reinhart, C. F., & Wienold, J. (2011). The daylighting
dashboard–A simulation-based design analysis for
daylit spaces. Building and Environment, 46(2), 386–
396.
Rockcastle, S., & Andersen, M. (2014). Measuring the
dynamics of contrast & daylight variability in
architecture: A proof-of-concept methodology.
Building and Environment, 81, 320–333.
https://doi.org/10.1016/j.buildenv.2014.06.012
Shen, H., & Tzempelikos, A. (2012). Daylighting and
energy analysis of private offices with automated
interior roller shades. Solar Energy, 86(2), 681–704.
https://doi.org/10.1016/j.solener.2011.11.016
Solemma LLC. (2016). DIVA. Retrieved from
http://solemma.net.
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), 2011–2020.
https://doi.org/10.1016/j.enbuild.2011.04.007
Utzinger, M., & Wasley, J. H. (1997). Building Balance
Point. Vital Signs Curriculum Materials Project,
University of California–Berkeley, and University of
Wisconsin–Milwaukee.
Wienold, J. (2009). Dynamic daylight glare evaluation.
In Proceedings of Building Simulation (pp. 944–
951).
Wienold, J., & Christoffersen, J. (2006). Evaluation
methods and development of a new glare prediction
model for daylight environments with the use of
CCD cameras. Energy and Buildings, 38(7), 743–
757.
Xue, P., Mak, C. M., & Cheung, H. D. (2014). The
effects of daylighting and human behavior on
luminous comfort in residential buildings: A
questionnaire survey. Building and Environment, 81,
51–59.
Appendix
Table A1: Surface reflectance and window transmittance
Surface Type
Refl. / Tvis
Ceiling
70%
Floor
20%
Interior Wall
50%
Glazing (Double Pane, Low-E)
65%
(When applicable) Exterior Shading
35%
Outside ground
20%
Table A2: Radiance parameters
aa .15
ab 5
ad 2048
ar 512
as 1024
Table A3: Interior Dynamic Blinds Parameters
Min. Number of Sensors for Blind Trigger
5
Sensor Spacing
0.6m
Sensor Min. Distance from Window
0.1m
Sensor Max. Distance from Window
10.0m
Roller shade total transmission
4%
Roller shade direct transmission
1%
Roller shade total reflection
41%
Table A4: Zone Loads
People: Density
0.0283 p/m2
People: Schedule
Department of Energy
reference buildings,
midrise apartment, v. 1.4
7.2: APT_OCC_SCH
Equip: Loads
5.38 w/m2
Equip: Schedule
Department of Energy
reference buildings,
midrise apartment, v. 1.4
7.2: APT_EQP_SCH
Lights: Loads
3.88 w/m2
Lights: Target
300 lx
Lights: Dimming
Continuous
Lights: Schedule
Department of Energy
reference buildings,
midrise apartment, v. 1.4
7.2: APT_LIGHT_SCH
Heating: Setpoint
21.1 C
Heating: Av. Schedule
All On
Cooling: Setpoint
23.9 C
Cooling: Av. Schedule
All On
Hum: On/Off
On
Hum: Bounds
20-80%
Mechanical Ventilation: On/Off
Off
Natural Ventilation: On/Off
Off
Infiltration
0.3 ACH
Hot Water Peak Flow (l/h/person)
1.667
Coefficient of Performance (H/C)
3
Primary Energy Factor (H/C,
Lighting)
2.2