Energy Performance of Dynamic
Windows in Different Climates
HANNES E. REYNISSON
SCHOOL OF ARCHITECTURE
AND THE BUILT ENVIRONMENT
Master’s Degree Project
Royal Institute of Technology
SE 100–44 Stockholm
June 2015, Sweden
School of Architecture and the Built Environment
Division of Building Technology
Supervisor: Kjartan Guðmundsson
Examiner: Kjartan Guðmundsson
TRITA-BYTE Master Thesis 437, 2015
i
SCHOOL OF ARCHITECTURE
AND THE BUILT ENVIRONMENT
Civil and Architectural Engineering
Kungliga Tekniska Högskolan
Energy Performance of Dynamic
Windows in Different Climates
Energiprestanda för dynamiska fönster under olika
klimatförhållanden
Master’s thesis in Building Technology
No. 437
Dept. of Civil and Architectural Engineering
2015 06 09
Hannes Ellert Reynisson
Supervisor:
Kjartan Guðmundsson
TRITA-BYTE Master Thesis 437, 2015
ISSN 1651-5536
ISRN KTH/BYTE/EX-437-SE
iii
Abstract
The European Union (EU) has expressed determination of reducing its energy consumption and the EU’s 2010 Energy Performance of Buildings Directive states that
all new buildings must be nearly zero energy by the end of the year 2020.
Dynamic or “smart” windows have been shown to be able to reduce HVAC
energy consumption, lighting energy and peek cooling loads in hot climates in the
US but it is difficult to find any work concerned with colder climates. This study is
intended to capture the performance of dynamic windows in a variety of European
climates to explore potential contributions to reaching the EU’s energy goals.
The building energy simulations of this study have been conducted in IDA ICE
for an office section with a large window. Three model variants are compared:
without a window shading, with an external window blind and with a dynamic
window. This comparison is repeated for six different locations; Kiruna, Reykjavik,
Stockholm, Copenhagen, Paris and Madrid.
The results of this study show that the dynamic window can reduce the total
consumed energy for lighting, heating and cooling in the range of 10%-30% more
than the external blind, depending on location. The reduction is 50%-75% when
compared to the unshaded window. This level of performance can move Europe a
step closer to zero energy buildings.
Keywords: IDA ICE, Building Energy Simulation, Electrochromic Window,
Smart Window, Window Shading.
Essentially, all models are wrong, but some are useful.
GEORGE E. P. BOX
(1919-2013)
vii
Acknowledgements
First of all, I would like to express my gratitude towards my supervisor, Kjartan
Guðmundsson. He was always available for discussions and he helped me see things
in a wider perspective.
I want to send my regards to EQUA Simulation AB for providing me with a
licence for IDA ICE. Without this powerful and flexible tool I would not have been
able to conduct the research in the way I wanted and compute the outputs I needed.
I furthermore want to thank Bengt Hellström at Equa for guidelines on standards
for various fenestration parameter calculations.
I would also like to thank D. Charlie Curcija, Ph.D. at Lawrence Berkeley National Laboratory for assisting me with the computer software Window 7 and for
giving me comments on the window parameter results from that software.
ix
Abbreviations
AHU air handling unit.
ASHRAE American Society of Heating, Refrigeration, and Air-Conditioning Engineers.
CEN Comité Européen de Normalisation.
COP coefficient of performance.
EU European Union.
GSA U.S. General Services Administration.
IGU insulated glass unit.
IWEC International Weather for Energy Calculations.
LBNL Lawrence Berkeley National Laboratory.
NFRC National Fenestration Rating Council.
PMV predicted mean vote.
PPD predicted percentage dissatisfied.
SPD suspended-particle devices.
TMM typical meteorological months.
TMY typical meteorological year.
USA United States of America.
xi
Nomenclature
M Metabolic rate [W/m2 ].
λ Wavelength in meters [m].
σSB The Stefan-Boltzmann constant mg Multiplier for the fully clear solar
(5.67×10−8 )[W/m2 /K4 ].
heat gain coefficient to represent
the fully shaded state.
bW The Wien’s displacement constant
(2,8977721×10−3 )[m·K].
P Total power per square meter emitted
by a black body at temperature T
c Speed of light in vacuum [m/s].
[W/m2 ].
F Planck spectral radiant
[(W/m2 )/m or W/m3 ].
exitance pa Water Vapour partial pressure [Pa].
fcl Clothing surface area factor [ ].
Ssignal Shading signal for the window
model.
g Center of glass solar heat gain factor T Temperature in Kelvin [K].
(see SHGC).
ta Air temperature [o C].
h The Planck’s Constant [J·s].
tcl Clothing surface temperature [o C].
hc Convective heat transfer coefficient
t̄r Mean radiant temperature [o C].
[W/(m2 ·K)].
Icl Clothing insulation [m2 · K/W].
va Relative air velocity [m/s].
kB The Boltzmann’s constant [J/K].
W Effective mechanical power [W/m2 ].
xiii
Glossary
AU The mean distance from the Sun to SHGC Solar heat gain coefficient. The
ratio of solar radiation energy dithe Earth is 1 AU (1,496×1011 m).
rectly and indirectly transmitted
through glazing assembly of the toHVAC Heating, ventilating and air
tal incident solar radiation energy.
conditioning unit.
illuminance The luminous flux inci- solar irradiation The total amount of
solar radiation energy received on
dent on a defined surface [lx].
a given surface area during a given
period [W/m2 ].
insolation (see solar irradiation).
low-e Low-emissivity.
LSG Light to solar
VLT/SHGC.
gain
ratio,
luminous efficacy Efficiency of a light
source. The ratio of the luminous
flux emitted to the electrical power
used ([lm/W]).
Tsol Shortwave radiation transmission
factor through a glazing unit.
U-value Heat transfer coefficient. It denotes the rate of heat loss through
a component.
VLT or VT. Visible light transmittance.
The ratio of the visible light diluminous flux Output of light source
rectly transmitted through a glazin all directions [lm].
ing assembly of the incident visible
light.
operative temperature The average
of the mean radiant and ambient air temperatures, weighted by WWR Window to wall ratio. The ratio of window (glazed) area to the
their respective heat transfer coeftotal wall area.
ficients.
xv
Contents
Frontmatter
Abstract . . . . . .
Acknowledgements
Abbreviations . . .
Nomenclature . . .
Glossary . . . . . .
Contents . . . . . .
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1
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2 Method
2.1 The Model Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1 Building Geometry . . . . . . . . . . . . . . . . . . . . . . . .
2.1.2 Structural Elements and Boundary Conditions . . . . . . . .
2.1.3 Occupancy, Internal Loads and Lighting . . . . . . . . . . . .
2.1.4 Room Heating and Cooling Units . . . . . . . . . . . . . . . .
2.1.5 Zone Lighting and Thermal Setpoints . . . . . . . . . . . . .
2.2 Model Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Window Without Shading . . . . . . . . . . . . . . . . . . . .
2.2.2 Window With External Blind . . . . . . . . . . . . . . . . . .
2.2.3 Dynamic Window . . . . . . . . . . . . . . . . . . . . . . . .
2.2.4 Dynamic Window (CEN Conditions) for Sensitivity Analysis
2.3 Shading Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1 Dynamic Window Shading Controls . . . . . . . . . . . . . .
2.3.2 External Blind Shading Controls . . . . . . . . . . . . . . . .
2.3.3 Shading Signal Example . . . . . . . . . . . . . . . . . . . . .
2.4 Weather Files and Locations . . . . . . . . . . . . . . . . . . . . . .
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3 Results
3.1 Duration of Shading Levels . . . . . . . . . . . . . . . . . . . . . . .
3.2 Energy Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . .
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1 Introduction
1.1 Background . . . . . . . . . . . . . . . . . . . .
1.2 Research Topics of Interest . . . . . . . . . . .
1.3 Related Work . . . . . . . . . . . . . . . . . . .
1.3.1 Dynamic Windows in Application . . .
1.3.2 Simulation of Dynamic Windows in IDA
1.4 Theory . . . . . . . . . . . . . . . . . . . . . . .
1.4.1 Solar Radiation . . . . . . . . . . . . . .
1.4.2 Glazing Properties . . . . . . . . . . . .
1.4.3 Dynamic Glazing . . . . . . . . . . . . .
1.4.4 Indoor Climate . . . . . . . . . . . . . .
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xvi
3.3
3.4
3.5
Thermal Comfort . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tinting/Bleaching Cycles . . . . . . . . . . . . . . . . . . . . . . . .
Sensitivity Analysis of SHGC . . . . . . . . . . . . . . . . . . . . . .
35
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4 Discussion
4.1 Scope and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Bibliography
47
Appendix A Full Results
A.1 Shading duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.2 Supplied energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49
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Appendix B Matlab Codes
B.1 Code for Shading Cycles . . . . . . . . . . . . . . . . . . . . . . . . .
55
55
Chapter 1
Introduction
1.1
Background
According to the European Commission ([n.d.]), buildings are responsible for 40% of
the total energy consumption in the European Union (EU) and 36% of the total CO2
emissions. Required heating energy for new EU buildings is around 12-25% of what
is required for older buildings and around 35% of the current building stock in the
EU is over 50 years old. Large energy improvements can be achieved by upgrading
these old buildings to today’s performance standards but even then, buildings will
continue to be large energy consumers. This will further push legislators to tighten
energy demands and force building constructors, owners and operators to continue
to develop with regard to energy efficiency. The EU’s 2010 Energy Performance of
Buildings Directive for example states that all new buildings must be nearly zero
energy by the end of the year 2020 and public buildings by the end of 2018.
Windows are used in buildings to achieve a certain level of natural light at
internal spaces and to give the occupants a view to the outside. They generally
have inferior thermal performance in comparison to the surrounding wall and their
maximum size is limited by the potential solar radiation heat gain and thermal
conduction through the window. The solar radiation heat gain can however be
used to the advantage of heating buildings in colder climates when needed and to
some extent counterbalance the poor thermal conduction properties of the window.
Window glazing composition, coatings and shading can be optimised to obtain a
desired balance between thermal gains and losses.
In recent years windows with a dynamic range of shading properties have been
becoming commercially available for the building sector. They are commonly referred to as “smart” or switchable but will herein be called dynamic. Dynamic
windows provide a control of heat gains and daylight and are believed to have the
potential to become net energy producers, thus requiring less building energy to
counteract heat gains and losses than through an insulated wall. (Lee et al., 2014)
These type of windows have been shown to be able to reduce HVAC energy
consumption (e.g. Lee et al. (2014)), lighting energy compared to well controlled
1
2
CHAPTER 1. INTRODUCTION
blinds and peak cooling loads. These studies have mostly been made for hot climates
in the USA while research is missing for colder climates, for example Nordic climates.
(Baetens et al., 2010) To determine whether dynamic windows can assist in reaching
the EU’s building energy goals, more studies for the variable climate conditions
within Europe need to be carried out.
1.2
Research Topics of Interest
One of the advantages of dynamic windows when compared to mechanically shaded
windows is that the shading level is adjustable, that is the shading does not have
to be in the two extreme levels, fully shaded (on) or clear (off), but it can have an
intermediate value. That way the dynamic windows can maintain certain levels of
natural light indoors and provide outside view, even when in a shaded state. In
light of that, it is important to evaluate in what states the dynamic windows will
be during occupancy over the year when comparing to a window with an operable
external blind. Another reason for evaluating the states of the window is that during
manufacturing of electrochromic windows, the shading levels are set to predefined
steps. These steps need to correspond to the most common states of shading that
provide the best performance for that particular climate. This leads to the following
research question.
For one year, what is the duration of different shading levels for
a dynamic window during occupancy compared to the duration of
on/off states for an operable external blind?
The very fact that dynamic windows provide an intermediate level of shading
allows a shading control strategy to increase the number of (partly) shaded hours
during occupancy while maintaining acceptable levels natural indoor lighting and
outside view. The dynamic windows might consequently be able to reject more
unwanted solar heat for a whole year than the operable external blind even though
the external blind might be able to reject more heat when both are compared in
the fully shaded state.
What is the annual heating and cooling energy consumption of a
building with a dynamic window compared to a static window
with an operable external blind?
When comparing design options with regard to energy efficiency, the effects on
the indoor climate and occupants need to be controlled or monitored.
For the two options in the previous research question, is there a
difference in the predicted occupant comfort?
The expected lifetime of dynamic windows might be dominated by the frequency
1.3. RELATED WORK
3
of tinting/bleaching cycles (Baetens et al., 2010). When evaluating the option of
installing dynamic windows, the number of tinting/bleaching cycles should thus
be an important measurement for the climate condition and the window shading
control strategy.
For one year, what is the number of tinting/bleaching cycles for
a dynamic window?
1.3
Related Work
A considerable amount of literature has been published on the potential energy savings of dynamic windows. The most relevant methods and results will be discussed
in this chapter, as well as their limitations and possible improvements.
1.3.1
Dynamic Windows in Application
Lee et al. (2014) published a paper on a pilot project of the U.S. General Services Administration (GSA) Region 8 for application of electrochromic and thermochromic
windows in a federal office building. The technical objectives of the projects were to
characterise and understand how dynamic windows work, estimate HVAC energy
consumption reduction, to understand the effects on occupant comfort, satisfaction
and acceptance of the technology and finally to estimate the economical feasibility
of the technology.
The building chosen for the pilot project was building 41 in the Denver Federal
Center, a low-rise office building in Denver, Colorado (latitude 38,75o N). The existing single pane clear windows on the west facing (orientation 67o west of south),
second floor were replaced with thermochromic, electrochromic and low-e windows
respectively in three defined zones from south to north. The Window to Wall ratio
(WWR) of the building was 0,27. (Lee et al., 2014)
For the first part of the study, weather and window conditions were monitored at
site in order to characterise dynamic windows. Additionally for the thermochromic
windows, thermal infra-red cameras monitored their condition for detailed evaluation of their switching patterns. The HVAC energy reduction was evaluated with a
building energy simulation conducted using the EnergyPlus1 software. The artificial
lighting was not dimmable so the simulation does not account for potential energy
variations for the lighting. Economical feasibility of the technology was evaluated
from the simulated energy savings and the additional installation cost. (Lee et al.,
2014)
The monitored behaviour of the thermochromic windows shows that they switch
based on both outdoor air temperature and the incident solar radiation (absorbed
radiation). For example on a sunny winter day in Denver when the external temperature was 5-15o C the windows were tinted for 4 hours in the afternoon. Since
1
EnergyPlus is available free of charge from the U.S. Department of Energy’s website.
4
CHAPTER 1. INTRODUCTION
office buildings with hight internal loads from lighting, occupancy, equipment often
require cooling throughout winter, this switching pattern does therefore not necessarily contradict the goal of HVAC energy reduction in office buildings. On the
negative side, the switching pattern can be inconsistent across the pane as the pane
temperature might be variable due to edge thermal bridges or partial external shading for example. Energy savings achieved by the thermochromic windows tested in
this project (type B-TC) showed to be the same as for static double-pane low-e
windows (13% and 14% annual HVAC cooling electricity reduction respectively and
26% and 28% zone cooling energy reduction for example), compared to the originally
installed, single pane, clear windows. Another type of windows (type C-TC) was
simulated where the thermochromic film properties were combined with the low-e
glazing. The annual result was 1% increase in zone heating energy, 48% decrease
in zone cooling energy and 22% decrease in HVAC cooling electricity consumption
compared to the original, single pane, clear windows. (Lee et al., 2014)
The result of the electrochromic window energy simulation is very similar to
the type C-TC thermochromic window result. Annual result shows 3% increase
in zone heating energy, 45% decrease in zone cooling energy and 22% decrease in
HVAC cooling electricity consumption compared to the original, single pane, clear
windows. The most common write-in comment from the occupants was that the
electrochromic window changed the occupant’s perceptions of the outdoor weather
patterns. No comments were made on the blue colour of the light through the
electrochromic window. (Lee et al., 2014)
This project by Lee et al. (2014) was conducted in a relatively warm climate.
Mean minimum and maximum temperatures in Denver are around -6o C and +8o C
in winter and +16o C and +31o C in summer. The project was limited to this one
location and this particular building with customised HVAC units. It is therefore
difficult to make inferences from this project of the performance of dynamic windows
in other, different climates.
The building energy performance for the different fenestration systems in this
research was obtained from building energy simulations. Even for an as extensive,
scientific renovation project as this, the difference in energy performance before and
after is very difficult to measure in reality and computer simulations were believed
to be the best option to evaluate the difference.
1.3.2
Simulation of Dynamic Windows in IDA ICE
Mäkitalo (2013) explored the simulation of electrochromic windows in the IDA ICE
software and constructed new control algorithms for more accurate simulation from
the previously available window and shading controls. The shading controls that are
currently available by default in IDA ICE are mainly intended to be used for shading
devices that use an on/off input signal. The software allows for a customisation to
create intermediate shading signals between 1 and 0, 1 for the window in its fully
shaded state and 0 for the window in a fully clear state. For more information about
the shading signal in IDA ICE, see section 2.3.
1.4. THEORY
5
The three custom shading control algorithms created by Mäkitalo (2013) will
be introduced here as they provide a foundation for the combined shading strategy
in this study that will be discussed in section 2.3.
“Schedule, façade and window”
This algorithm is designed to prevent excessive global solar radiation through the
window. It uses direct and global radiation outside the window as controls for
the shading signal while allowing for a manual schedule. The non-manual control
is not active unless direct radiation hitting the façade is above 50 W/m2 . The
shading signal is set to 0,5 if the global solar radiation is above 225 W/m2 and
to 1 (full shading) if the global solar radiation is above 450 W/m2 on the façade.
The setpoints of 50 W/m2 direct radiation and 450 W/m2 global radiation were
obtained from a study by Reinhart and Voss (2003).
“Operative temp”
The internal operative temperature is used to control the shading signal in this
algorithm. When a defined maximum temperature is reached, the shading signal is
turned to 1 (shading on). Mäkitalo (2013) used 24,5o C (0,5o C below the cooling
setpoint) as the defined operative temperature.
“Workplane”
The “Workplane” algorithm strives to maintain a fixed level of natural illumination
at the chosen location of the occupant workplane by tuning the shading signal. This
control method can provide the maximum energy savings possible as it can maintain
the minimum amount of natural light needed by the occupants, thus maintaining
as much natural light so artificial lighting is not needed but rejecting solar heat
from the excess natural light that is not needed. The illumination setpoint for
this control algorithm of 500 lx was obtained from SS-EN 12464-1:2011 (Swedish
Standards Institute, 2011) for a typical office building.
1.4
1.4.1
Theory
Solar Radiation
The solar radiation is composed of multiple frequencies with different energy intensities for each frequency. This is referred to as spectral properties of solar radiation
(Smith and Granqvist, 2011). Various factors influence the spectral properties of
solar radiation reaching the indoors of a building, e.g. sky cloud cover, solar radiation incident angle and glazing composition. When designing and evaluating a
glazing unit it is essential to realise what the incident radiation’s spectral properties
are and how the transmission of solar energy can be controlled. This section will
6
CHAPTER 1. INTRODUCTION
explain in details how the solar radiation spectrum is affected from the emittance
of the sun until it reaches the indoors of a building.
All objects that are above absolute zero in temperature emit thermal radiation.
The ideal object to describe thermal radiation is the black body. A black body absorbs all incident electromagnetic radiation but emits, isotropically, as much energy
as is theoretically possible for any body at all frequencies. Planck’s law states the
spectral radiant exitance of a black body as a function of temperature (T ) (Smith
and Granqvist, 2011):
2πhc2
.
(1.1)
hc
λ5 exp
−1
λkB T
If the radiant exitance is integrated over all frequencies we will get the total
power emitted by a black body at temperature T . This equation is known as the
Stefan-Boltzmann equation:
F (λ, T ) =
P (T ) = σSB T 4 .
(1.2)
Figure 1.1 shows the spectrum from Equation (1.1) graphically for black bodies
at different temperatures. The figure shows that with increased temperature, the
total emitted power (the area under the curve) will increase and the peak of the
curve will slide to lower wavelengths. The spectrum peak for a black body at
variable temperature T shifts according to Wien’s displacement law:
bW
.
(1.3)
T
The Sun’s exitance spectrum is similar to a black body at temperature T = 6 274
K (Smith and Granqvist, 2011). According to the Stefan-Boltzmann equation the
total emitted power of that black body is P(6 274 K) = 89 MW/m2 and according
Wien’s displacement law the peak of the spectrum is around λmax = 462 nm. That
wavelength falls inside the visible spectrum and if we take a look at Figure 1.2 we
see that the colour of that wavelength is light-blue. If, on the other hand, we look
at an object at room temperature of T = 20o C = 294 K the total emitted power is
P(294 K) = 418 W/m2 and the exitance spectrum peak for that object is λmax =9
856 nm according to Wien’s displacement law (assuming black body radiation).
That wavelength falls outside the visible spectrum (see Figure 1.2) but inside the
infra-red range.
In reality, the Sun is not a perfect black body and the total exitance power of
the Sun has been measured to be 63,3 MW/m2 at the Sun’s surface. The radiation
decreases with the distance squared as it spreads out spherically. The mean distance
from the Sun to Earth is 1 AU = 1,496×1011 m and when the radiation reaches the
Earth’s atmosphere, the total power has reduced down to 1 367 W/m2 . (Stine and
Geyer, 2001)
Gases and particles in the Earth’s atmosphere affect the solar radiation passing
through it. The radiation can be affected by the three following processes in the
λmax (T ) =
7
1.4. THEORY
Spectral Intensity (W/m2/µm) x 108
1
0K
1000 K
2000 K
3000 K
4000 K
5000 K
6000 K
0.8
0.6
0.4
0.2
0
0
0.5
1
1.5
Wavelength [µm]
2
2.5
3
Figure 1.1: Spectral exitance radiation data for perfect black bodies at different
temperatures according to Planck’s law. The peaks shift towards shorter
wavelengths with increasing temperatures according to Wien’s displacement law.
The curve for 6 000 K is close to the solar radiation surface exitance radiation
spectrum.
Figure 1.2: The electromagnetic wave spectrum. The visible range is highlighted
with blue light at around 410 nm to the left, green at 520 nm, yellow at 600 nm
and red at 710 nm. (Stangor, 2014)
atmosphere (Pidwirny, 2006). Solar radiation that is not affected by these processes
and reaches the Earth’s surface is called direct solar radiation.
8
CHAPTER 1. INTRODUCTION
Scattering
Scattering is the process when gas molecules or particles randomly change the direction of the radiation on impact. This process does not affect the wavelength of the
radiation but it can reduce the amount of radiation reaching the Earth’s surface.
The solar radiation that is affected by scattering and reaches the Earth’s surface is
called diffused solar radiation.
Reflection
When the direction of the radiation changes 180o (back the same path) on impact
with particles in the atmosphere the insolation is reduced by 100%. This process
is called reflection and it mostly occurs in clouds when radiation hits particles of
liquid and frozen water.
Absorption
Some gases and particles in the atmosphere have the ability to absorb incoming
solar radiation. The radiation will then convert to thermal energy stored in the
substance. This process will reduce the energy in the initial solar radiation but the
substance will start to emit its own radiation. That emitted radiation is on the
infra-red band according to Wien’s law for the temperatures in the atmosphere.
The radiation occurs in all directions so a part of the energy is lost back to space.
1.4.2
Glazing Properties
When the solar radiation hits a glass pane surface, a fraction of the beam is reflected
back. The size of that proportion is dependent on the window surface, incident angle
and wavelength of the radiation. A part of the radiation that is not reflected off
the pane is absorbed as heat but the remaining proportion is directly transmitted
through the pane. The energy absorbed in the pane as heat is then transferred
out of the pane to both sides by convection, conduction and radiation. The heat
transferred in that manner to the inside of the pane, opposite side of the source,
is called indirect transmittance. (University of Minnesota and Lawrence Berkeley
National Laboratory, 2014) Figure 1.3 shows a drawing of the radiation energy
losses through a glass pane.
The following four properties of windows are of most interest when quantifying
their thermal performance:
•
•
•
•
Heat Transfer Coefficient (U-value)
Solar Heat Gain Coefficient (SHGC)
Visible Light Transmittance (VLT)
Air Leakage
The U-value is a measure of the insulation value with regard to conduction,
convection and long-wave infra-red radiation of heat through the component. The
1.4. THEORY
9
Figure 1.3: Simplified image of solar radiation energy losses through a single glass
pane. Remake from University of Minnesota and Lawrence Berkeley National
Laboratory (2014).
U-value can affect both heat gains/losses due to temperature differences between
the inside and the outside of a window, and also the indirect transmission of solar
radiation energy absorbed by outermost pane, although the SHGC is used to quantify the solar radiation energy transmission, both direct and indirect, as a ratio of
the total incident solar energy. The VLT coefficient is a measurement of the visible
radiation directly transmitted. The Light-to-Solar-Gain (LSG) ratio (VLT/SHGC)
is often used as a measurement of how much heat will be generated by the daylight, affecting the cooling load. (University of Minnesota and Lawrence Berkeley
National Laboratory, 2014)
The glazing industry has standardised methods of calculating these parameters
for performance comparison of different products. Both Comité Européen de Normalisation (CEN) and U.S. National Fenestration Rating Council (NFRC) have
each developed their own method for determining these parameters. Not only do
they have different calculation procedures and reported partial properties, but they
use different boundary conditions in the calculations. (RDH Building Engineering
Ltd., 2014) This can result in mismatching parameters when using both methods
or unfair comparison between two products evaluated with the separate methods.
NFRC uses calculation procedures from the international standard ISO 15099
Thermal performance of windows, doors and shading devices - Detailed calculations
(The International Organization for Standardization, 2003) for determination of Uvalue, SHGC and VLT with NFRC defined boundary conditions (see Table 1.1).
European methods, however, follow other standards: EN 410 for SHGC and determination of luminous and solar characteristics and EN 673 for U-value according to
GEPVP or Glass for Europe’s ([n.d.]) code of practice. These methods use CEN
defined boundary conditions. D. Charlie Curcija, Ph.D. at the Lawrence Berkeley
10
CHAPTER 1. INTRODUCTION
National Laboratory (LBNL) claims that the standards for the European methods
are outdated and inaccurate (personal communication, May 8, 2015).
Hanam et al. (2014) state that neither the NFRC nor CEN method can be
considered “better”, they both have different sets of limitations. The NFRC method
is said to use more accurate algorithms that are able to compare all products under
the same conditions but the CEN method is said to use more realistic environmental
conditions.
Table 1.1 displays the environmental conditions assumed for the different methods and Table 1.2 shows the corresponding surface heat transfer film coefficients
assumed. The surface film coefficients for calculations of SHGC are for “summer
conditions” opposite to the “winter conditions” used for U-value calculations. (RDH
Building Engineering Ltd., 2014)
Table 1.1: Environmental conditions for different methods of determining window
parameters. Temperatures are in o C and solar radiation in W/m2 .
Method
Exterior
temperature
Interior
temperature
Solar
radiation
-18
32
0
30
21
24
20
25
783
500
NFRC (Winter)
NFRC (Summer)
CEN (Winter)
CEN (Summer)
Table 1.2: Surface heat transfer film coefficients for different methods of
determining window parameters.
Method
Film Coefficient
[W/m2 K]
Exterior
Comments
Interior
NFRC (Winter)
26,0
NFRC (Summer)
15,0
CEN (Winter)
25,0
7,7
CEN (Summer)
8,0
2,5
Convection only. Radiation model used.
Interior coefficients depend on frame
system.
Convection only. Radiation model used.
Interior coefficients depend on frame
system.
Combined convection and radiation coefficient (ISO 10292 for center of glass
simulations).
For SHGC calculations.
1.4. THEORY
1.4.3
11
Dynamic Glazing
Three different technologies are commonly used to achieve the dynamic nature of
the shading for these types windows in buildings: chromic materials, liquid crystals and electrophoretic or suspended-particle devices (SPD). The chromic materials
can be divided in four categories based on their control mechanism: electrochromic,
gasochromic, photochromic and thermochromic. Photochromic and thermochromic
devices are controlled by light and heat respectively so, in general building application, their state cannot be controlled by a building management system or manually
adjusted by the user. This lack of controllability renders photochromic and thermochromic less feasible to the others and their control system will not be simulated
in this research. (Baetens et al., 2010)
1.4.4
Indoor Climate
When comparing building energy performance for different building components
or different control strategies the occupant comfort levels need to be within the
same range or they need to be registered and evaluated for a fair comparison. For
case studies, energy savings obtained at the cost of lower comfort levels need to be
subjectively justified.
The indoor climate affects products, processes and the occupant comfort, health
and productivity. For office building the effects of the indoor climate on the occupant
is more relevant than on products and processes and since the research is aimed at
office buildings this chapter will focus on effects on the occupant.
One of the most common methods in Europe for evaluating the thermal indoor
climate is stated in the EN ISO 7730:2005 standard (Hegger et al., 2008). This standard provides analytical methods to numerically grade the indoor thermal climate
according to occupant impression. It also specifies local thermal comfort criteria
considered acceptable both for general- and local thermal discomfort. (Swedish
Standards Institute, 2006).
EN ISO 7730:2005 uses the Fanger indexes, predicted mean vote (PMV) and predicted percentage dissatisfied (PPD), to analyse and interpret the occupant thermal
comfort. The PMV index predicts the mean value of votes of a large group of people on a 7-point scale (see Table 1.3) for the experience of the thermal comfort,
based on the heat balance of the human body. The PPD index is a function of the
PMV index that establishes a quantitative prediction of the percentage of thermally
dissatisfied occupants. (Swedish Standards Institute, 2006)
12
CHAPTER 1. INTRODUCTION
Table 1.3: The 7-point thermal sensation scale of the PMV index.
PMV
(Predicted Mean Vote)
Explanation
3
2
1
0
-1
-2
-3
Hot
Warm
Slightly warm
Neutral
Slightly cool
Cool
Cold
The PMV index is calculated according to equations 1.4 to 1.7. The PMV
index is a function of a number of different variables. The variable notations in the
formulas are explained in the nomenclature.
h
i
P M V = 0, 303 · e−0,036·M + 0, 028 · (M − W ) − 3, 05 · 10−3
· [5733 − 6, 99 · (M − W ) − pa ] − 0, 42 · [(M − W ) − 58, 15]
(1.4)
− 1, 7 · 10−5 · M · (5867 − pa ) − 0, 0014 · M · (34 − ta )
h
i
− 3, 96 · 10−8 · fcl · (tcl + 273)4 − (t̄r + 273)4 − fcl · hc · (tcl − ta )
where
tcl = 35, 7 − 0, 028 · (M − W ) − Icl
h
h
i
i
· 3, 96 · 10−8 · fcl · (tcl + 273)4 − (t̄r + 273)4 + fcl · hc · (tcl − ta ) ,
(
hc =
2, 38 · |tcl − ta |0,25
√
12, 1 · va
and
(
fcl =
√
for 2, 38 · |tcl − ta |0,25 > 12, 1 · va
√
for 2, 38 · |tcl − ta |0,25 < 12, 1 · va
1, 00 + 1, 290 · Icl
1, 05 + 0, 645 · Icl
for Icl ≤ 0, 078 m2 · K/W
.
for Icl > 0, 078 m2 · K/W
(1.5)
(1.6)
(1.7)
When the PMV index has been evaluated, the PPD can be calculated according
to the following equation:
PPD = 100 − 95 · exp(−0, 03353 · PMV 4 − 0, 2179 · PMV 2 )
(1.8)
Chapter 2
Method
The best approach to this project was considered to be the usage of computer models
as physical models require much more effort, time and cost. The computer software
chosen for the task was IDA Indoor Climate and Energy or IDA ICE (2014). IDA
ICE is a whole year, dynamic, multi-zone simulation application for indoor thermal
climate and energy consumption of entire buildings. The mathematical models in
IDA ICE reflect the latest research and the results fit well with measured data.
At the start of this work the EnergyPlus building simulation engine was tried
out for the task as it has a built in feature of simulating a dynamic window and it
has been used in other studies (e.g. Lee et al. (2014)). The transparency of IDA
ICE made it much easier to understand and its extremely flexible nature made it
possible to customise the models to needs and to build the dynamic behaviour of a
smart window, even though it is not available by default in the software.
To minimise the calculation time and simplify the results a “Shoe Box” model of
a defined section of a building is simulated, see Figure 2.1. All loads and schedules
resemble activities for an office building with operation hours from 07:00 to 18:00
every weekday. All the simulated cases are based on the model foundation that
is described in section 2.1. For estimating the impact of different window shading
methods, three model variations are created: one variation with a dynamic window, one with an operable external blind for comparison and one variation with an
unshaded window as a reference. The model variations are described in section 2.2.
Simulations are run for six locations within Europe to see the dynamic window
performance in various climates representing different latitudes. The Shoe Box is
turned with the window facing south, east and west in separate simulations for
each location and for each direction the three model variations are simulated. For
Madrid, one extra model variation is run for a sensitivity analysis of the dynamic
window SHGC.
13
14
CHAPTER 2. METHOD
2.1
The Model Base
2.1.1
Building Geometry
The geometry of the Shoe Box is taken from the EN 15265:2007 standard (Swedish
Standards Institute, 2007) for validation tests of building simulation software. That
geometry has a high window to wall ratio (WWR) and that was considered optional
for emphasising the impact of different fenestration systems on the building’s performance because energy savings from electrochromic windows should be greater
with larger windows (Lee et al., 2014). It should be kept in mind when evaluating
the results of this study that the level of energy savings obtained in buildings with
as large WWR as the Shoe Box might not be reached in buildings with smaller
WWR.
Figure 2.1: The Shoe Box model used for the simulations.
The dimensions of the Shoe Box are the following: depth 5,5 m, width 3,6 m
and height 2,8 m. That gives an external surface of 10 m2 , floor area of 20 m2 and
a zone volume of 55 m3 . The window has a height of 2 m and a width of 3,5 m with
a 0,05 m wall margin on the sides and the top. The window surface is therefore 7
m2 and the window to wall ratio for the external wall is close to 0,7.
2.1.2
Structural Elements and Boundary Conditions
The wall with the window is external and it is the only external wall in the model.
Its construction is displayed in Table 2.1.
15
2.1. THE MODEL BASE
Table 2.1: External wall materials used for the all models.
External wall
Outside
Materials and thickness
Render
Light insulation
L/W concrete
Render
Inside
1 cm
25 cm
25 cm
1 cm
52
0,1136
Total thickness [cm]
Total U-value [W/m2 K]
The internal structural elements have adiabatic boundary conditions so the net
heat transfer across them is zero but they are able to store heat. The internal walls
are made of gypsum and the floor and ceiling are made of concrete. The internal
element materials are displayed in Table 2.2.
Table 2.2: Internal structural components used for all models.
Internal walls
Materials and thickness
Total thickness [cm]
Total U-value [W/m2 K]
2.1.3
Gypsum
Air
Gypsum
2,6 cm
7 cm
2,6 cm
Internal floor/ceiling
Outside
Concrete
L/W Concrete
Floor Coating
Inside
12,2
1,707
15 cm
2 cm
1 cm
18
2,237
Occupancy, Internal Loads and Lighting
Only one person is assumed to occupy the Shoe Box from 07:00 to 18:00 on weekdays.
No occupancy is assumed on weekends. Metabolic rate for the occupant is 1,2 met
= 70 W/m2 for sedentary activity (Swedish Standards Institute, 2006) and IDA ICE
assumes the surface area of 1,8 m2 /person that corresponds to Nilson’s (2007) 1,77
m2 /person for the average Scandinavian population. This means that the occupant
generates 126 W of heat in the model. For the thermal comfort calculations clothing
insulation is assumed 85 ± 25 clo = 0,13 ± 0,04 m2 K/W. Variable clothing levels
represent the person’s ability to change clothing according to temperature. Variable
clothing level can also influence the power emitted by the person.
The occupant is placed at the centre of the Shoe Box, about 2,3 m away from
the window. The workplane height is set to 0,8 m. Equipment in the Shoe Box
16
CHAPTER 2. METHOD
is assumed to use 150 W of electricity power and generate 150 W of heat. The
equipment is only turned on during occupancy.
Two lighting units are in the ceiling, each with 50 W input power. Their luminous efficacy is set to 20 lm/W thus able to produce in total 2000 lm luminous flux
at full power.
2.1.4
Room Heating and Cooling Units
For simplification, the Shoe Box model has idealised local room units for heating and
cooling. The units are assumed to have no power limitations, thus able to maintain
setpoint temperatures even at high thermal loads. Coefficient of performance (COP)
for both the ideal heater and ideal cooler is assumed equal to 1 and no emission
losses are registered. By having this configuration, the registered supplied energy
can be used as a measurement of the thermal energy flows required to maintain the
heating and cooling setpoints. For the ideal heater, the supplied energy equals the
sensible heat provided for the zone as the heater does not add or remove moisture
from the air. The supplied energy for the ideal cooler equals the sum of the latent
and the sensible heat removed from the zone as the cooler can remove moisture from
the air. An air handling unit (AHU) is not connected to the model as air changes
and thermal recovery are not of interest in the study.
2.1.5
Zone Lighting and Thermal Setpoints
The artificial lighting in the model is dimmable, controlled by occupancy and natural illuminance at workplane. The minimum natural illuminance for full artificial
lighting to be active is set to 100 lx and the artificial lighting is turned of at above
500 lx natural illuminance. Between these points the artificial lighting is given a
linearly interpolated value. The lighting is turned off when the office is vacant.
During vacancy there is no requirement of natural illuminance so at that time the
measured level of natural light does not affect the shading signal.
The thermal setpoints for the zone are set for the air temperature as it is more
common in reality than to use the operative temperature. The heating setpoint
is set to 20o C and the cooling setpoint is set to 26o C. A setpoint shift of ±6o C is
used during vacancy so the building is not heating or cooling when it is not needed.
These setpoints are determined with reference to EN ISO 7730 (Swedish Standards
Institute, 2006) (see Table 2.3). The values in that table are for the operative
temperature and to use them for the air temperature will have an impact on the
occupant comfort. The occupant comfort will therefore have to be evaluated when
comparing the results.
17
2.2. MODEL VARIATIONS
Table 2.3: Operative temperature requirements for sedentary activities according
to EN ISO 7730 (Swedish Standards Institute, 2006).
2.2
Category
Summer
(cooling period)
Winter
(heating period)
A
B
C
24,5o C ± 1,0
24,5o C ± 1,5
24,5o C ± 2,5
22o C ± 1,0
22o C ± 2,0
22o C ± 3,0
Model Variations
All simulated models are based on the model described in section 2.1. The window
shading parameters and the shading strategies are the only things that change
between the different models. Four model variations are used for the simulations,
each described in the following sections.
2.2.1
Window Without Shading
This first model variation is used as a reference case. The window in the model
is without any type of shading. The window parameters used for this case are
displayed in Table 2.4. The values are obtained from the clear state of the dynamic
window product introduced in section 2.2.3. As there was no shading in this model,
the window does not have values for a shaded state.
Table 2.4: Window parameters used in IDA ICE for the window without shading.
Clear state
2.2.2
SHGC
Tsol
Tvis
U-value
0,413
0,331
0,602
1,56
Window With External Blind
The second model includes an external, operable, window blind. The same clear
state values are used for this window as for the window without shading and the
dynamic window. The shaded state values of this window are obtained by built in
multipliers that represent an active external blind. In IDA ICE the shaded state
values are not entered directly but they are calculated by multiplying the clear state
values and the relevant multipliers (see Equation 2.1 in section 2.3). The window
parameters for this case are displayed in Table 2.5. The shading control strategy is
custom made for the external blind using the same setpoints as the shading control
strategy for the dynamic window in the following section. More information on the
shading controls and the shading control strategies may be found in section 2.3.
18
CHAPTER 2. METHOD
Table 2.5: Window parameters used in IDA ICE for the externally shaded window.
Clear state
Shaded state
2.2.3
SHGC
Tsol
Tvis
U-value
0,413
0,058
0,331
0,030
0,602
0,054
1,56
1,56
Dynamic Window
In a literature review of properties, requirements and possibilities of dynamic windows for daylight and solar energy control in buildings published by Baetens et al.
(2010) it is stated that “electrochromic windows seem to be the most promising stateof-the-art technology for daylight and solar energy purposes”. Based on that, the
dynamic window properties chosen for the energy simulation model in this study
are representative for a high-end electrochromic window product. Even though it
is not the purpose of this research to model a specific product or technology of
dynamic glazing, the properties that are chosen for the dynamic glazing needed to
be realistic and show the potentials for products in the near future.
The extreme state parameters used for the dynamic window in the model are
displayed in Table 2.6. They represent an actual electrochromic window product
by SAGE Electrochromics: SageGlass® Clear w/SR2.0. These values are available
in the product specifications from the manufacturer and the same values can be obtained by using the computer program Window 7 (2014) to calculate the combined
insulated glass unit (IGU) parameters with NFRC environmental conditions and
ISO 15099 method for thermal and optical calculations (see section 1.4.2). Information on the shading controls may be found in section 2.3.
Table 2.6: Window parameters used in IDA ICE for the dynamic window (NFRC
conditions).
Clear state
Shaded state
2.2.4
SHGC
Tsol
Tvis
U-value
0,413
0,087
0,331
0,005
0,602
0,009
1,56
1,56
Dynamic Window (CEN Conditions) for Sensitivity
Analysis
As mentioned in the previous section the dynamic window parameters provided
by the manufacturer are calculated with NFRC environmental conditions and ISO
15099 method for thermal and optical calculations. These methods are used in
North America but other methods are commonly used in Europe as discussed in
section 1.4.2. Table 2.7 displays the window parameters for the same product but
calculated according to European, CEN defined, methods in Window 7 (2014).
19
2.3. SHADING CONTROLS
Table 2.7: Window parameters used in IDA ICE for the sensitivity analysis of the
SHGC (CEN conditions).
Clear state
Shaded state
SHGC
Tsol
Tvis
U-value
0,431
0,116
0,346
0,005
0,602
0,009
1,47
1,47
The two methods produce slightly different SHGC for the extreme states as may
be seen when Table 2.6 and Table 2.7 are compared. The NFRC method gives a
shaded state SHGC value that is 25% lower than that obtained by the CEN method.
That means that the NFRC method provides a shaded state SHGC that is more
in favour of the dynamic window product and makes it look like it is able to reject
more heat in a shaded state than according to CEN methods.
The NFRC calculated values are chosen to represent the dynamic window in
this research (see section 1.4.2) but a sensitivity analysis is conducted to see the
effect of using the CEN values in parallel.
2.3
Shading Controls
The CeWind or Simple Window Model in IDA ICE uses input values of SHGC,
U-value and Tsol to represent the window in a fully clear state (when the shading
signal is 0). In this model, the shaded state values are obtained by multiplying
the clear state values with relevant multipliers (mg in equation 2.1). So the shaded
state values are not entered directly to the model but calculated from the clear state
values and their multipliers. When the shading signal takes on a value between 0
and 1 the parameters will take on a linearly interpolated value between the two
extreme states. This can be described mathematically by the following equation
as the center of glass SHGC is used as an example. The center of glass SHGC is
represented by g in the equation, mg represents the multiplier, Ssignal is the shading
signal and the subscript 0 (e.g., g0 ) denotes the original, fully clear state value.
g = (mg · g 0 ) · Ssignal + g 0 · (1 − Ssignal )
2.3.1
(2.1)
Dynamic Window Shading Controls
An electrochromic window unit has predefined shading steps built in. The number of
steps and their levels is defined by the manufacturer and it cannot be changed after
production. (Lee et al., 2014) The control strategy in this energy simulation does
not account for these predefined shading steps, but assumes the window can take
on any shading state linearly interpolated between the two extreme shading states
(on/off). Dynamic windows have a certain response time and the pane can appear
non-uniform while changing states. Mäkitalo (2013) made a sensitivity analysis of
the response time of a dynamic window on the simulated HVAC energy consumption
20
CHAPTER 2. METHOD
and the result was that the response time has very little effect. With that in mind,
the dynamic window control strategy in this study does not account for a shading
response time and all requested changes in shading occur instantly.
The combined shading strategy used for the dynamic window in this research
is largely based on the three different components created by Mäkitalo (2013) discussed in section 1.3.2 but with few additional components. A flowchart of the
combined shading strategy for the dynamic window is displayed in Figure 2.2.
The component “Free solar heat wanted?” evaluates if the solar heat is to be
rejected (during cooling periods) or harvested (during heating periods). It uses the
mean internal air temperature and a 24 hour sliding average of the external ambient
air temperature as controls. During occupancy, if either the sliding average external
air temperature exceeded 8o C or the internal air temperature exceeded 23,5o C the
solar heat is to be rejected. During vacancy these setpoints are different. During
vacancy the setpoint for the sliding average of the external air temperature is 7o C
and the setpoint for the air temperature is 22o C on weekdays and 21o C during
weekends. These setpoints are obtained by trial and error for trying to find the
balance temperature for the building and its thermal loads.
Global radiation of 225 W/m2 on façade is used as a setpoint for the window
to turn to 50% shading when the solar heat was wanted (during heating periods).
Global radiation of 450 W/m2 on façade is used as a setpoint for the dynamic
shading to turn to maintaining 800 lx at workplane when the solar heat is wanted.
This strategy is a combination of Mäkitalo’s (2013) “Schedule, façade and window”
algorithm and the “Workplane” algorithm. The setpoints are the same except the
800 lx at workplane. Here it is raised from 500 lx when the solar heat is wanted to
increase positive solar heat gain yet still providing protection from excessive solar
heat gain. When no direct radiation hits the façade, these setpoints are inactive, also
when the solar heat is not wanted (during cooling periods) the shading maintains
500 lx at workplane at all times during occupancy so the 225 W/m2 and 450 W/m2
setpoints are not active at those times.
During vacancy the dynamic window is set to only take on the two extreme
shading states, darkest or clearest.
2.3. SHADING CONTROLS
Figure 2.2: Flowchart of the control strategy used for the dynamic window.
21
22
2.3.2
CHAPTER 2. METHOD
External Blind Shading Controls
The control strategy for the external blind uses the same setpoints as the strategy for
the dynamic window. A flowchart of the strategy for the externally shaded window
may be found in Figure 2.3. The main difference between the two strategies is that
the components that maintain a fixed level of natural illuminance at workplane are
not present in the one for the external blind due to the fact that the external blind
can only be on or off. The component that measures if the global radiation is above
225 W/m2 is also left out so during occupancy the external blind is only turned on
if direct solar radiation is above 50 W/m2 on the façade and the global radiation is
above 450 W/m2 .
Figure 2.3: Flowchart of the control strategy used for the mechanically, externally
shaded window.
23
2.4. WEATHER FILES AND LOCATIONS
2.3.3
Shading Signal Example
An example of the shading signal output of the two different shading strategies
may be found in Figure 2.4. The figure displays the shading signal output for one
weekday in April for Stockholm. All numerical output values of the simulations
are registered for half hour intervals so the curves are not completely smooth. The
figure shows the dynamic window striving to maintain a fixed level of illuminance
at workplane during occupancy between 07:00 and 18:00 but after 18:00 the signal
jumps to full shading. The external blind is only able to be “on” or “off” so the
shading signal jumps between 1 for shading “on” and 0 for shading “off”. Between
12:00 and 14:00 the external blind jumps to “on” to prevent excessive radiation as
the global radiation is above 450 W/m2 and direct radiation hits the façade. After
occupancy at 18:00, the signal jumps again to “on” using the same strategy as the
dynamic window during vacancy.
1
StockholmDynamicSouth
StockholmExternalSouth
Shading signal [ ]
0.8
0.6
0.4
0.2
0
0
2
4
6
8
10
12
Time [h]
14
16
18
20
22
24
Figure 2.4: Example of the output of the shading strategy. These shading signals
are for one weekday in April for Stockholm.
2.4
Weather Files and Locations
Geographical locations for the different simulations are chosen so that they have
an equal latitudinal spread to capture the variety of available daylight hours and
temperature in Europe. Reykjavik and Stockholm are chosen as they are of interest
to the author and Madrid is chosen as it has a close latitude to Denver where the
pilot project of Lee et al. (2014) was conducted. The other three locations, Kiruna,
Copenhagen and Paris, provide equal latitudinal spread as is displayed in Figure
2.5.
Two main types of weather files are currently available from American Society
of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) for building
24
CHAPTER 2. METHOD
75 °
60 °
N
Kiruna
Reykjavik
N
Stockholm
Copenhagen
45 °
N
Paris
Madrid
30 °
N
°
45
15 °
W
0°
°
15 E
E
°
30 E
Figure 2.5: Location of cities where energy simulations are performed.
energy simulations, both types are typical meteorological year (TMY). The old
ones, International Weather for Energy Calculations (IWEC), are derived from 18
years (1982-1999) of DATSAV3 hourly weather date from the National Climatic
Data Center. 12 typical meteorological months (TMM) are chosen from that period
to compose a TMY. Solar radiation is calculated from cloud cover and Earth-Sun
geometry. (U.S. Department of Energy, 2011)
Lundström (2012) found the direct solar radiation in the old IWEC files to be
underestimated of about 20-40% for Northern Europe. He states that those files
should be used with care if solar radiation has significant effect on the result.
A second version, IWEC2 weather files were developed through ASHRAE Research Project RP-1477. The underestimation of direct solar radiation seems to
be fixed, at least for Stockholm and Helsinki. Both IWEC and IWEC2 use the
same Zang-Huang model for global horizontal solar radiation from cloud cover but
a new model is used for splitting the global horizontal radiation to diffuse and direct
normal solar radiation. In addition the IWEC2 stations use different regression coefficients for different Köppen-Geiger zones instead of using the same set of regression
coefficients for all locations in the old IWEC. (Lundström, 2012)
In light of the above, IWEC2 files are selected for the simulations in this research.
25
2.4. WEATHER FILES AND LOCATIONS
As the IWEC2 weather files are for TMY, the year chosen for the annual simulations
only affects how weekdays are arranged for the year, for example if 1 January is a
Monday. All simulations are made for the weekday arrangement of the year 2015.
Locations of the weather station may be found in Table 2.8.
Table 2.8: Locations of the IWEC2 weather stations used for the simulations.
Location
Latitude
Longitude
Kiruna
Reykjavik
Stockholm (Bromma)
Copenhagen (Kastrup)
Paris (Orly)
Madrid (Getafe)
67,817 N
64,132 N
59,367 N
55,617 N
48,717 N
40,3 N
20,333 E
21,9 W
17,9 E
12,65 E
2,383 E
3,717 W
Chapter 3
Results
3.1
Duration of Shading Levels
This chapter will display in the states of the dynamic- and externally shaded windows for the whole simulated period of the different cases. As the dynamic window
was able to take on any interpolated shading signal and the values varied constantly
throughout the period, the best way to show the most frequent states of the window
is to display a duration diagram. The values used for a duration diagram plot are
sorted in an ascending order and plotted in the sorted order. From these plots, one
can choose two points on the Shading signal axis (y-axis) and read the duration of
values between these states on the Time duration axis (x-axis) or vice versa.
The following duration diagrams for the shading signal are for occupant hours
only. As the shading signal during vacancy could only either be “on” or “off” for
both the dynamic- or externally shaded windows a duration diagram is not needed.
The time duration for the shading signal during vacancy may be read from the bar
chart in Figure 3.7 where the vacant shading signal duration is compared to the
occupied shading signal duration.
Each duration diagram includes results for one location. The externally shaded
window results will appear as vertical lines through the graph as the signal can only
be either 0 or 1. The time duration above that line is therefore the shaded state
duration. The total number of occupied hours is 2871 for all cases, thus being the
maximum value on the Time duration axis. Total number of hours for the 365 days
simulated is 8760 and the unoccupied hours are 5889.
The shading duration diagram for Kiruna in Figure 3.1 shows that for each
direction the dynamic window was in its clearest state for about half of the occupied
time. The external shading was active for around 300 hours for both south and east
facing directions. The west facing window required less shading, with the dynamic
window clear for about two thirds of the occupied time and the external shading
was almost never required.
27
28
CHAPTER 3. RESULTS
1
Kiruna, Dynamic, South
Kiruna, External, South
Kiruna, Dynamic, East
Kiruna, External, East
Kiruna, Dynamic, West
Kiruna, External, West
Shading signal [ ]
0.8
0.6
0.4
0.2
0
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.1: Shading duration diagram for Kiruna during occupancy.
1
Reykjavik, Dynamic, South
Reykjavik, External, South
Reykjavik, Dynamic, East
Reykjavik, External, East
Reykjavik, Dynamic, West
Reykjavik, External, West
Shading signal [ ]
0.8
0.6
0.4
0.2
0
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.2: Shading duration diagram for Reykjavik during occupancy.
Less shading was required for Reykjavik than for Kiruna. Figure 3.2 shows that
similar shading duration pattern applied in all directions for Reykjavik with a slight
shift. The south facing windows were shaded for the longest and the west facing
the shortest.
29
3.1. DURATION OF SHADING LEVELS
1
Stockholm, Dynamic, South
Stockholm, External, South
Stockholm, Dynamic, East
Stockholm, External, East
Stockholm, Dynamic, West
Stockholm, External, West
Shading signal [ ]
0.8
0.6
0.4
0.2
0
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.3: Shading duration diagram for Stockholm during occupancy.
The south facing windows in Stockholm had the longest shading duration by
far of the simulated directions (see Figure 3.3). The dynamic window facing south
was in a shading state over 50% in more than half of the occupied time and the
externally shaded window facing south was shaded for 500 occupied hours or about
20% of the occupied time. In east and west facing directions the dynamic window
was in a fully clear state for about half of the occupied time.
1
Copenhagen, Dynamic, South
Copenhagen, External, South
Copenhagen, Dynamic, East
Copenhagen, External, East
Copenhagen, Dynamic, West
Copenhagen, External, West
Shading signal [ ]
0.8
0.6
0.4
0.2
0
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.4: Shading duration diagram for Copenhagen during occupancy.
The shading duration patterns for Copenhagen were similar to the ones for
Stockholm with a slight shift to the right. One apparent change from the Stockholm
diagram was that the curves for the east facing windows were closer to the curves
for the south facing windows. Stockholm and Copenhagen are in the same time
30
CHAPTER 3. RESULTS
zone but Copenhagen lies further west than Stockholm. The occupancy was for the
same hours of the day so for Copenhagen the occupancy started and ended when
the sun was further east than in Stockholm. That may explain why the east shading
was active relatively longer in Copenhagen than in Stockholm during occupancy.
The same might apply to other locations, i.e., inconsistency between solar time and
clock time.
1
Paris, Dynamic, South
Paris, External, South
Paris, Dynamic, East
Paris, External, East
Paris, Dynamic, West
Paris, External, West
Shading signal [ ]
0.8
0.6
0.4
0.2
0
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.5: Shading duration diagram for Paris during occupancy.
The shading duration diagram for Paris in Figure 3.5 was very similar to the
one for Copenhagen except the shading duration curve for the west facing windows
moved slightly to the left, closer to the curve for the east facing window.
1
Madrid, Dynamic, South
Madrid, External, South
Madrid, Dynamic, East
Madrid, External, East
Madrid, Dynamic, West
Madrid, External, West
Shading signal [ ]
0.8
0.6
0.4
0.2
0
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.6: Shading duration diagram for Madrid during occupancy.
The simulated cases in Madrid clearly required more shading than for the other
3.1. DURATION OF SHADING LEVELS
31
locations. Figure 3.6 shows that the south facing dynamic window was in a state
of over 50% shading for about 75% of the occupied time and the external shading
in the same direction was active for around 25% of the occupied time.
Figure 3.7 combines the shading duration from the previous graphs for occupancy with the shading duration during vacancy. During vacancy the dynamic
window was only able to take on the two extreme states, i.e., “on” or “off”, so it
behaved in in the same way as the external shading. The black columns on the
graph therefore represent the on state during vacancy. For the dynamic window
during occupancy the shading was considered “on” when the shading level reached
above 50%. More detailed result may be found in Table A.1 in Appendix.
If we take a further look at Figure 3.7 we see that the difference in shading during
vacancy between the different cases was not significant, although the Madrid cases
utilised the shading apparently more than the others during vacancy. The grey
columns for occupancy were transferred from duration diagrams in figures 3.1 to
3.6 so we have already compared the levels different cases but on the bar chart we
can see the ratio between shading during occupancy and shading during vacancy.
We see that the dynamic window, in some cases, was in over 50% shaded state
during occupancy for as long time as the shading was “on” during vacancy. The
externally shaded windows in most cases were however in a shaded state during
occupancy only a fraction of the time they were in a shaded state during vacancy.
4000
Time duration [h]
3500
During vacancy
During occupancy
3000
2500
2000
1500
1000
0
KirunaDynamicSouth
KirunaExternalSouth
KirunaDynamicEast
KirunaExternalEast
KirunaDynamicWest
KirunaExternalWest
ReykjavikDynamicSouth
ReykjavikExternalSouth
ReykjavikDynamicEast
ReykjavikExternalEast
ReykjavikDynamicWest
ReykjavikExternalWest
StockholmDynamicSouth
StockholmExternalSouth
StockholmDynamicEast
StockholmExternalEast
StockholmDynamicWest
StockholmExternalWest
CopenhagenDynamicSouth
CopenhagenExternalSouth
CopenhagenDynamicEast
CopenhagenExternalEast
CopenhagenDynamicWest
CopenhagenExternalWest
ParisDynamicSouth
ParisExternalSouth
ParisDynamicEast
ParisExternalEast
ParisDynamicWest
ParisExternalWest
MadridDynamicSouth
MadridExternalSouth
MadridDynamicEast
MadridExternalEast
MadridDynamicWest
MadridExternalWest
500
Figure 3.7: Number of hours when shading is on. For the dynamic window,
shading is considered “on” when the shading level is above 50%. During vacancy
the dynamic window only takes on the two extreme states so it behaves in a
similar way to the externally shaded window, i.e., on/off.
32
3.2
CHAPTER 3. RESULTS
Energy Consumption
There were four components in the models that required supplied energy: heating,
cooling, lighting and equipment. As number of occupant hours was the same for all
models, the supplied energy for equipment was the same for all cases according to
occupant schedule. Total sensible heat gain caused by equipment was 430 kWh/year
for all simulations. The heat generated by the equipment was equal to its supplied
energy. This energy will not be included in the energy consumption comparison.
Internal gain caused by the occupant was not included in the supplied energy
results as that thermal energy may be considered as free. Even though the occupant
activity level was constant and the same for all simulations the power emitted by the
occupant varied since the clothing level (the thermal resistance) is variable. This
variance was not registered in the following results.
The lighting is controlled by illuminance sensors so supplied energy and thermal
gains for the lights vary between simulations. As discussed in section 2.1.4, the
delivered energy to the room units was equal to the thermal energy flows provided by
the units. For the heater, the supplied energy was equal to the sensible heat added
to the zone as the latent heat provided by the heater was always zero. Condensation
could occur in the cooler so the supplied energy for the cooler was equal to the sum
of the sensible and the latent heat removed from the zone. The results only show the
supplied energy to the ideal heater and the ideal cooler but not distinction between
sensible and latent heat removed from the zone by the cooler.
Table 3.1 displays a summary of the supplied energy result for the simulated
cases. The table does not display the supplied energy for each simulated direction
but it has been summed for all directions for each case. Supplied energy in separate
directions may be found in Table A.2 in appendix A.2.
The cases without shading were used as base cases for reference in Table 3.1.
The results for the cases with external shading and the dynamic window are also
displayed as a percentage of the base case to the right of the column with the actual
results of the simulations. From the table we can see that the external shading cut
the cooling energy down to 31-49% depending on location but the dynamic window
would decrease the cooling energy need even further, down to 9-32%. The cooling
need for Reykjavik is close to eliminated with the dynamic window or decreased
down to 9% and the total energy for heating, cooling and lighting is decreased
by 50%. As more cooling energy is required for other locations than Reykjavik,
a proportional decrease in cooling need has a larger effect on the total heating,
cooling and lighting energy for those locations. Madrid, with no heating need,
shows a decrease down to 47% and 35% in total heating, cooling and lighting energy
consumption for the external and dynamic window respectively.
33
3.2. ENERGY CONSUMPTION
Table 3.1: Supplied energy for the different cases. The case without shading is
used as a base case for the percentage calculations. The percentage number
compares the result to the base case (not the savings). For clearer presentation of
the results, all simulated directions (south, east and west) have been summed up
for each case. For full result see Table A.2 in appendix.
Location
Without
(100%) [kWh]
External
[kWh] [%]
Dynamic
[kWh] [%]
Kiruna
Lighting
Cooling
Heating
Total
282
2574
1610
4466
292
810
1653
2755
104
31
103
62
294
469
1689
2452
104
18
105
55
Reykjavik
Lighting
Cooling
Heating
Total
343
1203
589
2135
352
462
604
1418
103
38
103
66
351
103
617
1071
102
9
105
50
Stockholm
Lighting
Cooling
Heating
Total
182
3711
351
4244
196
1353
381
1930
108
36
109
45
196
940
393
1529
108
25
112
36
Copenhagen
Lighting
Cooling
Heating
Total
220
2863
192
3275
231
1284
207
1722
105
45
108
53
231
822
212
1265
105
29
110
39
Paris
Lighting
Cooling
Heating
Total
192
3421
56
3669
203
1690
73
1966
106
49
130
54
200
1104
80
1384
104
32
143
38
Madrid
Lighting
Cooling
Heating
Total
133
5841
0
5974
146
2658
1
2805
110
46
0
47
143
1922
2
2067
108
33
0
35
34
CHAPTER 3. RESULTS
6000
Lighting
Cooling
Heating
Supplied energy [kWh]
5000
4000
3000
2000
1000
0
Figure 3.8: Total supplied energy, summed for all simulated directions (south, east
and west), for each location and shading type.
Figure 3.8 shows the results displayed in Table 3.1 graphically for a better view
of the composition of utilised energy. Figure 3.9 on page 35 then displays the reduction in total supplied energy between the different cases. The black bars on that
graph represent the reduction in total supplied energy when shading was added to
a window without shading. The two shades of grey represent the introduction of
a dynamic window. The light grey when a window without shading was replaced
with a dynamic window and the darker grey when a window with external shading
was replaced with a dynamic window. On that graph we can see that the dynamic
window decreased the total energy consumption in Reykjavik, Stockholm, Copenhagen, Paris and Madrid about 20-30% from the cases with external shading, but
about 10% in Kiruna.
3.3. THERMAL COMFORT
Total supplied energy reduction [%]
80
70
35
A window without shading is replaced with a window with external shading
A window without shading is replaced with a dynamic window
A window with external shading is replaced with a dynamic window
60
50
40
30
20
10
0
Figure 3.9: Reduction of the total supplied energy when different changes were
introduced to the models. Total values of Table 3.1 were used to produce this
graph to further illustrate the differences.
3.3
Thermal Comfort
For every simulated cases, the PPD index was calculated according to EN ISO
7730:2005 (previously discussed in section 1.4.4) and registered for every half hour
of the simulation. To get a good overview of the thermal comfort for the whole
annual simulation, the PPD index will be presented on duration diagrams for each
location. The duration diagrams only cover the occupied periods as the comfort
indexes are of no importance during vacancy. Total number of occupied hours was,
as earlier stated, 2871 for all cases.
When looking at the PPD index duration diagrams in figures 3.10 to 3.15 the
same trend can be detected on all of them. The cases without shading had much
longer duration of higher PPD than the cases with external shading. The dynamic
window then further improved the thermal comfort for all cases.
The temperature setpoints in the model controlled the air temperature of the
zone. During summer the operative temperature is generally higher than the air
temperature and therefore the operative temperature can exceed the cooling setpoints. The reverse can occur in winter and the operative temperature can reach
below the heating setpoint. This can cause increased occupant dissatisfaction. EN
ISO 7730:2005 categorises the thermal environment according to the PPD index.
PPD < 6 falls inside Category A. This category is very hard to reach and might
cause unreasonable amount of HVAC energy to achieve. PPD < 10 is within Category B and PPD < 15 is in category C. This should provide a base for evaluating
the following diagrams. It should be noted that, in reality, the rate of dissatisfaction might in some cases be considered to be unacceptable, but for this study the
difference between the cases is of more interest than the levels reached.
36
CHAPTER 3. RESULTS
35
Kiruna, Dynamic, South
Kiruna, External, South
Kiruna, Without, South
Kiruna, Dynamic, East
Kiruna, External, East
Kiruna, Without, East
Kiruna, Dynamic, West
Kiruna, External, West
Kiruna, Without, West
PPD index [%]
25
15
5
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.10: PPD index duration diagram for Kiruna.
35
Reykjavik, Dynamic, South
Reykjavik, External, South
Reykjavik, Without, South
Reykjavik, Dynamic, East
Reykjavik, External, East
Reykjavik, Without, East
Reykjavik, Dynamic, West
Reykjavik, External, West
Reykjavik, Without, West
PPD index [%]
25
15
5
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.11: PPD index duration diagram for Reykjavik.
37
3.3. THERMAL COMFORT
35
Stockholm, Dynamic, South
Stockholm, External, South
Stockholm, Without, South
Stockholm, Dynamic, East
Stockholm, External, East
Stockholm, Without, East
Stockholm, Dynamic, West
Stockholm, External, West
Stockholm, Without, West
PPD index [%]
25
15
5
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.12: PPD index duration diagram for Stockholm.
35
Copenhagen, Dynamic, South
Copenhagen, External, South
Copenhagen, Without, South
Copenhagen, Dynamic, East
Copenhagen, External, East
Copenhagen, Without, East
Copenhagen, Dynamic, West
Copenhagen, External, West
Copenhagen, Without, West
PPD index [%]
25
15
5
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.13: PPD index duration diagram for Copenhagen.
38
CHAPTER 3. RESULTS
35
Paris, Dynamic, South
Paris, External, South
Paris, Without, South
Paris, Dynamic, East
Paris, External, East
Paris, Without, East
Paris, Dynamic, West
Paris, External, West
Paris, Without, West
PPD index [%]
25
15
5
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.14: PPD index duration diagram for Paris.
35
Madrid, Dynamic, South
Madrid, External, South
Madrid, Without, South
Madrid, Dynamic, East
Madrid, External, East
Madrid, Without, East
Madrid, Dynamic, West
Madrid, External, West
Madrid, Without, West
PPD index [%]
25
15
5
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.15: PPD index duration diagram for Madrid.
39
3.4. TINTING/BLEACHING CYCLES
3.4
Tinting/Bleaching Cycles
The shading signal was registered for each simulated case for every half hour. We
have already seen the duration of different shading levels in section 3.1 but the
frequency of change in shading state of the dynamic windows is also of importance
as it can effect the lifetime of some products.
The tinting/bleaching cycles were calculated from the shading signals with a
simple Matlab code that may be found in appendix B.1. The code calculates
the accumulated positive changes in the shading signal. It therefore does not only
calculate full shading cycles as one, but also for example two half cycles equal one
cycle. The resulting annual cycles for each location ad direction are displayed in
Table 3.2. Table 3.3 then displays the expected number of cycles for 25 years as a
reference for a possible expected lifetime.
Table 3.2: Shading cycles calculated from the shading signal [Cycles/year].
South
East
West
Kiruna
Reykjavik
Stockholm
Copenhagen
Paris
Madrid
357
365
266
301
264
242
461
410
352
443
434
315
386
377
312
427
445
365
Table 3.3: Shading cycles calculated from the shading signal. Estimation for 25
years [Cycles/25 years].
South
East
West
Kiruna
Reykjavik
Stockholm
Copenhagen
Paris
Madrid
8921
9128
6645
7536
6604
6044
11536
10250
8797
11082
10850
7882
9651
9416
7796
10663
11129
9125
The 25 year shading cycles ranged from around 6000 for the west facing window
in Reykjavik to almost twice that value, 11500 for the south facing window in
Stockholm. Apart from the local climate, the shading strategy effected the number
of shading cycles. During cooling periods, the shading reached almost two whole
cycles per day as may be seen in Figure 2.4 on page 23. The first cycle when the
window is maintaining the illuminance setpoint over the occupied period and the
second when the shading level jumps to 100% when occupancy ends and down to
0% after sunset.
40
3.5
CHAPTER 3. RESULTS
Sensitivity Analysis of SHGC
The decision was made to use glazing parameters for the dynamic window determined according to the ISO 15099 standard and NFRC defined boundary conditions,
and the clear state values of the dynamic window were then used for the window
without shading and the unshaded state of the window with external shading. As
tables 2.6 and 2.7 show, there is a slight difference between parameters calculated
according to NFRC on one hand an CEN on the other. In particular, the SHGC in
the shaded state of the dynamic window is 25% lower according to NFRC than according to CEN. This will have an impact on the result and this chapter will display
the difference for the differently calculated parameters. This sensitivity analysis can
also provide an indication of how the results change if dynamic windows continue
to improve, e.g. if their dark state SHGC lowers even further.
Madrid was chosen as a location for this analysis. Table 3.4 displays the supplied
energy for Madrid. It repeats the result of Table 3.1 for the cases without shading
and with a dynamic window with parameters according to NFRC and adds the
result for a dynamic window with parameters according to CEN for comparison.
As before the supplied energy is summed for all simulated directions. Heating is
not required in Madrid, supplied energy for heating is not displayed in the table.
The case with dynamic window with the NFRC parameters utilised 33% of the
cooling energy of the case without shading but the dynamic window with the CEN
parameters utilised 41% of the cooling energy of the same case. The equivalent
number for the case with external shading in Madrid from Table 3.1 was 47% for
comparison. If the two dynamic window cases for Madrid, NFRC and CEN, are
compared directly, the NFRC case utilised 19% less cooling energy than the CEN
case. This shows that the difference in parameters retrieved by the two calculation
methods had relatively large effect on the energy consumption of the building.
Figures 3.16 and 3.17 show a shading duration digram and a PPD duration
diagram respectively for the NFRC and CEN cases in all simulated directions. No
significant changes are evident but a slight shift occurs in lower levels of shading
where the NFRC cases require a little less shading. The same applies for the PPD
duration diagram. A shift is evident, especially for the lower levels of PPD where
the NFRC is providing better thermal comfort by a small margin.
41
3.5. SENSITIVITY ANALYSIS OF SHGC
Table 3.4: Comparison of supplied energy for the dynamic window results in
Madrid with window parameters calculated according to CEN and NFRC
methods and environmental conditions. Results for all simulated directions (south,
east and west) are summed up for each case. The simulation result for the window
without shading is included as a reference.
Without
(100%) [kWh]
Dynamic-NFRC
[kWh]
[%]
Dynamic-CEN
[kWh]
[%]
Lighting
133
143
108
143
108
Cooling
5841
1922
33
2372
41
Total
5974
2065
35
2515
42
1
Shading signal [ ]
0.8
0.6
Madrid, Dynamic, South, NFRC
Madrid, Dynamic, South, CEN
Madrid, Dynamic, East, NFRC
Madrid, Dynamic, East, CEN
Madrid, Dynamic, West, NFRC
Madrid, Dynamic, West, CEN
0.4
0.2
0
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.16: Shading duration diagram for Madrid during occupancy. Comparison
for window parameters calculated with CEN and NFRC methods and
environmental conditions.
42
CHAPTER 3. RESULTS
25
Madrid, Dynamic, South, NFRC
Madrid, Dynamic, South, CEN
Madrid, Dynamic, East, NFRC
Madrid, Dynamic, East, CEN
Madrid, Dynamic, West, NFRC
Madrid, Dynamic, West, CEN
PPD index [%]
20
15
10
5
0
500
1000
1500
Time duration [h]
2000
2500
Figure 3.17: PPD index duration diagram for Madrid during occupancy.
Comparison for window parameters calculated with CEN and NFRC methods and
environmental conditions.
Chapter 4
Discussion
4.1
Scope and Limitations
Building energy simulation programs are becoming a very powerful tool to make
predictions of energy consumption of buildings and occupant comfort. In some
countries building energy simulations are required before a building permit is
issued for larger buildings to show the design meets regulations. Results of building
energy simulations are also used for classification of buildings in different certification systems.
An accurate building simulation requires experience and effort to achieve but
even if all input parameters correspond to a case in reality, the software still uses
mathematical models as simplification of reality. The mathematical models might
provide a good representation of reality but, as all models, they are incorrect. Building energy simulation programs are benchmark tested for specific cases to verify that
their accuracy is within restrictions. IDA ICE has been benchmark tested according
to relevant standards.
The main limitation of this research is that it was bound to the use of mathematical models. The simulated cases will have errors but the important thing is that
they will all have the same basic errors. This research was set out to be comparative
so if the change between different cases was modelled accurately, the difference in
result should have given a good indication of the effect of the change.
The choices made in the modelling process will bring limitations that effect
the scope of the study. These choices need to be considered when making valid
deductive inferences from this research. The most influential choices made during
this study are listed below and discussed.
•
•
•
•
•
•
WWR is high, 70%.
Maximum possible insolation assumed, no external objects shade the façade.
The shading control has an influence on the result.
No glare estimation is possible in the chosen software, IDA ICE (version 4.6.1).
No occupant override for shading signals.
External shading assumed operable in all weather conditions.
43
44
CHAPTER 4. DISCUSSION
• No air handling unit.
• Energy consumption of dynamic window/external blind not calculated.
The WWR was intentionally set high to amplify the effect of changing window
types. The results may not apply for buildings with low WWR as the solar heat
gain from smaller windows is a smaller proportion of the total cooling load of those
buildings. No adjoining buildings or other objects are assumed to shade the façade so
maximum insolation is assumed, calculated from the IWEC2 weather files. Windows
shaded or partly shaded from direct solar radiation by other buildings or external
objects will not show as much decrease in solar heat gain by operable external
shading blinds or dynamic windows as the model in this study.
The shading strategy produces the energy savings from shading devices. It
chooses the applicable shading signal for each time step and decides the window
parameters. To achieve the same savings in reality, the same shading strategy
must be followed. Some factors in reality may require a deviation from the design
strategy. Occupants might override the strategy signal and request either more or
less shading effecting the energy performance of the building. Occupant override
is difficult to predict but a good shading strategy can minimise the frequency of
overrides. A possibility of glare estimation at workplane would assist in evaluating
the quality of the shading control strategy but that feature is not available in IDA
ICE (version 4.6.1). High winds can also require a change in the strategy for the
external shading devices. Some external shading devices cannot be operated in high
winds but the strategy used in this study assumes they are operable at all times.
The results for energy consumption display the delivered energy and since COP
of the ideal heating and cooling devices is set to unity and no losses are registered,
supplied energy equals the thermal energy needed to maintain the air temperature
setpoints. This estimation was made for simplification and variables for a ventilation unit were eliminated. Availability of sustainable and efficient energy sources
varies between the different locations and this research does not consider where the
supplied energy comes from or how efficient the HVAC units are.
Electrochromic windows consume low voltage electricity when changing shading
states. The energy consumption of the dynamic window itself is not included in
the results. Its energy consumption can however be estimated for a specific window
product by using the shading cycle results. The external shading blind can also be
driven by electricity. The same applies for the external blind as for the dynamic
window, the energy consumption is not calculated but can be estimated for a specific
product from the shading cycle results.
4.2. CONCLUSIONS
4.2
45
Conclusions
This research shows that, during occupancy and over a year, the dynamic window
is clearly active for a much longer time than the external blind for the simulated
cases. However, the dynamic window is rarely in its fully shaded state. This increased duration of shading makes the dynamic window able to reduce the cooling
energy consumption more than for the external shading and provide slightly better
occupant comfort. This is possible even though the external shading is able to reject
more heat when the fully shaded states are compared.
The extent of the measured energy savings for the dynamic window compared to
the external blind is ranging from 10% to 30%, depending on location. If Kiruna is
excluded, this range is from 20% to 30%. This means that the proportional decrease
in energy consumption of using a dynamic window instead of an external shading
is very similar for these locations. However, the scale of the total energy reduction
in kWh variates with location due to the variation in total cooling requirement.
Therefore the results suggest that dynamic windows will save most kWh of energy
in warm climates. Numbers for total energy consumption should not be extracted
from this study, the focus should be on the difference of the results.
The amplitude of the peak thermal loads were not measured in this study for
sizing of HVAC units but still some conclusions can be drawn from the results
in that regard. The results for Reykjavik for example show that annual cooling
requirement can almost be eliminated with the use of dynamic windows. Smaller
cooling equipment may be used and that reduces the installation and maintaining
cost.
Madrid was chosen as one of the locations as it has similar latitude to Denver
where the pilot project of Lee et al. (2014) was conducted. The initial goal was to
compare the results for Madrid and the pilot project but that comparison turns out
to be difficult for number of reasons, for example:
•
•
•
•
•
The climates are different, even though the latitudes are similar.
The shading strategies are different.
The HVAC system in the pilot project is complex.
The office structure and geometry are different.
WWR is smaller in the pilot project.
Even though the two cases are different, the results are in the same vicinity,
large reduction in cooling energy for a dynamic window compared to an unshaded
window. What Lee et al. (2014) are missing in their study is a comparison of the
dynamic windows to an external blind. External window blinds can be a good
option for reducing unwanted solar heat gain.
This research shows that dynamic windows have a potential to help reaching
the EU’s energy goals but the installation and operational costs were not evaluated.
Dynamic windows have developed quickly in the recent years and they will continue to develop in coming years. Cost benefit analyses will need to be conducted
46
CHAPTER 4. DISCUSSION
repeatedly as well as performance simulations for new, improved products.
4.3
Future work
A model component for a dynamic window is not available by default in the current
version of IDA ICE. To create a shading strategy in order to interpolate the shading
signal to simulate dynamic window is time consuming and requires deep knowledge
of the software. If this feature is made available in a simple interface model component it would make it easier for users to introduce a dynamic window to their
design for checking its impact on the result. If this model component is created by
specialists it could also reduce calculation time and increase the reliability of the
model as probability of computational errors would be minimised.
The Simple Window Model in IDA ICE was used in this research. The input parameters for that model are fixed, calculated according to standard environmental
conditions (see section 1.4.2). The more correct Advanced Window Model in IDA
ICE calculates these parameters dynamically for the occurring environmental conditions in the simulation with a model of the IGU. If an advanced window model for
a dynamic IGU would be made available, more accurate results might be obtained.
As mentioned earlier, probability of glare at occupant workplane can not be
estimated with IDA ICE (version 4.6.1). IDA ICE is under constant development
and updates are available at a regular basis. If a beam tracking algorithm will be
introduced to IDA ICE for direct solar radiation prediction within a zone, solar
glare estimations could be possible. It would be interesting to add a control to the
shading strategy based on glare probability at the occupant workplane. That would
give a more accurate shading strategy as it would increase the visual comfort of the
occupant. Increased visual comfort would reduce the need for occupant override for
the shading and the strategy would better correspond to reality.
Bibliography
Baetens, R., Jelle, B. r. P., Gustavsen, A., 2010. Properties, requirements and possibilities of smart windows for dynamic daylight and solar energy control in buildings: A state-of-the-art review. Solar Energy Materials and Solar Cells 94 (2),
87–105.
European Commission, [n.d.]. Buildings. Available from:
http://ec.europa.eu/energy/en/topics/energy-efficiency/buildings [16
March 2015].
Glass for Europe, [n.d.]. GEPVP Code of practice. Available from:
http://www.glassforeurope.com/images/cont/194_929_file.pdf
2015].
[9
May
Hanam, B., Jaugelis, A., Finch, G., 2014. Energy Performance of Windows: Navigating North American and European window standards. In: 14th Canadian
Conference on Building Science and Technology. Toronto.
Hegger, M., Fuchs, M., Stark, T., Zeumer, M., 2008. Energy Manual - Sustainable
Architecture. Birkhäuser Verlag AG, Basel.
IDA ICE, 2014. (Software). Version 4.6.1. Stockholm: EQUA Simulation AB.
Lee, E. S., Fernandes, L. L., Goudey, C. H., Jonsson, C. J., Curcija, D. C., Pang, X.,
DiBartolomeo, D., Hoffmann, S., 2014. A Pilot Demonstration of Electrochromic
and Thermochromic Windows in the Denver Federal Center , Building 41. Tech.
rep., General Services Administration.
Lundström, L., 2012. Weather data for building simulation - New actual weather
files for North Europe combining observed weather and modeled solar radiation.
Master’s thesis.
Mäkitalo, J., 2013. Simulating control strategies of electrochromic windows. Master
thesis, Uppsala Universitet.
Nilson, P. E., 2007. Achieving the Desired Indoor Climate. Studentlitteratur.
47
48
BIBLIOGRAPHY
Pidwirny, M., 2006. Atmospheric Effects on Incoming Solar Radiation. In: Fundamentals of Physical Geography, 2nd Edition. Available from:
http://www.physicalgeography.net/fundamentals/7f.html [6 June 2015].
RDH Building Engineering Ltd., 2014. International Window Standards. Tech. rep.,
Homeowner Protection Office - Branch of BC Housing.
Reinhart, C. F., Voss, K., 2003. Monitoring manual control of electric lighting and
blinds. Lighting Research and Technology 35 (3), 243–260.
Smith, G. B., Granqvist, C. G., 2011. Green Nanotechnology - Solutions for Sustainability and Energy in the Built Environment. CRC Press, Boca Raton, FL.
Stangor, C., 2014. Introduction to Psychology. Ch. 5.2 Seeing, available from:
http://opentextbc.ca/introductiontopsychology/chapter/4-2-seeing/
#Figure5.6 [3 April 2015].
Stine, W. B., Geyer, M., 2001. Power From The Sun. Ch. 2. The Sun’s Energy,
available from:
http://www.powerfromthesun.net/Book/chapter02/chapter02.html [3 April
2015].
Swedish Standards Institute, 2006. SS-EN ISO 7730:2006.
Swedish Standards Institute, 2007. SS-EN 15265:2007.
Swedish Standards Institute, 2011. SS-EN 12464-1:2011.
The International Organization for Standardization, 2003. ISO 15099:2003(E).
University of Minnesota, Lawrence Berkeley National Laboratory, 2014. Windows
for High-Performance Commercial Buildings. Available from:
http://www.commercialwindows.org/technologies.php [24 March 2015].
U.S. Department of Energy, 2011. Building Energy Software Tools Directory:
IWEC. Available from:
http://apps1.eere.energy.gov/buildings/tools_directory/software.
cfm/ID=369/pagename=alpha_list [7 June 2015].
Window 7, 2014. (Software). Version 7.3. Berkeley: Lawrence Berkeley National
Laboratory.
Appendix A
Full Results
A.1
Shading duration
Table A.1: Full result of the shading duration. Total number of occupied hours
was 2871 and total number of vacant hours was 5889. Shading for the dynamic
window was considered “on” when shading signal was above 0,5.
Simulated case
KirunaDynamicSouth
KirunaExternalSouth
KirunaDynamicEast
KirunaExternalEast
KirunaDynamicWest
KirunaExternalWest
ReykjavikDynamicSouth
ReykjavikExternalSouth
ReykjavikDynamicEast
ReykjavikExternalEast
ReykjavikDynamicWest
ReykjavikExternalWest
StockholmDynamicSouth
StockholmExternalSouth
StockholmDynamicEast
StockholmExternalEast
StockholmDynamicWest
StockholmExternalWest
CopenhagenDynamicSouth
CopenhagenExternalSouth
Shading duration
on vacancy [h]
on [h] % of vacancy
1465
1582
1597
1657
1549
1620
1338
1446
1297
1335
1409
1499
1511
1559
1469
1499
1545
1604
1453
1521
25%
27%
27%
28%
26%
28%
23%
25%
22%
23%
24%
25%
26%
26%
25%
25%
26%
27%
25%
26%
49
Shading duration
on occupancy [h]
on [h] % of occup.
1323
349
1234
321
804
23
913
178
774
66
622
14
1689
574
1234
246
1265
235
1462
415
Continued on
46%
12%
43%
11%
28%
1%
32%
6%
27%
2%
22%
0%
59%
20%
43%
9%
44%
8%
51%
14%
next page
50
APPENDIX A. FULL RESULTS
Table A.1 – Continued from previous page
Simulated case
CopenhagenDynamicEast
CopenhagenExternalEast
CopenhagenDynamicWest
CopenhagenExternalWest
ParisDynamicSouth
ParisExternalSouth
ParisDynamicEast
ParisExternalEast
ParisDynamicWest
ParisExternalWest
MadridDynamicSouth
MadridExternalSouth
MadridDynamicEast
MadridExternalEast
MadridDynamicWest
MadridExternalWest
Shading duration
on vacancy [h]
on [h] % of vacancy
1422
1442
1408
1452
1579
1617
1503
1523
1602
1636
1910
1922
1769
1809
1960
2040
24%
24%
24%
25%
27%
27%
26%
26%
27%
28%
32%
33%
30%
31%
33%
35%
Shading duration
on occupancy [h]
on [h] % of occup.
1276
230
924
31
1534
351
1371
146
1252
139
2080
650
1932
338
1677
310
44%
8%
32%
1%
53%
12%
48%
5%
44%
5%
72%
23%
67%
12%
58%
11%
A.2. SUPPLIED ENERGY
A.2
Supplied energy
51
Stockholm-Dynamic-South-NFRC
Stockholm-Dynamic-South-CEN
Stockholm-External-South-NFRC
Stockholm-Without-South-NFRC
Stockholm-Dynamic-East-NFRC
Stockholm-Dynamic-East-CEN
Stockholm-External-East-NFRC
Stockholm-Without-East-NFRC
Stockholm-Dynamic-West-NFRC
Stockholm-Dynamic-West-CEN
Stockholm-External-West-NFRC
Stockholm-Without-West-NFRC
Reykjavik-Dynamic-South-NFRC
Reykjavik-External-South-NFRC
Reykjavik-Without-South-NFRC
Reykjavik-Dynamic-East-NFRC
Reykjavik-External-East-NFRC
Reykjavik-Without-East-NFRC
Reykjavik-Dynamic-West-NFRC
Reykjavik-External-West-NFRC
Reykjavik-Without-West-NFRC
Kiruna-Dynamic-South-NFRC
Kiruna-Dynamic-South-NFRC-Small
Kiruna-External-South-NFRC-Small
Simulation
430
430
430
429
429
429
429
428
430
430
430
429
430
430
429
430
429
429
430
429
429
429
430
430
Equipment
52
52
53
47
73
74
73
69
71
71
70
66
106
107
103
118
118
115
127
127
125
88
116
140
Lighting
347
473
515
1464
276
361
398
993
317
418
440
1254
46
176
463
19
155
348
38
131
392
162
313
351
Cooling
Grand total
44
873
27
982
42
1040
15
1955
188
966
157
1021
189
1089
189
1679
161
979
131
1050
150
1090
147
1896
145
727
138
851
125
1120
248
815
246
948
245
1137
224
819
220
907
219
1165
464
1143
79
938
76
997
Continued on next page
Heating
Table A.2: Full result of supplied energy for all simulated cases.
52
APPENDIX A. FULL RESULTS
Kiruna-Without-South-NFRC-Small
Kiruna-External-South-NFRC
Kiruna-Without-South-NFRC
Kiruna-Dynamic-East-NFRC
Kiruna-Dynamic-East-NFRC-Small
Kiruna-External-East-NFRC-Small
Kiruna-Without-East-NFRC-Small
Kiruna-External-East-NFRC
Kiruna-Without-East-NFRC
Kiruna-Dynamic-West-NFRC
Kiruna-Dynamic-West-NFRC-Small
Kiruna-External-West-NFRC-Small
Kiruna-Without-West-NFRC-Small
Kiruna-External-West-NFRC
Kiruna-Without-West-NFRC
Copenhagen-Dynamic-South-NFRC
Copenhagen-External-South-NFRC
Copenhagen-Without-South-NFRC
Copenhagen-Dynamic-East-NFRC
Copenhagen-External-East-NFRC
Copenhagen-Without-East-NFRC
Copenhagen-Dynamic-West-NFRC
Copenhagen-External-West-NFRC
Copenhagen-Without-West-NFRC
Paris-Dynamic-South-NFRC
Simulation
428
429
429
429
430
429
428
428
428
429
430
430
428
429
428
430
430
429
430
429
428
431
430
429
430
Equipment
113
88
84
96
127
147
122
95
91
110
160
161
157
109
107
64
64
60
72
72
67
95
95
93
64
Lighting
654
307
889
175
296
325
688
291
1036
132
261
263
501
212
649
310
483
1209
276
433
945
236
368
709
397
Cooling
Table A.2 – Continued from previous page
Grand total
73
1268
444
1268
414
1816
564
1264
99
952
99
1000
97
1335
553
1367
541
2096
661
1332
116
967
116
970
116
1202
656
1406
655
1839
14
818
12
989
3
1701
71
849
69
1003
64
1504
127
889
126
1019
125
1356
11
902
Continued on next page
Heating
A.2. SUPPLIED ENERGY
53
Paris-External-South-NFRC
Paris-Without-South-NFRC
Paris-Dynamic-East-NFRC
Paris-External-East-NFRC
Paris-Without-East-NFRC
Paris-Dynamic-West-NFRC
Paris-External-West-NFRC
Paris-Without-West-NFRC
Madrid-Dynamic-South-NFRC
Madrid-Dynamic-South-CEN
Madrid-External-South-NFRC
Madrid-Without-South-NFRC
Madrid-Dynamic-East-NFRC
Madrid-Dynamic-East-CEN
Madrid-External-East-NFRC
Madrid-Without-East-NFRC
Madrid-Dynamic-West-NFRC
Madrid-Dynamic-West-CEN
Madrid-External-West-NFRC
Madrid-Without-West-NFRC
Simulation
430
429
429
429
428
430
430
429
430
430
430
429
429
429
428
428
429
429
430
429
Equipment
66
61
64
65
61
72
72
70
49
49
51
45
36
36
38
33
58
58
57
55
Lighting
617
1298
327
531
925
380
542
1198
673
857
977
2193
578
696
852
1571
671
819
829
2077
Cooling
Table A.2 – Continued from previous page
9
2
40
39
35
29
25
19
0
0
0
0
2
0
1
0
0
0
0
0
Heating
1122
1790
860
1064
1449
911
1069
1716
1152
1336
1458
2667
1045
1161
1319
2032
1158
1306
1316
2561
Grand total
54
APPENDIX A. FULL RESULTS
Appendix B
Matlab Codes
B.1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Code for Shading Cycles
function [Cycles,TimeStep] = IDA_ShadingCycles( Time,ShadingSignal )
%IDA_SHADINGCYCLES calculates the change in the ShadingSignal and
%
sums up the positive changes. That value denotes the total
%
theoretical full cycles the shading has undergone.
%
%
INPUT
%
Time is a vector for the time in hours.
%
ShadinSignal is a vector for the shading signal output.
%
OUTUPTS
%
Cycles is a number for the total number of on/off cycles the
%
shading has undergone.
%
TimeStep shows the timesteps of the data in hours.
%
%
Hannes Ellert Reynisson
%
KTH, Stockholm
%
March 2015
17
18
19
20
%% Time step for the data
% Assumes equal time steps
TimeStep=Time(2)−Time(1);
21
22
23
24
25
%% Shading signal change and cycles
ShadingSignalChange=ShadingSignal(2:end)−ShadingSignal(1:end−1);
ShadingSignalOnlyPositive=ShadingSignalChange.*(ShadingSignalChange>0);
Cycles=sum(ShadingSignalOnlyPositive);
26
27
end
55
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