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


Abstract and Figures

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.
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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
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.
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
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.
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 Name
Example Cities
Very Hot-
Hong Kong
Tel Aviv
Los Angeles
Mexico City
San Francisco
Cape Town
New York
Very Cold
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
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
Tvis 0.65,
SHGC 0.27
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
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
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.
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.
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.
The authors would like to thank the Cornell University
Atkinson Center and the Hunter Rawlings Presidential
Research Scholars program for funding this research.
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Table A1: Surface reflectance and window transmittance
Surface Type
Refl. / Tvis
Interior Wall
Glazing (Double Pane, Low-E)
(When applicable) Exterior Shading
Outside ground
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
Sensor Spacing
Sensor Min. Distance from Window
Sensor Max. Distance from Window
Roller shade total transmission
Roller shade direct transmission
Roller shade total reflection
Table A4: Zone Loads
People: Density
0.0283 p/m2
People: Schedule
Department of Energy
reference buildings,
midrise apartment, v. 1.4
Equip: Loads
5.38 w/m2
Equip: Schedule
Department of Energy
reference buildings,
midrise apartment, v. 1.4
Lights: Loads
3.88 w/m2
Lights: Target
300 lx
Lights: Dimming
Lights: Schedule
Department of Energy
reference buildings,
midrise apartment, v. 1.4
Heating: Setpoint
21.1 C
Heating: Av. Schedule
All On
Cooling: Setpoint
23.9 C
Cooling: Av. Schedule
All On
Hum: On/Off
Hum: Bounds
Mechanical Ventilation: On/Off
Natural Ventilation: On/Off
0.3 ACH
Hot Water Peak Flow (l/h/person)
Coefficient of Performance (H/C)
Primary Energy Factor (H/C,
... Useful daylight illuminance (UDI), developed by Nabil and Mardaljevic in 2006, provides multiple illuminance ranges as proxies for classifying target illuminance levels, dividing the illuminance ranges into UDI-fell short (0-100lux), UDIsupplementary (100-300lux), UDI-autonomous (300-3000lux), and UDI-exceeded (>3000lux) (Nabil and Mardaljevic 2006). Park 2017, 2019;Park and Dogan 2020), no other daylight metric is specifically designed for residential buildings. Some challenges exist when using RDS: (1) interior activities require different illuminance levels; thus, using 300 lux to generalize all visual tasks is not comprehensive; (2) the overlit areas typically concentrate near the windows. ...
... Useful daylight illuminance (UDI), developed by Nabil and Mardaljevic in 2006, provides multiple illuminance ranges as proxies for classifying target illuminance levels, dividing the illuminance ranges into UDI-fell short (0-100lux), UDIsupplementary (100-300lux), UDI-autonomous (300-3000lux), and UDI-exceeded (>3000lux) (Nabil and Mardaljevic 2006). Park 2017, 2019;Park and Dogan 2020), no other daylight metric is specifically designed for residential buildings. Some challenges exist when using RDS: (1) interior activities require different illuminance levels; thus, using 300 lux to generalize all visual tasks is not comprehensive; (2) the overlit areas typically concentrate near the windows. ...
Conference Paper
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Most existing daylight studies are focused on commercial buildings, with very few studies on residential. Due to flexible floor layouts of residential buildings and diverse interior activities, daylight analysis tools need to provide a flexible approach for designers to understand the horizontal illuminance distribution and light levels in multiple areas of the floor plan. When light levels are measured and averaged across the entire room, it is challenging to effectively distinguish the amount of light measured by each sensor point in different locations. This study introduces a novel computational workflow to customize illuminance ranges and select target areas to examine local light levels. The workflow allows users to further optimize the residential floor plan and fenestration design.
... For instance, it is known that the availability of contrast and direct sunlight can make a space look more interesting and appealing (Chamilothori et al., 2019;Rockcastle et al., 2017;Rockcastle & Andersen, 2012). Based on this idea, the framework described by Park (2017, 2019) and Park and Dogan (2019) adapts the LM-83 (2012) standard to acknowledge that, within residential contexts, direct sunlight contributes to the attractiveness of spaces. However, this approach does not fit easily within the definition of visual comfort. ...
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The fact that comfort is a subjective state of the mind is widely accepted by engineers, architects and building scientists. Despite this, capturing all the complexity, subjectivity and richness of this construct in models that are useful in building science contexts is far from straightforward. By prioritizing usability, building science has produced models of comfort (e.g., acoustic, visual and thermal) that overly simplify this concept to something nearly objective that can be directly associated with people’s physiology and measurable and quantifiable environmental factors. This is a contradiction because, even if comfort is supposed to be subjective, most of the complexity of “the subject” is avoided by focusing on physiology; and, even if comfort is supposed to reside in the mind, the cognitive processes that characterize the mind are disregarded. This research partially mitigates this contradiction by exploring people’s non-physical personal factors and cognition within the context of their comfort and by proposing a way in which they can be incorporated into building science research and practice. This research refers to these elements together—i.e., people’s non-physical personal factors and cognition—as “the mind”. This research proposes a new qualitative model of the Feeling of Comfort that embraces “the mind”. This model was developed from the results of a first study in which 18 people—from Chile and New Zealand—were asked to describe “a home with good daylight” and “a warm home” in their own words. These results were then replicated in a second study in which another group of 24 people—also from Chile and New Zealand—described “a home with good acoustic performance”, “a home with good air quality” and “a pleasantly cool home”. The Feeling of Comfort model not only was capable of making sense of the new data (gathered in this second study) but also proved to be simple enough to be useful in the context of comfort research and practice. For instance, it guided the development of a quantitative Feeling of Comfort model and also of a prototype building simulation tool that embraces “the mind” and thus can potentially estimate people’s Feeling of Comfort. This research concludes that embracing “the mind” is not only possible but necessary. The reason for this is that “the mind” plays a significant role in the development of people’s comfort. Thus, theories and models of comfort that ignore it fail to represent properly the concept of comfort held by the people for whom buildings are designed. However, incorporating “the mind” into building science’s research and practice implies embracing tools, research methods and conceptual frameworks that have historically not been used by such a discipline. Specifically, it concludes that building science should normalize a more holistic view of comfort and perform more exploratory and qualitative research.
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Residential architecture constitutes one of the largest market segments in the construction sector. However, the attention that it is given in the field of daylight performance simulation is surprisingly low. This poses the question of whether existing daylighting metrics are well suited for residential design. Findings from 79 references are summarized, and a critical review of current climate-based daylighting metrics in the context of residential architecture is provided. It is found that existing workflows often overlook relevant aspects of daylight in residential spaces, such as diurnal and seasonal availability of daylight and access to direct sunlight. Hence, a concept for a new climate-based, annual evaluation framework that overcomes these shortcomings, called the residential daylight score, is introduced.
Conference Paper
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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 questionnaire survey was conducted to investigate the effects of daylighting and human behavior patterns on subjective luminous comfort in Hong Kong housing units. The participants were recruited via mail, and included residents of both public and private housing units. 340 questionnaires were returned and analyzed by using SPSS 19.0. In analyzing the response statistics, the Cronbach's alpha coefficient, Spearman rank correlation coefficient, Chi-square test, Kruskal–Wallis test and stepwise regression were adopted to identify the effects of particular aspects of human behavior and daylighting quality. The results confirmed that luminous comfort is a function of both behavior patterns and daylighting conditions. Behavior factors have a significant influence on luminous comfort among people who grade their satisfaction with daylighting as moderate. In general, the degree of luminous comfort is most affected by satisfaction with daylighting. External obstruction is the major physical factor affecting luminous comfort, while the perception of uniformity is the major factor of residents' feelings toward daylight. The use of artificial lighting is the most relevant behavior factor affecting luminous comfort, as using artificial lighting for many hours per day indicates poor daylighting conditions and decreased luminous comfort. These results should help to raise awareness of the detailed factors that influence the luminous environment. Our findings may also assist planners and architects to implement better daylighting for housing projects and provide residents with greater luminous comfort.
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Discomfort glare is an underutilized parameter in contemporary architectural design due to uncertainties about the meaning of existing metrics, how they should be applied and what the benefits of such analysis are. Glare is position and view direction-dependent within a space, rendering it difficult to assess compared to conventional illuminance-based metrics. This paper compares simulation results for five glare metrics under 144 clear sky conditions in three spaces in order to investigate the ability of these metrics to predict the occurrence of discomfort glare and to hence support the design of comfortable spaces. The metrics analyzed areDaylight Glare Index, CIE Glare Index, Visual Comfort Probability, Unified Glare Rating and Daylight Glare Probability. It is found that Daylight Glare Probability yields the most plausible results. In an attempt to deal with multiple positions and view directions simultaneously, the concept of an ‘adaptive zone’ is introduced within which building occupants may freely adjust their position and view in order to minimise the effect of glare. The spatial and directional extents of the adaptive zone depend on furniture layout and the freedom of occupants’ tasks. It is found that applying the adaptive zone concept to a sidelit office with manually operated venetian blinds reduces the predicted hours of intolerable discomfort glare from 735 to 18 occupied hours per year and increases the annual mean daylight availability from 40% to 72%.
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This paper investigates the formulation of a modelling framework for the non-visual effects of daylight, such as entrainment of the circadian system and maintenance of alertness. The body of empirical data from photobiology studies is now sufficient to start developing preliminary non-visual lighting evaluation methods for lighting design. Eventually, these non-visual effects have the potential to become a relevant quantity to consider when assessing the overall daylighting performance of a space. This paper describes the assumptions and general approach that were developed to propose a modeling framework for occupant exposure to non-visual effects of light, and presents a novel means of visualising the ‘circadian potential’ of a point in space. The proposed approach uses current outcomes of photobiology research to define – at this point static – threshold values for illumination in terms of spectrum, intensity and timing of light at the human eye. These values are then translated into goals for lighting simulation, based on vertical illuminance at the eye, that – ultimately – could become goals for building design. A new climate-based simulation model has been developed to apply these concepts to a residential environment. This will be described in Part 2 of this paper.
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This paper introduces a new paradigm to assess daylight in buildings called ‘useful daylight illuminance’, or UDI. The UDI paradigm preserves much of the interpretive simplicity of the conventional daylight factor approach. In contrast to daylight factors however, UDI is founded on an annual time-series of absolute values for illuminance predicted under realistic skies generated from standard meteorological datasets. Achieved UDI is defined as the annual occurrence of illuminances across the work plane where all the illuminances are within the range 100-2000 lux. These limits are based on reports of occupant preferences and behaviour in daylit offices with user-operated shading devices. The degree to which UDI is not achieved because illuminances exceed the upper limit is indicative of the potential for occupant discomfort. The relation between achieved UDI and annual energy consumption for lighting is examined.
The analysis of the global energetic needs in an office building has to take into account the daylight and artificial lighting which are tightly coupled. The luminous and thermal behaviours of a room are influenced by the artificial lighting that generates thermal loads and by the natural lighting coming from the windows. This paper deals with the foreseen energy balance of an office building with regards to the technological and architectural solutions. This study has been conducted with EDF Research and Development, Services, energies and living spaces Department. A global analysis is needed for a consistent approach of Energy Demand Save Management [Etude de l’influence de la complémentarité entre l’éclairage naturel et l’éclairage artificiel sur le comportement thermique des bâtiments tertiaires, Thèse Université de Savoie, March 2001, 213 pages].
This paper presents a comprehensive analysis to study the balance between daylighting benefits and energy requirements (control of solar gains) in perimeter private office spaces with interior roller shades taking into account glazing properties, shading properties and control together with window size, climate and orientation in an integrated daylighting and thermal manner. Daylight autonomy and useful daylight illuminances were computed as a function of façade design parameters. A thermal simulation module using the explicit finite difference thermal network approach runs at the same time step and calculates heating, cooling and lighting source energy consumption as well as surface temperatures and operative temperature. Based on the daylighting results, lighting internal gains (continuous dimming control) are simultaneously input to the thermal module. The model also considers the air in the gap between shade and interior glass as a separate thermal node.
The link between urban density and building energy use is a complex balance between climatic factors and the spatial, material and use patterns of urban spaces and the buildings that constitute them. This study uses the concept of the urban canyon to investigate the ways that the energy performance of low-energy buildings in a north-European setting is affected by their context.This study uses a comprehensive suite of climate-based dynamic thermal and daylight simulations to describe how these primary factors in the passive energy properties of buildings are affected by increases in urban density.It was found that the geometry of urban canyons has an impact on total energy consumption in the range of up to +30% for offices and +19% for housing, which shows that the geometry of urban canyons is a key factor in energy use in buildings. It was demonstrated how the reflectivity of urban canyons plays an important, previously underestimated role, which needs to be taken into account when designing low-energy buildings in dense cities. Energy optimization of urban and building design requires a detailed understanding of the complex interplay between the temporal and spatial phenomena taking place, merging qualitative and quantitative considerations.