Daylight metrics and their sensitivity

Daylight metrics
and their
sensitivity
Main report
Sophie Stoffer
Kathrine N. Brejnrod

Title page
Title: Daylight metrics and their sensitivity
Subtitle: Main report
Written by: Anne Sophie Stoffer [06042]
Kathrine N. Brejnrod [20062459]
Study: Architectural Engineering
School: Aarhus School of Engineering
Project period: 27th
of Jan. – 30th
of May. 2014
Mentor: Werner Osterhaus
Pages: Main report: 32,4 pages [á 2400 characters]
Appendices: 34 pages [á 2400 characters]

Chapter 1 -
Daylight metrics and their sensitivity
Sophie Stoffer and Kathrine N. Brejnrod Side 3
Abstract
Nowadays there is a considerable knowledge and focus on how daylight increases our
physical and mental well-being as well as our performance (HeschongMahone Group, 1999).
Gradually the focus is increased on, how to bring daylight into buildings and at the same
time taking the building's overall energy consumption and indoor climate into account.
The current report has assessed and evaluated the sensitivity of different daylight metrics
with the aim to create a basic for a recommendation for a common daylight metric for the
European Daylighting Standard. The assessment contains an evaluation of advantages and
disadvantages of each metric, including the challenges by moving from a static to a dynamic
climate-based daylight metric.
The sensitivity of the various daylight metrics is investigated via a parametric analysis
according to different design alternatives and conditions, to create an image of their
sensitivity towards different parametric changes. Changes in different design parameters as
well as changes in orientation and location are taken into account in the evaluation.
The study showed that the dynamic metrics shows great variations on both location- and
orientation changes as well as facade and window changes such as window area and shading
devices. The static metrics, the DF, on the other hand does not changes according to
orientation and location and shows only minor variations towards facade and window
changes.
Together with the great sensitivity of the dynamic metrics, the energy consumption also
shows large variations when orientation, location and geometry is changed. The energy
consumption also showed to have a complex connection to the dynamic metrics which shifts
when orientation and location is changes.
It was found that the dynamic metrics was relatively simple to calculate, and a combination
of the dynamic metrics UDIcon and DAmax together with the effect on the energy consumption
is therefore preferable when evaluating the daylight conditions from different design options.

Chapter 1 -
#Table of Contents
1 Introduction ...................................................................................................................... 1
1.1 Research and background knowledge...................................................................... 3
1.1.1 Daylight Factor................................................................................................. 3
1.1.2 Daylight Autonomy.......................................................................................... 5
1.1.3 Continuous Daylight Autonomy ...................................................................... 5
1.1.4 Maximum Daylight Autonomy ........................................................................ 6
1.1.5 Useful daylight illuminance ............................................................................. 7
1.1.6 Continuous Useful Daylight Illuminance......................................................... 7
2 Method ............................................................................................................................. 9
2.1 Simulations and simulations tools............................................................................ 9
2.1.1 iDbuild ............................................................................................................. 9
2.1.2 Validation of LightCalc.................................................................................. 10
2.2 Hypothesis.............................................................................................................. 15
2.3 Reference room...................................................................................................... 16
2.3.1 Geometry........................................................................................................ 17
2.3.2 Materials......................................................................................................... 17
2.3.3 Internal thermal loads..................................................................................... 18
2.3.4 Climate control............................................................................................... 19
2.3.5 Energy Data.................................................................................................... 20
2.3.6 Weather data................................................................................................... 20
2.4 Parameter variations............................................................................................... 20
2.4.1 Location ......................................................................................................... 21
2.4.2 Orientation ..................................................................................................... 25
2.4.3 Room depth.................................................................................................... 26
2.4.4 Window area .................................................................................................. 26
2.4.5 Window element ............................................................................................ 26
2.5 Description of graphs ............................................................................................. 28

Chapter 1 - Introduction
3 Results and discussion.................................................................................................... 30
3.1 Parameters.............................................................................................................. 30
3.1.1 Locations........................................................................................................ 30
3.1.2 Orientations.................................................................................................... 33
3.1.3 Room depth.................................................................................................... 36
3.1.4 Window width – South................................................................................... 37
3.1.5 Window width – West.................................................................................... 41
3.1.6 Window width – North................................................................................... 43
3.1.7 Window element – South............................................................................... 45
3.1.8 Window element – West and East.................................................................. 48
3.2 Ease and accessibility of the dynamic metrics....................................................... 50
3.2.1 Calculation time ............................................................................................. 50
3.2.2 Amount and complexity of input data............................................................ 50
3.3 Sensitivity of the daylight metrics.......................................................................... 53
4 Conclusion...................................................................................................................... 55
5 References ...................................................................................................................... 56
6 Computer programs........................................................................................................ 59

Recently it is decided to use the European standard EN 12464-1, Lighting of Workplaces, in Denmark
instead of DS 700.
1 Introduction
Nowadays there is a considerable knowledge and focus on how daylight increases our
physical and mental well-being as well as our performance (HeschongMahone Group,
1999).Gradually the focus is increased on, how to bring daylight into buildings and at the
same time taking building's overall energy consumption and indoor climate into account.
Current architectural building traditions often results in buildings with large glass facades
why a large percentage of the facade relative to the floor area is glazing. For non-residential
buildings this often results in overheating and increased energy consumption for cooling in
summer, since larger windows results in an increase in direct sunlight and passive solar gain
in the hours of occupancy.
From a light quality and energy perspective, is it not necessarily desirable to have a huge
amount of daylight entering a room, but more importantly to get usable daylight into the
building. Meaning that there must be taken into account, if the daylight entering to room is
sufficient or optionally too much.
This infers the question, what usable daylight is and how to evaluate this?
Although daylight is universal, the understanding and use of it is not always the same.
Daylight is often defined and used differently by different groups. Table 1 shows a list of
definitions for daylighting, used for a survey about use of daylighting in sustainable building
design made by Reinhart and Galasiu in 2006.
Profession Daylighting definition
Architectural The interplay of natural light and building form to provide a
visually stimulating, healthful, and productive interior
environment
Lighting and Energy
Savings
The replacement of indoor electric illumination needs by
daylight, resulting in reduced annual energy consumption for
lighting
Building Energy The use of fenestration systems and responsive electric
lighting controls to reduce overall building energy

Side 2 Daylight metrics and their sensitivity
Sophie Stoffer and Kathrine N. Brejnrod
Consumption requirements (heating, cooling, lighting)
Load Management Dynamic control of fenestration and lighting to manage and
control building peak electric demand and load shape
Cost The use of daylighting strategies to minimize operating cost
and maximize output, sales, or productivity
Table 1 Defintion of Daylight regarding different professions and (Christoph, et al., 2006)
Today the daylight conditions in a building are often assed on the basis of the Daylight
Factor (DF). DF evaluates the daylight level at a point in comparison to the daylight level
outside on an overcast day. DF is a static metric that does not take current weather
conditions, location and orientation as well as direct sunlight into account.
Over the last years different alternative dynamic daylight metrics have been proposed. A
dynamic daylight metric means that they take location, orientation, weather and direct
sunlight into consideration. But none of them has become common use and in relation to
standards it is still only a recommendation to assess daylight in buildings according to the
DF.
The current report will assess and evaluate the sensitivity of different daylight metrics with
the aim to create a basic for usable daylight metric for the European Daylighting Standard.
The assessment will contain an evaluation of advantages and disadvantages of each metric,
including the challenges by moving from a static to a dynamic climate-based daylight metric.
In this report the following daylight metrics will be evaluated:
Daylight Factor (DF)
Continuous Daylight Autonomy (DAcon)
Continuous Useful Daylight Illuminance (UDIcon)
The sensitivity of the various daylight metrics will be investigated by a parametric analysis
according to different design alternatives and conditions, to create an image of their
sensitivity towards different parametric changes. Changes in different design parameters as
well as changing orientation and location will be taken into account when evaluating.

1.1 Research and background knowledge
1.1.1 Daylight Factor
The daylight factor (DF) is the ratio, usually in percentage, between the external horizontal
illuminance and the internal illuminance at a point in a building, and is only calculated under
overcast sky conditions (standard CIE overcast sky). Since the DF is determined under the
CIE standard overcast sky condition it does not take direct sunlight into consideration.
Calculating with standard CIE overcast sky distribution also means that DF does not take the
building's orientation and location into consideration, while the standard CIE overcast sky is
symmetric about the vertical axis and therefor shows no differences in sky conditions
between i.e. north and south. Nor does DF take the building location in consideration as DF
simply is the ratio between the indoor illuminance and the outdoor illuminance (Mardaljevic,
et al., 2009).
The DF is therefore the same for a given geometry regardless time of day, year or geographic
location, and is therefore defined as a static daylight metric.
Usually DF is calculated by dividing the inside illuminance on a horizontal plane at working
height (0,85m) and the horizontal illuminance on the roof (and multiplying by 100). Since
DF is the ratio between the internal and external illuminance it tells something about the
light transmitting properties of the glazing, but is also influenced by outside nearby
obstructions. This means, that DF is a total amount at a given point in a room for the daylight
coming into the room directly through the window, reflected daylight from other surfaces
inside the room and outside the window (Johnsen, et al., 2008).
Currently the recommended minimum DF at a working place is 2%, which is equivalent to
200 lux with an overcast sky of around 10,000 lux.
1.1.1.1 The daylight factor as an evaluation tool of daylight conditions
Currently daylight conditions are mainly evaluated after the DF. But the DF definitely has
some limits and is maybe not up to date regarding nowadays available calculation tools and
the more strict building requirements.
DF was proposed for the first time in the UK in 1909 by Waldram as a sky factor, and was
developed as a measurement technique to calculate the contribution of direct light from a sky
dome to a point inside a building. The reason to use the ration between internal and external
illuminance was to avoid difficulties by calculating with frequent variations in the intensity
of daylight. In 1950 Waldram developed it into the daylight factor which also included the

reflected light from external obstructions and internal surfaces, and the light loss through the
glazing (Christoph, et al., 2006). Approximately at the same time (1949), the uniform
reference sky changed to the CIE overcast sky, defined by Moon and Spencer (1942).
At the beginning DF was primarily used in court as a legal evidence to qualify access to a
minimum of daylight. In an old Roman law (1832) it was written, that the rights to light is
granted to the window (Christoph, et al., 2006) . Taking this to account DF does not
necessarily support good daylight conditions but only a minimum of daylight in spaces based
on requirements to the light.
The question is, if it is sufficient to calculate the daylight distribution in a room from the
minimum illumination conditions in a room?
Daylight is not static but dynamic in nature and is constantly changing in intensity and
patterns because of the variability in the sun and sky. As the daylight is dependent of the sun
and sky conditions as well are the daylight conditions in a space too, and is always in
changing relatively to the outside daylight. Because daylight is dynamic and always
changing the daylight illumination and pattern distribution in a space are too.
The daylight factor is a static metric and does not take into account the changing in
illuminance conditions and spatial distribution – it is constant. It doesn’t even take the direct
sunlight into consideration. So it does not give a picture about possible problems like glare
or overheating caused by the direct sunlight penetrating through the window. The daylight
factor does not take the orientation, location and the time of the day or year into account.
This means that the results for a given geometry will be the same whether the windows are
oriented to the north, south, east or west. It will not even have an impact on DF whether the
calculations are carried out in the northern or southern Europe. This is not consistent with
reality, where one would expect a much higher risk for direct sunlight, glare and overheating
in southern Europe than northern.
Nowadays there are quite different available to tools for calculating the daylighting in
buildings than at the time the DF was developed. Therefore, it is no longer impossible to
make a dynamic evaluating of daylight conditions. A dynamic daylight evaluation takes the
actual conditions into account and makes it easier to develop usable daylight conditions in
spaces, why it would make sense to evaluate the illuminance in a space after dynamic
daylight metrics.

1.1.2 Daylight Autonomy
The Daylight Autonomy (DA) is a dynamic daylight metrics which is defined as the
percentage of a year where the minimum illuminance in occupancy is fulfilled by only
daylight. The DA uses the Illuminance at the work plane to assess whether the daylight is
sufficient to allow the users to work only with daylight. The recommended light levels are
available in applicable standards, e.g. EN 12464-1, Lighting at Workplaces. Illuminance
below the minimum threshold is not taking into consideration in the DA. DA is calculated
with dynamic sky conditions, so it takes sun angle, geographic locations and direct sun in to
considerations on an annual basis.
The definition of DA goes back to at least 1989 where it is mentioned in the Swiss norm,
Assosiation Suisse des Electriciens, as a function of daylight factor and required
Illuminance. In 2001 DA got redefined (by Reinhardt and Walkenhorst) as a percentage of
occupied times of the year where the required illuminance at a point in the building is
maintained only by daylight, since time outside working hours is not in interest to the
building users. In 2006 DA got further improved by Reinhardt and Andersen to consider the
use of manual shading devices and predict the position of them at all time steps at the year.
(Christoph, et al., 2006).
Figure 1 Daylight Autonomy
1.1.3 Continuous Daylight Autonomy
The previously described DA does not take illuminance into account if they are just below
the required threshold, though the users might consider the daylight conditions as acceptable
or with only little complementary electrical lighting. The continuous Daylight Autonomy
(DAcon) is a modification of DA which takes partial the illuminance under the minimum
threshold in to considerations and is ranges in percentage.
I.e. if the threshold for the illuminance requires 100 lux and 50 lux are provide by daylight at
a given time step, the partial credit for that time step would be 50 lux/100 lux = 0,5. By
given a partial credit to at time step where daylight illuminance does not meet the required
threshold, DAcon gives a more realistic evaluation for the daylight conditions in the building
and softens op the boundaries for the given thresholds.

There have been different approaches to the rating of DAcon. Mardaljevich recommended
concentrating on work planes, so daylight only considers as appropriate if all the sensors at
the working area are in the recommended range between 100 lux and 2000 lux, which
resulted in the metrics called UDI, described below.
Rogers recommended evaluating DAcon in levels above either 40 percentages, 60 percentage
or 80 percentage (Christoph, et al., 2006).
Figure 2 Continous Daylight Autonomy
1.1.4 Maximum Daylight Autonomy
The Maximum Daylight Autonomy (DAmax) is used together with DAcon to consider the
likelihood of potentially glare conditions and indicate the magnitude of the illuminance
contrast in a room and how often it appears. DAmax indicate the percentage of the occupied
time where direct sunlight or high daylight conditions are present. In 2006 Rogers proposed
DAmax to be a sliding illuminance equal to ten times the design illuminance of a room.
Rogers also proposed that the percentage level for DAmax at a work plane should not exceed
more than 5 percentage of the time, to avoid daylight conditions with too much direct
sunlight (Christoph, et al., 2006).
Figure 3 Maximum Daylight Autonomy

1.1.5 Useful daylight illuminance
The Useful Daylight Illuminance (UDI) is a dynamic daylight metrics indicating in
percentage when the daylight is acceptable for the users at a horizontal working plane;
neither too dark (<100 lux) nor too bright (>2000 lux). UDI is actually indicated as three
metrics that show the percentage of occupied times of the year:
UDI < 100 lux : Too dark and are generally considered insufficient
100 lux ≤ UDI ≤ 2000 lux : Useful
o Daylight illuminance in the range of 100 lux – 500 lux are considered
as effective
o Daylight illuminance in the range of 500 lux – 2000 lux are often
perceived either as desirable or at least tolerable
UDI > 2000 lux: Too bright and are likely to produce visual or thermal discomfort,
or both.
By dividing the thresholds into three metrics, it specifies that a situation with too much light
can be just as intolerable as a situation with to low illuminance. The last metrics is to
indicate any potential risk of glare or overheating.
Like DA, UDI is only given credit for the values for the accepted range, which in this case
means the range between 100 lux to 2000 lux. The upper the upper threshold at 2000 lux is
still on debating. (Nabil, et al., 2005)
Figure 4 Useful Daylight Autonomy
1.1.6 Continuous Useful Daylight Illuminance
The Continuous Useful Daylight Illuminance (UDIcon) is a modification of UDI and just like
DAcon it takes the illuminance under the minimum threshold into considerations, and is
expressed in percentage. I.e. when the illuminance from the daylight is 50 lux at a given time
step and the minimum threshold is 100 lux, it will still contribute with an amount of
illuminance which should be taken into consideration. In this case the partial credit for that
time step would be 50 lux/100 lux = 0.5. By given a partial credit to at time step where

daylight illuminance does not meet the required threshold, UDIcon gives a more realistic
evaluation for the daylight conditions in the building and softens op the boundaries for the
given thresholds. (Nabil, et al., 2005)
Figure 5 Contionous Daylight Autonomy

Chapter 2 - Method
Recently it is decided to use the European standard EN 12464-1, Lighting of Workplaces, in Denmark instead
of DS 700.
2 Method
The aim of the project is to study different daylight metrics sensitivity through a parametric analysis
together with a short evaluation of the difference of time and complexity of the calculation of the
static and the dynamic metrics. The parametric study is carried out for a simple single-façade office
room with one window, and the project is divided into five phases:
Literature review and knowledge collection
Hypothesis of daylight metrics behavior and their sensitivity
Validation of the desired simulation program, iDbuild
Parameter preparation and analysis
Communication and layout of parametric data
The different parameters and hypotheses are described in the chapters of parameter analysis and
hypotheses. The following computer simulations programs are used to assess the different dynamic
daylight metrics and daylight factor.
iDbuild: Daylight, thermal and energy calculation
DAYSIM: Daylight calculations to validate the calculations in iDbuild
2.1 Simulations and simulations tools
Daylight and indoor climate calculations will be carried out and evaluated according to European
standards
For the reference room and the following parametric studies the UDIcon and DAcon is simulated with a
minimum threshold at 200 lux, since this is the required illuminance at work plane (according to the
Danish Standard DS 700), and an upper threshold for UDIcon at 2000 lux.
2.1.1 iDbuild
The simulations in this study are carried out using the simulation tool iDbuild. iDbuild is an hourly
based simulation tool initially developed at the Technical University of Denmark (DTU) as a part of a
PhD-thesis (Petersen, 2011). The current version used in this study is iDbuild 2014a, and the program
is currently maintained and developed by staff and students at DTU and Aarhus University.

Chapter 2 - Method
iDbuild is developed as a tool in an integrated design process of low-energy buildings, and provides
energy- and daylight simulations as well as calculations of the thermal and atmospheric indoor
environment.
iDbuild is developed as an early-stage design tool, why the input side and calculation speed is kept
simple without trading off the accuracy level needed in the design phase.
2.1.2 Validation of LightCalc
To ensure that iDbuild's daylight calculations are valid, the daylight calculations will be validated
against the program DAYSIM. iDbuild is currently validated in accordance to Radiance when it
comes to the daylight factor and illuminance (Hviid, et al., 2008). But in the case of the dynamic
daylight calculations an official validation is not available.
This section therefore provides a comparison of the dynamic daylight metrics calculated with the tool
iDbuild 2014a to the dynamic daylight metrics calculated with the validated tool DAYSIM 3.1.e
(beta).
The uniformity of the model build-ups in respectively iDbuild and DAYSIM will be determined
through calculations of the daylight factor. When the uniformity of the two models is established, the
dynamic metrics will be compared. The errors displayed are specified in percentage points instead of
relative errors, since the metrics are already expressed in terms of percentage and a relative error
would therefore be deceptive.
2.1.2.1 Model
2.1.2.1.1 Reference room
The room set up as reference for the validation is a single-facade room with a standard two-layer
glazing, 4-15Ar-SN4, situated in Copenhagen, Denmark. The build-up corresponds to the one in the
article “Simple tool to evaluate energy demand and indoor environment in the early stages of building
design” (Nielsen, 2005) , and the specifications appears in Figure 6.

Chapter 2 - Method
Room geometry
Height 3000 mm
Width 4000 mm
Depth 6000 mm
Facade orientation West
Window geometry
Height 1600 mm
Width 2000 mm
Offset floor 900 mm
Offset wall 1000 mm
Wall depth 0 mm
Occupancy hours 8-17
Location Surfaces
Copenhagen, Denmark Wall 0,7 -
Longitude 12,19 ⁰ Ceiling 0,8 -
Latitude 55,4 ⁰ Floor 0,3 -
Time meridan 15 ⁰ Albedo 0,2 -
Window Glazing
Uw [W/m2
K] 1,19 W/m2
K 4-15Ar-SN4
gw [-] 0,62 - gg 0,63 -
Frame width [m] 0,001 m Ug 1,19 W/m2
K
Inner surface
reflectance
0,215 -
Figure 6 Refernce model for validation
2.1.2.1.2 Simulation input
The simulation settings used for the DAYSIM calculation appears in Figure 7. The simulation settings
should provide a satisfyingly accuracy for the current case, and is according to the simulation scenario
“Medium” in the Master’s thesis “Daylight in Urban environments” (Momme, 2013).
ab ad as ar aa lr st sj lw dj ds dr dp
10 1024 512 600 0,05 10 0,05 1 0,003 0 0,2 2 512
Figure 7 Simulation settings for DAYSIM calculations

Chapter 2 - Method
When evaluating the simulated data, a minimum threshold of 100lux and a maximum threshold of
2000lux are used.
2.1.2.1.3 Angle dependent transmittance
iDbuild comes with a glazing database where the angle dependent transmittance is pre-generated
using WIS, but it is also possible to manually type in the transmittance values. In DAYSIM the angle
dependent visual transmittance is determined in accordance to Radiance which differs from the WIS-
data. To create a comparable basis the transmittance values in accordance with Radiance is therefor
used in both iDbuild and DAYSIM simulations. The angle dependent transmittance of the particular
glazing used in the model appears in Table 2.
0 10 20 30 40 50 60 70 80
Angle [⁰] 0,749 0,748 0,745 0,739 0,726 0,699 0,639 0,504 0,235
Table 2 Angle dependent transmittance with 0⁰ being perpendicular to the glazing
2.1.2.2 Validation of model
As described in section 2.1.2 correctness of the model build-up is assessed through a comparison of
the DF calculated in respectively iDbuild and DAYSIM.
Figure 8 shows the DF in the centerline of the room calculated in respectively iDbuild and DAYSIM.
As the figure shows, the curves and levels are almost identical all the way through the room, only
with a minor deviation closest to the window.
Figure 8 Comparison of the daylight factor calculated in respectively iDbuild and DaySim
Since the error on the iDbuild simulation has a maximum of 1,1pp, the deviations are assumed to be
insignificant and negligible, and the model build-ups established as identical.

Chapter 2 - Method
2.1.2.3 Validation of dynamic metrics
As described in section 0, the DAcon expresses the percentage of the occupancy hours where the
illuminance is above a minimum threshold, in this case 100lux, but with partial credit to the
illuminance below the threshold. From Figure 9 the DAcon in the centerline through the room is
displayed together with the error stated in percentage points.
Figure 9 Comparison of the continous daylight autonomy calculated in respectively iDbuild and DAYSIM
From Figure 9 it shows how the curve of DAcon is almost identical through the room and the error is
therefore fairly stable, but it also shows how the actual level of the DAcon differs from the iDbuild
calculation to the DAYSIM calculation. The error on the iDbuild results compared to DAYSIM is
therefore seen to lie between 5-10pp.
The error on DAcon is still considered acceptable although remarkable higher than for DF, and the fact
that the error is quiet stable results in a DAcon that is almost identical to the one calculated in
DAYSIM just shifted 10pp upwards.
When continuing onwards to the second dynamic metric, DAmax, the comparison of the resulting
metric from the two simulation programs shows in Figure 10. The upper limit is set to 2000lux, and
from the figure the error between the two calculations also appears.

Chapter 2 - Method
Figure 10 Comparison of the maximum daylight autonomy calcuated in respectively iDbuild and DAYSIM
When looking at the curves of DAmax it shows that iDbuild and DAYSIM agrees on the overall
course. In the front and in the back of the room they are almost identical, but with higher deviations in
the middle of the room. When dealing with DAmax, if any, an error in the middle of the room is to be
expected, since this will be the transition area from illuminance way above 2000lux to illuminance
way below 2000lux. The deviations on DAmax from iDbuild to DAYSIM are found to be acceptable,
but a more unified course between the two would have been desirable.
When looking at the UDIcon, the errors from the previous two metrics will influence on the
calculation, since UDIcon consists of a combination of the two, DAcon and DAmax. From Figure 11 the
difference between UDIcon calculated with respectively iDbuild and DAYSIM is illustrated.
Figure 11 Comparison of the continous useful daylight illuminance calculated in respectively iDbuild and DAYSIM
Overall the UDIcon-curves are seen to be relatively identical, but as expected the error is more
significant in the middle of the room, due to the deviation on the DAmax results. The deviations are

Chapter 2 - Method
seen to be somewhat higher than for the two previous metrics, which as described comes from the fact
that UDIcon is a combination of the two.
The current study does not focus on specific illuminance but on relative changes from different
parameter variations. Therefore the relative uniformity of the metrics is the most important factor
compared to the actual level which is found less important. The relative uniformity of the course is
found to be adequate for the current study both in terms of the static daylight metric, DF, and the
dynamic metrics, DAcon, DAmax and UDIcon, why the following study is proceeded using the daylight
calculations carried out in iDbuild 2014a.
2.2 Hypothesis
In the figures below is showed how the graphs of UDIcon is expected to behave for the different
locations to the orientations, east, south, west and north.
Tabel 1: Hypothes of behaviour of the UDIcon for the different location to the orientations, east, south, west and north.
The daylight factor will be the same for all locations and orientations since it is calculated for a CIE
overcast sky.
Generally, is it expected that for all locations UDIcon will be highest at the back of the room when the
window is facing south, due to a general increase solar intensity for south compared to the other

Chapter 2 - Method
orientations.To the north UDIcon will be lower both in the front of the room and in the back of the
room than towards the other orientations, due to the northward only is diffuse light.
Generally, is the UDIcon line will be lowest in front of the room for all orientations due much of the
time the lux level here will be above 2000 lux (DAmax).
Rome is expected to have the highest UDIcon in the back of the room for all four orientations. At the
front of the room Rome’s UDIcon will be lower when the window is facing south, because of the direct
sunlight.
Kiruna is expected to be lower in front of the room compared to Berlin and Copenhagen while UDIcon
for Kiruna will become reduced from lux values above 2000 lux as well as lux levels below 200 lux.
In the back of the room will Kiruna have the lowest UDIcon value due to a higher UDIcon <200.
Copenhagen will likely have a higher UDIcon in front of the room than Berlin because of lower DAmax
(UDIcon> 2000). In the back of the room will Copenhagen have a lower value than Berlin since
Copenhagen will have a bigger value for UDIcon <200. To see graphs showing hypotheses for the
other parameter variations, see Appendix 2.
2.3 Reference room
As basis for the parametric study, a room is build up as a reference. All parameter variations are
carried out based on the reference room, and the following is a definition of this reference.
Building traditions often leads to offices
designed with access to daylight from only
one façade (
Figure 12) .
The reference model is therefore based on
an office located in the middle of a building
with storeys above and below and only one
façade.
This example is considered as the worst case
for the indoor environment since the
insulation requirements often leads to
overheating problems.
Figure 12 Sketch of placement of the reference room
in a building.
The room is therefore modeled with no heat exchange between the adjacent rooms, so the adjoining
rooms have the same thermal indoor environment as the modeled office room. Possible overheating is

Chapter 2 - Method
evaluated after cooling needs why the cooling effect is set to "infinite" high, and the air change is
constant to clarify the cooling load. The reference room is not simulated with surrounding
constructions.
2.3.1 Geometry
The reference model is intended as a one-man office with dimensions of 6.0 m × 3.0 m, a total floor
area of 18 m2
and an interior ceiling height of 3 m, see Figure 13 and Table 3.
Room geometry
Height 3000 mm
Width 4000 mm
Depth 6000 mm
Facade
orientation
West
Window geometry
Height 1600 mm
Width 2000 mm
Offset floor 900 mm
Offset wall 1000 mm
Wall depth 0 mm
Figure 13 Sketch of the reference room. All numbers are in
meters.
Table 3 Room and window geometry
The reference room is modeled having one window with dimensions of 1.8 m × 2.0 m and 0.8 mm
above floor level. The window and parapet height is kept constant throughout all the parametric
variations.
2.3.2 Materials
From Table 4 the material properties are displayed for the façade materials, the interior and exterior
surface reflectance together with the properties of the window and glazing used in the reference room
simulations.

Chapter 2 - Method
Façade Materials Surfaces Properties
U-value: 0,15 W/m2
K Wall 0,7 -
Thermal capacity: Middle heavy Ceiling 0,8 -
Floor 0,3 -
Albedo 0,2 -
Window Glazing
Uw 1,42 W/m2
K 4-15Ar-SN4
gw 0,62 - gg 0,63 -
Frame width 0,001 m Ug 1,19 W/m2
K
LT 0,78 -
Table 4 Façade materials, surface properties and window and glazing properties.
2.3.3 Internal thermal loads
The reference model is simulated with an internal load equivalent to one person who has an activity
equal to 1.2 met with a variable clothing level and an assumed effect on equipment equivalent to 100
W. The occupancy is set to five days a week between 8 am to 17 am.
Internal Thermal Loads
Number of people 1 person
Longtitude 1,2 met
Lattitude 100 W
Time meridan Kl. 08:00- 17:00
Table 5 Internal thermal loads
The general illumination level in the space is simulated with a power of 7 W/m2
and is continuous
daylight controlled by a horizontal illuminance of 200 lux at desk height (0.85 m), and task lighting of
1.7 W/m2
with on /off control by 500 lux.

Chapter 2 - Method
General Lighting Task Lighting
Min power: 0,5 W/m2
Min power 0 W/m2
Max power: 6 W/m2
Max power 1 W/m2
W/m2
/100 lux: 3 W/m2
/100 lux 0,2
Continuous control On/off control
Table 6 Data input for general and task lighting
For the reference room and the following parametric studies the UDIcon and DAcon is simulated with a
minimum threshold at 200 lux, since this is the required illuminance at work plane, and an upper
threshold for UDIcon at 2000 lux.
2.3.4 Climate control
The reference model is based on a thermal zone with mechanical air supply and exhaust. The
ventilation in the occupancy is set to fulfill the indoor air quality class II according to the European
standard DS/EN 15251. The ventilation system is CAV with a cooling and heating coil and a constant
air flow of 1.09 l / s per m2 for occupancy and 0.7 l / s per m2 outside the occupancy. Set point for
heating in the occupied time is 20°C and for cooling 26°C. There is no mechanical cooling outside
occupancy hours, during the winter or at night time. The ventilation system is assuming a heat
exchanger efficiency of 0.85.
Since the overheating is evaluated from the cooling load the cooling effect is set to an unrealistically
high level of 1000 W/m2
, why the room temperature never will exceed 26 o
C.
Mechanical CAV Ventilation Thermal Indoor Environment
Infiltration 0,1 l/s per m2
Heating setpoint 20 o
C
Airchange time in use 1,09 l/s per m2
Cooling setpoint winther 26 o
C
Airchange outside use 0,7 l/s per m2
Cooling setpoint
summer
26 o
C
Heat exchanger
efficiency:
0,85
Mechanical cooling -1000 W/m2
Infiltration 0,1 l/s per m2
Table 7 Left colum shows data for the mechanical CAV ventilation and right colum shows the setpoint for the thermal
indoor environment.
The simulations do not take any use of natural ventilation into account.

Chapter 2 - Method
2.3.5 Energy Data
Table 8 shows the input for the energy data for the reference room. The energy factor for the
simulations is 1,0 for both heating and electricity.
Energy Data
Mechanical ventilation, SFP 1 kJ/m2
COP, heating 1 -
COP, cooling 2,5 -
Hot water 10000 Liter/m2
Table 8 Energi data for mechanical ventilation, energy supply system and hot water.
2.3.6 Weather data
The weather data for the simulations for the different locations is downloaded at the homepage for
U.S. Department for Energy (U.S Department for Energy) and converted with an epw-converter.
2.4 Parameter variations
The following section is a description of the different parameters from which the sensitivity of the
daylight metrics is evaluated. As described earlier the reference room will form the basis of all
parameter variations.
The parametric analysis will be conducted for different orientations and locations together with the
parameters listed below:
Various room depths
Various window width
Various window elements (solar coated glazing, overhang and internal- and extrernal
shading)

Chapter 2 - Method
2.4.1 Location
Four different European locations have been chosen as basis for the study. The locations are selected
to represent the diversities in the European climate together with their diverse geographical
placement. The four locations used in the study appear in Table 9.
City Country Area
Kiruna Sweden Northern Europe
Copenhagen Denmark Northern/central Europe
Berlin Germany Central Europe
Rome Italy Southern Europe
Table 9 European locations used in the study
All parameter variations are simulated for all four geographical locations, since the different locations
are of great importance to the parametric analysis.
Since the climates at the four locations have large variations, the following section is a representation
of the different climate and geographical attributes of the four cities. Summertime is not accounted for
in any calculations carried out in this study.
2.4.1.1 Kiruna, Sweden
Kiruna is situated on the 67,82th
latitude and the 20,33th
longitude, and its geographical location
results in a minimum and maximum sun angle on respectively -1,32⁰ and 45,68⁰.
The climate in Kiruna is defined as a
Continental Subartic Climate, and the
average temperature over the year is 1,1⁰C.
The winter in Kiruna is long and cold and
together with the low minimum sun angle
this means also very short days. But the
winter days are mostly clear and with
relatively little precipitation mostly in the
form of snow. The humidity is low during
the winter. The coolest month of the year is
December with an average of -13,3⁰C
Figure 14 Sun path Kiruna Sweden

Chapter 2 - Method
The summers are short and mild and have on the contrary long days. The summer months has the
most part of the yearly precipitation, and due to the low temperatures, the annual frost free period is
only 50-90 days. The warmest month of the year is July with an average of 12,2⁰C.
(weatherbase.com)
Figure 15 Climate data Kiruna, Sweden (worldclimateguide.co.uk)
2.4.1.2 Copenhagen, Denmark
Copenhagen is situated on the 55,4th
longitude, which results in a minimum
and maximum sun angle on respectively 11,1⁰ and 58,1⁰.
The climate in Copenhagen is defined as a
Marine West Coast Climate. The climate is
characterized by equable climates where
there are few extreme temperatures and with
plentiful precipitation. The precipitation is
almost evenly distributed over the year.
The average temperature over the year is
8,3⁰C, and the warmest month is July with
an average of 16,7⁰C, and the coolest month
is February with an average of 0⁰C.
(weatherbase.com)
Figure 16 Sun path Copenhagen, Denmark

Chapter 2 - Method
Figure 17 Climate data Copenhagen, Denmark (worldclimateguide.co.uk)
2.4.1.3 Berlin, Germany
Berlin is situated on the 52,5th
longitude, which results in a minimum and
maximum sun angle on respectively 14⁰ and 61⁰.
As Copenhagen the climate in Berlin is
defined as Marine West Coast Climate with
the equable climate and only few extreme
temperatures. Compared to the climate in
Copenhagen the temperature range in Berlin
is a little wider, and especially in the
summer time with higher temperatures.
The yearly average temperature is 9,4⁰C, the
warmest month on average is July with an
average of 18,3⁰C and the coolest month
January with -0,6⁰C. (weatherbase.com) Figure 18 Sun path Berlin, Germany

Chapter 2 - Method
Figure 19 Climate data Berlin, Germany (worldclimateguide.co.uk)
2.4.1.4 Rome, Italy
Rome is situated on the 41,8th
latitude and on the 12,23th
longitude, which results in a minimum and a
maximum sun angle on 24,7⁰ and 71,7⁰.
The climate in Rome is defined as a
Mediterran Climate, where the average
temperature in the warmest month does not
go below 10⁰C and the average in the
coldest is between -3⁰C and 18⁰C. The
average yearly temperature in Rome is
15,5⁰C.
Compared to the three other locations, the
temperatures are remarkable higher, and
with temperatures that only rarely goes
below the freezing point. Figure 20 Sun path Rome, Italy
The summer tends to be dry with only minor precipitations compared to the winter months. The
warmest month is August with an average temperature of 23,6⁰C, and the coolest month is January
with an average temperature of 8,3⁰C. (weatherbase.com)

Chapter 2 - Method
Figure 21 Climate data Rome, Italy (worldclimateguide.co.uk)
2.4.2 Orientation
Due to a limited period of time for the project, some of the parametric variations will only be
simulated for the orientations wherein the different orientations will have the largest impact. For
example it only makes sense to simulate the overhang for a southern oriented room, as an overhang
only makes a difference for high sun angles.
In Table 10 the different parameter variations are listed, and weather the parameter is calculated for a
given orientation is marked with an x.
East South West North
Reference x x x x
Parameter variations
Room depth x
Window width x x x x
Solar coated glazing x x
Internal shading x x x
External shading x x x
Overhang x
Table 10 Orientations simulated for the different parameters

Chapter 2 - Method
2.4.3 Room depth
The different room depths listed below are simulated in the study:
4 meters
6 meters (reference model)
8 meters
The daylight conditions are not expected to change noticeably by the different room depths, looking at
the same distance into the room. Instead the changes in this parameter are expected to be seen in the
energy consumption per square meter, since the window percentage relative to the floor area will be
changed at different room depths. It is expected that a smaller room depth will increase the energy
consumption per square meter, since the window area will increase compared to the square meters.
2.4.4 Window area
The window area is also changed in the study, to identify the changes in connection to this. The
different sizes of the window with will be investigating for following:
1 m
2m (reference model)
3m
The height of the window and the parapet height are maintained for all the variations so it is only the
pane width that is changed. The reason for maintaining the parapet height for all calculations is
because a reduced parapet height only has an impact on daylight below the working height (0,8m).
Increasing the height of the parapet will not increase the amount of daylight in the working zone just
shift it further into the room.
The main effect on the daylight conditions of a changed window width is expected to be seen in the
front of the room, and near the side walls. A greater window area results in an increased solar gain
and thereby a risk for increased overheating and transmission losses.
2.4.5 Window element
To identify the influence of different window elements, the most common types are included in the
parametric analysis. From Table 11 the different window elements are listed together with their
specifications.

Chapter 2 - Method
Clear glazing (reference)
Glass unit 4-15Ar-SN4
Ug 1,19 W/m2
K
gg 0,63 -
LT 0,78 -
Solar coated glazing
Glass unit Pilkington Suncool Brilliant 6B(66)-15Ar-4
Ug 1,09 W/m2
K
gg 0,38 -
LT 0,66 -
Internal shading
Glass unit 4-15Ar-S(3)-5-WinDat#02 Internal light venet. blinds
Ug 1,13 W/m2
K
gg 0,63 -
LT 0,80 -
Slat distance 0,022 m
Slat width 0,025 m
Control Temp. And glare cut-off
External shading
Glass unit Hunter douglass 0150 light blinds-20Air-4-15Ar-SN4
Ug 1,19 W/m2
K
gg 0,63 -
LT 0,78 -
Slat distance 0,0425 m
Slat width 0,05 m
Control Temp. And glare cut-off
Overhang
Length 1000 m
Table 11 Specification for the different window elements

Chapter 2 - Method
2.5 Description of graphs
In the analysis, section 0, the results are illustrated in graph similar to Figure 23. The following is a
short reading instruction of that graph.
For each parameter variation a plot for each of the daylight metrics (UDIcon , DAcon, DAmax and DF) is
generated. An example of these plots is illustrated in Figure 22.
Figure 22: Plots for the different daylightmetrics. From left Continuous Daylight Illuminance (UDIcon), Continuous
Daylight Autonomy (DAcon), Maximum Daylight Autonomy (DAmax) and Daylight Factor (DF).
The graphs used in the analysis part are based on values for each 0.5 m in the center line in the room,
shown as a red line across the plots, see Figure 22. The graph, Figure 23, then illustrates by a line the
UDIcon through the room, with left being closest to the window. The coloured lines are UDIcon for the
four different locations (K = Kiruna, C = Copenhagen, B= Berlin and R=Rome) and the black line is
the DF (similar for all locations). UDIcon and DF are read on respectively the left and right y-axis.

Chapter 2 - Method
Figure 23: Graph for the different daylight metrics where the lines are UDIcon and DF. The gray bars show the
distribution of UDIcon for less than 200 lux, between 200-2000 lux and over 2000 lux.
Since a change in UDIcon can be caused by changes in the illuminance below the minimum or above
the maximum thresholds, the grey bars are applied to the graph. The gray bars show the distribution of
UDIcon. The different gray colours indicate whether it is UDIcon for less than 200 lux, between 200-
2000 lux or over 2000 lux (DAmax). On the gray bars DAcon can also be read as equivalent to UDIcon +
UDIcon> 2000.

Chapter 3 - Results and discussion
3 Results and discussion
3.1 Parameters
This section is a display and analysis of the results of the different parameter variations. The analysis
is based on the graphs described in section 2.5 and a display of the effect on the different parts of the
energy consumption. Daylight plots for the different metrics for all parameter variations appears in
appendix 4, and details on input an results for the individual parameter appears in appendix 3.
3.1.1 Locations
For the reference case the four locations is compared, to identify the impact on the different daylight
metrics. As shown in Figure 24 the reference case is evaluated in all four orientations, but the actual
effect of the orientations will be identified in the forthcoming section.
Daylight metrics in center line

Figure 24 Daylight metrics assed in the center line for the Reference case
As described earlier the DF is calculated for a CIE overcast sky, which does not take orientation and
location into account. The DF in the four graphs above is therefore seen to be identical, and only the
dynamic metrics varies.
Generally the UDIcon-lines are seen for all four locations at all orientations to be lower in the front of
the room and then increasing further into the room.
The UDIcon is seen to be highest in Copenhagen compared to the other three locations in the front of
the room. To the north, east and west this changes after approximately 2 meters into the room, where
Copenhagen then has the lowest value. To the south on the other hand UDIcon of Copenhagen is seen
to have a higher value than Kiruna and Berlin throughout the whole room length.
The reason why Copenhagen in generally is highest in the front of the room is, that it does not have as
high of a sun intensity as Rome and Berlin, which can be seen on DAmax in the charts. At the charts
the light gray columns is shown as UDIcon> 2000, see Figure 24.
On the contrary Kiruna has lower sun intensity than Copenhagen why UDIcon for Kiruna is lower than
Copenhagen in the front part of the room. This is also seen in charts where Kiruna generally has the
highest percentage of UDIcon <200 in front of the room. The reason why Copenhagen facing south has
a higher UDIcon in the back of the room compared to Berlin and Kiruna, is that it has a lower altitude

than Berlin why the direct sun goes further into the space in Copenhagen. Kiruna however, has a
lower altitude than Copenhagen, but not as many hours of sunshine nor as high sun intensity, which
results in a higher UDIcon <200 for Kiruna.
For all orientations Rome has the lowest UDIcon in front of the room and the highest in the back of the
room. This is caused by DAmax (UDIcon> 2000) which is predominant in front of the room because of
Rome's high sun intensity. The high sun intensity implies that Rome has the highest amount of
daylight in the back of the room for all four locations and therefore the highest UDIcon.
To the south the UDIcon-line for Rome is different than at the other three orientations, see Figure 24.
UDIcon goes from a value of 0 in the front of the room to a value of 100 in the middle of the room. The
sudden transition from 0-100 takes place where the direct sunlight stops coming into the room.
Figure 25 Energy consumption for the Reference case - Locations
In the graph showing the energy consumption, see Figure 25, can be seen that Copenhagen and Berlin
are generally very similar with respect to the energy consumption. However, Berlin has a higher use
for cooling. Generally Kiruna has the highest energy consumption due to heating needs.
The graph shows that the two northern locations (Kiruna and Copenhagen) have the highest energy
consumption facing north due to increased heating needs, while Rome has a lower energy use due to
reduced cooling needs.
Kiruna generally have higher energy consumption for lighting than the other locations. Berlin and
Rome has slightly lower energy use for lighting facing south due to a general increase in daylighting
level. For Kiruna and Copenhagen the energy use for lighting is more or less the same against the
different orientations.
Rome's energy consumption is primarily for cooling and hot water, which means that there is almost
no use for electric lighting and heating, which the dynamic metrics also implies due to the non-
existing hours below the minimum threshold.

3.1.2 Orientations
To assess the orientation-dependency of the daylight metrics, the metrics for the four orientations are
held together for each individual location for the reference case.

Figure 26 Daylight metrics assed in the center line for the Reference case
In general the comparison shows that for all four locations UDIcon is highest for the northern
orientation in the front end of the room. At the same time the energy consumption is seen to rise at the
northern orientation in the locations Kiruna and Copenhagen due to increased heating demand. On the
contrary in Berlin only the southern oriented room has lower energy consumption than the northern,
and in Rome the northern oriented room is seen to have the lowest energy consumption compared to
the three other orientations.
Around the midpoint of the room where the direct daylight fades out the UDIcon for the different
orientations changes, thus the northern orientations has the lowest UDIcon in the rear end of the room.
Rome is an exception since all orientations have the same UDIcon in the rear end since the DAcon is
only reduces with DAmax and not with hours below the minimum threshold. As the graphs clearly
shows, the difference between the useful daylight at the different orientations is much greater in the
front half of the room than in the back half of the room.
In general Rome has a 100% UDIcon in the backend of the room in contrary to the three other locations
where the UDIcon ranges between 70-90%. As mentioned for the northern orientation in Rome, the
DAcon is only reduced with DAmax since there are no hours below the minimum threshold due to the
high solar intensity in Rome. This also applies to the three other orientations in Rome why the UDIcon
is 100% in the back end of the room where the direct sunlight is excluded.
In contrast to the northern orientations for all locations, the southern orientations show that the UDIcon
is the lowest of all orientations in the front end of the room, due to the high amount of hours above the
maximum threshold.
For the eastern and western locations it shows that UDIcon in the front half of the room is highest for
the eastern location, which is most significant in Kiruna, but for the rear end of the room UDIcon for
the two orientations is almost identical.

Figure 27 Energy consumption for the reference case - Orientations
From Figure 27 it shows that the total energy consumption for Rome mainly consists of the need for
cooling, and only against northern orientations there is a need for heating. Compared to the other
locations the energy consumption for Rome is in overall lower but with a remarkably higher need for
cooling than the other locations. The need for electric lighting is almost also non-existing in Rome
compared to the other locations.
On the other hand Kiruna is seen to have a remarkable higher energy use for heating due to the
extreme climate situation, and almost no cooling need at all.
In general there is not a clear correlation between a high UDIcon and lower energy consumption or the
opposite. But it shows that the relation between the amount of useful daylight for the four orientations
and the energy consumption varies for the different locations.

3.1.3 Room depth
Two different room depths are assessed, apart from the room depth in the reference case (6m), mainly
to identify the influence on the energy consumption. The room depth is only evaluated and analyzed
for a southern orientation.
Figure 28 Daylight metrics assed in the center line for the Room depth - South
As expected the daylight conditions are almost identical for the different room depths, e.g. the first 6
meters of the graph with 8 meters room depth is identical to the same 6 meter of the reference, see

Figure 28. After the first 6 meters the UDIcon has a slightly steeper decline the last 2m in the case with
the room depth of 8 meters. The similarities are for both the static and the dynamic metrics.
The difference on the three different room depth scenarios is on the other hand seen when looking at
the energy consumption. From Figure 29 the energy consumption of the three room depths shows.
Figure 29 Energy consumption for Room depth - South
Generally, there is an increased cooling demand by the 4 meter room depth compared to the reference
room with a room depth of 6 meters. This is due to the relatively larger window area compared to the
floor/room area. The increased window area compared to the floor area, results in a higher cooling
need at all four locations (especially in Rome) and for Kiruna it also results in a remarkable increase
in the heating demand.
Conversely the cooling needs for Rome, Berlin and Copenhagen is reduced at the room depth of 8
meters. At the same time the heating demand decreases for Kiruna in winter time, while the heating
needs of Copenhagen and Berlin are the same for a room depth whether it is 4m, 6m or 8 m.
3.1.4 Window width – South
When evaluating the sensitivity towards the window width, the evaluation in the centerline is not
necessarily adequate on their own, why the distribution is taken into account.

UDIcon Dacon Damax DFWidth1mWidth2m(Reference)
Width3m
Table 12 Daylight plots for Copenhagen, southen orientation
When looking at DAcon in Table 12 it clearly shows how the window with of 1m creates an uneven
distribution where the center line of the room has a higher DAcon compared to the sidelines of the
room. When the window width increases the DAcon evens out. When instead looking at DAmax it
shows how the 1m window width only contributes to a fairly little area right in front of the window

with hours above the maximum threshold. When the window width increases to 2m and 3m, the area
with hours above the maximum threshold enlarges both in terms of width and depth.
As for the static metrics, the DF, the same tendency shows nearest to the window, there the daylight
level rises and broadens when the window width increases. When having the distribution in the entire
room in mind, Figure 30 shows the distribution through the room in the centerline for the southern
oriented windows.
Figure 30 Daylight metrics assed in the center line for the window width case - South

When the width of the window increases UDIcon is seen to decrease in the front end of the room,
except very nearest to the window, due to the increase in hours above the maximum threshold. On the
contrary UDIcon is seen to increase in the back end of the room due to reduction of hours below the
minimum threshold.
In the rear part of the room, only a small deviation between the different locations is observed, and
only Rome differs noticeably since it due to the high solar intensity is has no hours below the
minimum threshold. In general the relative relationship between the four locations does not seem to
change with the window width, only the specific level shifts.
When looking at the daylight factor, the window width is seen to have only little impact. The daylight
factor is seen to increase minimal with the increase in window width. Nearest to the window the most
visible change is seen, where the increase in window width causes a little less stiff slope of the
daylight factor-curve.
Figure 31 Energy consumption for window width - South
In case of the energy consumption the window width has a great impact, since the increase in width
changes the relationship between window- and floor area. The increased window width is seen to
have a negative impact on the total energy consumption on all four locations, but with the relative
greatest impact on Rome where the cooling need almost doubles. In Kiruna the increased window
width also has an negative impact on the heating demand, which is almost unchanged for the three
other locations.
Naturally the increased window width also sees to cause a slightly decrease in the energy
consumption to electrical lighting.

3.1.5 Window width – West
For the eastern and western orientations the window width is seen to have an almost identical effect
on the daylight metrics and the energy consumption, why only the western orientations will be
illustrated and discussed here.
Figure 32 Daylight metrics assed in the center line for the window width - West

As for the southern orientation the UDIcon is seen to decrease in the rear end of the room due to the
reduced hours below the minimum threshold, and increase in the front end of the room due to the
increased hours above the maximum threshold. In comparison with the southern orientation, the
UDIcon in the rear end of the room for the western orientation is seen to differ more according to
location.
Figure 33 Energy consumption for window width - west
As for the energy consumption the same tendency is seen in the westerna orientations as for the
southern orientations, see section 0 for south, where the energy consumption rises with the window
width. Especially Kiruna and Rome has an increased energy consumption due to respectively
increased heating demand and increased cooling demand.

3.1.6 Window width – North
The effects on the daylight conditions and energy consumption is also identified when the window is
northern oriented, which is illustrated in Figure 34 and Figure 35.
Figure 34 Daylight metrics assed in the center line for the window width - North

In the northern orientation where the direct sunlight is ruled out and only the diffuse light is
present, the UDIcon is seen to increase significantly with an increase from 1m to 2m window
width, and again but not as significantly from 2m to 3m window width.
Rome is generally seen to have the highest UDIcon through the room of all the locations, with
an acceptance of the first two meters where the illuminance is too high.
Figure 35 Energy consumption window width - North
It generally shows how an increased window width, and thereby the window area, also has a negative
effect on the total energy consumption on northern oriented rooms. In Kiruna and Copenhagen the
increase in energy consumption is solemnly due to an increased heating demand, where in Berlin it is
a combination of increased heating demand and increased cooling demand. In Rome the increase in
the total energy consumption is only due to an increase in the cooling demand.
The increased window width is also in the northern orientation causing a decrease in the energy
consumption to the electrical lighting, but nothing near the negative effects on cooling and heating
demands.

3.1.7 Window element – South
The four different window elements; solar coated glazing, internal shading, external shading and
overhang are compared to the reference case in the following section.

Figure 36 Daylight metrics assed in the center line for the window element - South
In Figure 36 it shows how the solar coated glazing primarily effects the DF in the front end of the
room, where the DF decreases compared to the reference case. Regarding the DF with internal and
external shading only minor changes to the DF is shown, since the shading is dynamic and the DF
only represents a monentarily picture of an CIE overcast day. If a fixed shading device were selected
a decrease in the DF for both shadings would have been seen. For the overhang the DF decreases
significantly in the front of the room.
For the dynamic daylight metrics the solar coated glazing has no effect on UDIcon in Rome.
The internal and external shadings helps leveling out the UDIcon since it increases noticeably at the
front of the room and also reduced considerably in the back of the room. This is most clear for Rome
as the front of the room gets exploitable daylight conditions, due to a reduced DAmax and the sharp
transition, as seen in the reference model, is more equalized.
In the model with an overhang mainly have an effect for Rome. As the sharp transition between 0 and
100 in UDIcon, which is for Rome, is moved forward against the window. This is due to the overhang
prevents the direct sun to go quite so far into the room. It also shows that UDIcon fro the overgang for
almost all locations rises a little bit in the front of the room but decreases in the back of the room
compared to the reference case.

Figure 37 Energy consumption window element - South
Solar coated glazing and external shadings increases the heating demand for Kiruna as well as for
Copenhagen compared to the reference case.
Solar coated glazing is the window element that reduces the cooling load most for Rome in southern
oriented rooms, then the external shadings and overhangs comes next in cooling savings. Internal
shading gives a very small decrease in the cooling demand. In general, the reference model for Rome
has the highest cooling demand..
It should be noted, that solar coated glazing has no impact on UDIcon to Rome compared to the
reference model, why the only effect of solar coated glazing for Rome is seen on the decreased energy
consumption for cooling.

3.1.8 Window element – West and East
The resulting daylight metrics for the window elements in western and eastern orientations is almost
identical. Therefore this section only deals with the western oriented facades when it comes to
daylight metrics and energy consumption which is found adequate to cover both west and east.

Figure 38 Daylight metrics assed in the center line for the window element - South
To the east and west it is chosen not to simulate the parameter overhang, as the overhang only has a
noticable effect if the window is facing south, due to the low sun angles in western and eastern
directions and the lack of direct sun against north.
The DF is naturally as described under the Southern oriented window elements, see section 3.1.7,
since the DF does not change with orientation.
With solar coated glazing there is only very small change in UDIcon when the window is facing east or
west. Except for Rome where is UDIcon is completely unchanged.
As for the southern oriented window elements, the internal and external shading devices contributes to
a more even UDIcon through the room against west.
Figure 39 Energy consumption window element - West

In general, the energy consumption of the window element is higher in the east and west than in the
south, due to higher heating needs. Especially for Kiruna because of a lower passive solar gain.
Copenhagen has an increased need for heating to the west compared to south for solar coated glazing,
which is caused by an increased passive solar gain to the southern orientation.
The energy consumption to the east and west is quite identical except for Kiruna, where the reference
model has an increased need for heating to the west.
3.2 Ease and accessibility of the dynamic metrics
The calculations of the dynamic metrics, UDIcon, DAcon and DAmax, differ from the static calculation
of the daylight factor on both the input amount and the calculation time. The following is an
identification of the needed input for the calculation of both the dynamic and the static metrics and an
assessment of the time consumed in the process.
3.2.1 Calculation time
When using iDbuild for calculations of the daylight factor and the dynamic daylight metrics, the
basis-calculation needs to be performed before the additional daylight calculations can be carried out.
When the basis calculation is done, the daylight factor in a specific point is available. But if the
daylight factor at the work plane in the entire room is needed, an additional calculation is required.
The additional calculation to obtain the daylight factor plot takes 4 seconds in actual process time.
The total calculation time consumed to achieve the area plot of the daylight factor is there for 38
seconds.
When the dynamic metrics is needed, an additional calculation to the basis calculation is also carried
out. This calculation is not as fast as the daylight factor calculations, and takes 61 seconds to process.
This gives a total calculation time for the dynamic daylight calculation of 1 minute and 35 seconds.
Calculation type Calculation time Applicable to
Basis-Calculation 34 sec Both
DF-calculation 4 sec Only daylight factor
Dynamic- Calculation 61 sec Only dynamic metrics
3.2.2 Amount and complexity of input data
The input needed for the dynamic and the static calculation is listed in Table 13. The input needed
when calculating the daylight factor is the simple geometry data for the room and window together

with the glazing type and surface properties of the internal surfaces, meaning 15 input parameters in
iDbuild.
On the contrary the calculation of the dynamic metrics requires additional input concerning location,
weather and usages time, resulting in additional 7 inputs and a definition of the usages time. If the
room has a dynamic shading device additional inputs on the thermal control system is also requires,
meaning an additional 26 and the definition of usages time. When calculating the dynamic metrics the
minimum and maximum illuminance threshold is also required.
The input parameters are different, and some input are easy to make correctly and some are harder.
But since iDbuild is a program aimed at the design stage of the building process, the amount of input
is lower and less complex compared to other more advanced programs used in the final phase. The
complexity of the different input to iDbuild is estimated and indicated in Table 13. Most of the inputs
are rated medium, which indicates that the input requires a little care, but at the same time is relatively
easy to reach to some standard values.
The additional input contributes to the total time consumed in the process, but is very individual and
is not accounted for in this study. In Table 13 it is marked how complex the individual parameter is to
identify correctly.
Simular input for both Additional inputs for dynamic metrics
Input Complexity Input Complexity
Geometry Location
Room depth Simple Lattitude Simple
Facade width Simple Longtitude Simple
Room height Simple Time meridan Simple
Albedo Simple
Window Weatherdata
Height Simple Weather file Medium*
Width Simple Window
Offset floor Simple Orientation Simple
Offset wall Simple Window frame
Glazing Simple U-value Medium
Psi-value Medium
Window frame Construction
Width Simple Tran.of facade Medium
Overhang Additional trans. Medium
Distance Simple Thermal capacity Medium
Length Simple Thermal cap. interior Medium

Wall depth Simple
Surface properties Ventilation
Wall Medium Setpoint heating Medium*
Ceiling Medium Setpoint cooling Medium*
Floor Medium Infiltration Medium
Min airchange Medium*
Max airchange Medium
Max venting Medium
Heat exchanger
efficiency
Medium
Mechanical cooling Medium
People
Number of people Simple
Activity level Medium
Clothing level Medium
Lighting
Min power Medium
Max power Medium
Control Medium
Schedules/Use
The different use of the office is defined, and the
systems with the above mentioned inputs is defined
for the needed schedules.
* Simple guide to input in iDbuild
Table 13 Inputs iDbuild for daylight factor and dynamic metric calculation. Parameters marked with green is only
relevant when dynamic shading is used
When running the daylight factor calculation in iDbuild an input amount of 15 parameters is required
and a processing time of 38 seconds is consumed. When on the other hand running the dynamic
daylight metric calculation in iDbuild a total amount of input of 22 is required without dynamic
shading and a total amount of 41 with dynamic shading. Both with and without dynamic shading a
processing time of 1 minute and 35 seconds is consumed in the calculation process.
Since iDbuild is a program aimed at the early design phase of the building process, the complexity
and amount of input data is relatively low. This makes it easy accessible, and is relatively fast to build
up models within. As mentioned above the difference in running a daylight factor calculation and a
dynamic daylight metric calculation is 67 seconds, and compared to the findings of the varity of the
dynamic metrics compared to the daylight factor an extra minute should be considered negligible.

3.3 Sensitivity of the daylight metrics
After the individual analysis of the reference case and the different parameter variations in section 3.1,
it is evident how large variations shows on both the dynamic daylight metrics and the energy
consumption but only minor variations on the DF.
For a given geometry and facade DF is identical although the facade facing different orientations or at
different locations, since the DF is calculated for a CIE overcast sky. This is in contrast shown in the
dynamic metrics and the energy consumptions. For example when comparing Kiruna and Rome,
which are opposites of each other in sun and sky conditions, there is no difference in DF whereas
changes is clearly seen on UDIcon, DAcon, DAmax and the energy consumption.
When calculating the DF-plots, it only results in nine different daylight plots for the different
parameters (including the reference case), and when comparing the nine different DF-plots the
variations are minor compared to the variations seen on the dynamic daylight plots.
Since the DF is calculated for an overcast sky condition, one should think that the DF is most
applicable under the climate in Kiruna or Copenhagen. But even here great variations on the dynamic
metrics and energy consumption are still seen when changing the façade design, and only minor
changes is shown on the DF.
Some of the dynamic metrics provide more information about daylight conditions than others. E.g.
DAcon has no upper value for illuminance why high illuminance is included as usable daylight
conditions. For many locations, especially in southern Europe, this means that DAcon does not
necessarily give a more complete picture of the daylight performance than DF does.
DF might be a better indication of possible high illuminance compared to the DAcon, even though the
DF is momentary picture. DAcon does not show if illuminance is 500 lux or 5000 lux, as it simply
indicates the percentage above a given threshold (ex. 200lux). DAcon is therefore often not usable
without being compared to DAmax.
In contrast to DAcon, UDIcon is limited by an upper threshold and shows the usable daylight illuminance
within the defined thresholds. It includes neither too high nor too low illuminance. However, it does
not show whether the changes in UDIcon is because of too high or too low illuminance, why it for
many locations make most sense to use UDIcon together with DAmax as DAmax shows illuminance
above the given threshold. DAmax is therefore indicating the reasons for the changes in UDIcon.
For example UDIcon in some cases shows to be almost identical in some point in the room for both
Kiruna and Rome, but the basis for the UDIcon is very different, since Kiruna has hours below the
minimum threshold where UDIcon in Rome is only affected by hours above the maximum threshold.
Looking at the different parameter variations it shows that DAmax is extra important for locations with
high solar intensity and many hours of sunshine, such as in Rome, where there are no problems with

to low daylight illuminance. Too high illuminance has a negative effect on energy consumption why
DAmax is almost more important for the southern locations than UDIcon since DAmax shows illuminance
which has big impact on energy consumption as well as the risk of glare.
The analysis also shows that changes in the various daylight metrics do not necessarily predict
changes in energy consumption, as this is very individual for the different locations and orientations.
In general there is not a clear correlation between a high UDIcon and lower energy consumption or the
opposite. But it shows that the relation between the amount of useful daylight for the four orientations
and the energy consumption varies for the different locations.
For example, an increase in daylight illuminance in Rome often leads to an increased cooling demand
while the same in Kiruna most likely leads to a reduction in heating demand.
It is important to point out that DF as well as the dynamic metrics only provides a quantitative picture
of the daylight conditions, and does not deal with the actual quality of the daylight. This study does
not include an investigation of the quality of daylight, but does only evaluated illuminance
distribution of daylight in spaces.
The quality of the daylight is not to be neglected since high quality of daylight in spaces is not
necessarily equal to quantitative good daylight conditions, measure through the daylight metrics. In
fact very high illuminance often results in glare problems and also has a potential negative effect on
the energy consumptions.
The quality of the daylight is not evaluated in this project since is no simple useful tool to include the
quality yet. Light quality depends on how light is adapted to the function in a room. I.e. good daylight
quality is not the same for an office and a shop. Including the quality of the daylight in a study like
this, is a good basis for future work.

Chapter 4 - Conclusion
instead of DS 700.
4 Conclusion
The aim of the study was to identify the variations and sensitivity of the different daylight
metrics and to identify how and if the calculation effort differs for the different metrics.
The study has clearly shown that the dynamic metrics have great variations and high
sensitivity towards changes in location and orientations, which at the same time is not
reflected in the DF. When it comes to the different geometrical and window element changes
such as window size, shading devices etc., the dynamic metrics also shows great sensitivity
where the DF only reflects the changes with minor modifications.
The parametric analysis shows that for especially the southern orientations DAcon is not
necessarily more usable then the DF, since DAcon does not have an upper threshold. This
means, that the DAcon does not show if there is a possibility of very high illuminance and
thereby overheating and glare problems.
At the same time it was found, that when comes to the dynamic metrics it is recommendable
to involve both UDIcon and DAmax to get a full picture of the quantitative daylight conditions,
since the substance of UDIcon is important.
The study also shows that daylight conditions and energy consumption goes together hand in
hand and cannot be considered as two completely separate things. The building and façade
design has great impact on the daylight conditions as well as the energy consumption, and
one needs to be aware of the impact the different design decisions have on both parameters.
Furthermore the study shows that quantitative good or improved daylight conditions does not
have an unambiguous impact on the energy consumption, and this relationship is seen rather
complex and changing with location and orientations. This underlines the necessity of
including both dynamic daylight metrics and the impact on energy consumption when
evaluating different design options in a daylight perspective.
When it comes to the calculation effort, it was found that the available tool iDbuild 2014a is
an easy-to-use tool with a low input amount. At the same time it was found that the extra
calculation time from the static to the dynamic metrics was negligible, and that should not be
a reason to deselect the dynamic metrics.
Concluding the recommendation is to use UDIcon together with DAmax when evaluating the
quantitative daylight conditions from one design option to another and at the same time
involves the energy consumption since the impact on this is great.

Chapter 5 - <References
Side 56 Daylight metrics and their
sensitivity
Sophie Stoffer and Kathrine N.
Brejnrod
5 References
Heschong Mahone Group. 1999. An Investigation into the Relationship between
Daylighting and Human Perforance. 1999.
Christoph, F. R., Mardaljevic, J. and Rogers, Z. 2006. Dynamic Daylight Performance
Metrics for Sustainable Building Design. Leukos. Vol. 3, 2006, No.1 July .
Hviid, C. A., Nielsen, T.R. and Svendsen, S. 2008. Simple tool to evaluate the impact of
daylight on building energy consumption. Solar Energy. 82, 2008.
International Energy Agency. 1990. Guidlines & Practice Vol. 2 - Annex 14
"Condensation and Energy". s.l. : IEA, 1990.
Johnsen, K. and Christoffersen, J. 2008. SBI anvisning 219: Dagslys i rum og bygninger.
s.l. : SBI, 2008.
Mardaljevic, J., Heschong, L. and Lee, E. 2009. Daylighting metrics and energy savings.
Lighting, Research and Technology. 41:261, 2009.
Momme, A.J. 2013. Daylight in Urban Environments: Inter-reflections and their
Contribution to the indoor Daylight Levels. s.l. : Aarhus Universitet, 2013.
Nabil, A. and Mardaljevich, J. 2005. Useful Daylight Illuminance: A New Paradigm to
Acess Daylighting in Buildings. Lighting Research and Technology. 37(1): 41-59, 2005.
Nielsen, T.R. 2005. Simple tool to evaluate energy demand and indoor environment in the
early stages of building design. Solar Energy. 78, 2005.
Petersen, S. 2011. Simulation-based support for integrateddesign of new low-energy office
buildings. s.l. : Technical University of Denmark, 2011.
U.S Department for Energy. www.energy.gov. [Online]
http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data2.cfm/region=6_europe
_wmo_region_6.

Chapter 5 - <References
Web-pages:
weatherbase.com. www.weatherbase.com. [Online] [Cited: May 12th, 2014.]
worldclimateguide.co.uk. [Online] www.worldclimateguide.co.uk.

Chapter 6 - Computer programs
instead of DS 700.
6 Computer programs
iDbuild ver 2014a
For further information and free download see www.idbuild.dk
DAYSIM ver. 3.1.e (beta)
For further information and free download www.daysim.ning.com

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