Energy Performance of Dynamic Windows in Different Climates825465/FULLTEXT01.pdf · 2015. 6....

Energy Performance of DynamicWindows in Different Climates

HANNES E. REYNISSON

Master’s Degree ProjectRoyal Institute of Technology

SE 100–44 Stockholm

June 2015, Sweden

School of Architecture and the Built EnvironmentDivision of Building Technology

Supervisor: Kjartan GuðmundssonExaminer: Kjartan Guðmundsson

TRITA-BYTE Master Thesis 437, 2015

SCHOOL OF ARCHITECTURE

AND THE BUILT ENVIRONMENT

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

AbstractThe European Union (EU) has expressed determination of reducing its energy con-sumption and the EU’s 2010 Energy Performance of Buildings Directive states thatall 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 HVACenergy consumption, lighting energy and peek cooling loads in hot climates in theUS but it is difficult to find any work concerned with colder climates. This study isintended to capture the performance of dynamic windows in a variety of Europeanclimates to explore potential contributions to reaching the EU’s energy goals.

The building energy simulations of this study have been conducted in IDA ICEfor an office section with a large window. Three model variants are compared:without a window shading, with an external window blind and with a dynamicwindow. 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 totalconsumed energy for lighting, heating and cooling in the range of 10%-30% morethan the external blind, depending on location. The reduction is 50%-75% whencompared to the unshaded window. This level of performance can move Europe astep 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, KjartanGuðmundsson. He was always available for discussions and he helped me see thingsin a wider perspective.

I want to send my regards to EQUA Simulation AB for providing me with alicence for IDA ICE. Without this powerful and flexible tool I would not have beenable 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 standardsfor various fenestration parameter calculations.

I would also like to thank D. Charlie Curcija, Ph.D. at Lawrence Berkeley Na-tional Laboratory for assisting me with the computer software Window 7 and forgiving 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 En-gineers.

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

λ Wavelength in meters [m].

σSB The Stefan-Boltzmann constant(5.67×10−8)[W/m2/K4].

bW The Wien’s displacement constant(2,8977721×10−3)[m·K].

c Speed of light in vacuum [m/s].

F Planck spectral radiant exitance[(W/m2)/m or W/m3].

fcl Clothing surface area factor [ ].

g Center of glass solar heat gain factor(see SHGC).

h The Planck’s Constant [J·s].

hc Convective heat transfer coefficient[W/(m2·K)].

Icl Clothing insulation [m2· K/W].

kB The Boltzmann’s constant [J/K].

M Metabolic rate [W/m2].

mg Multiplier for the fully clear solarheat gain coefficient to representthe fully shaded state.

P Total power per square meter emittedby a black body at temperature T[W/m2].

pa Water Vapour partial pressure [Pa].

Ssignal Shading signal for the windowmodel.

T Temperature in Kelvin [K].

ta Air temperature [oC].

tcl Clothing surface temperature [oC].

t̄r Mean radiant temperature [oC].

va Relative air velocity [m/s].

W Effective mechanical power [W/m2].

xiii

Glossary

AU The mean distance from the Sun tothe Earth is 1 AU (1,496×1011 m).

HVAC Heating, ventilating and airconditioning unit.

illuminance The luminous flux inci-dent on a defined surface [lx].

insolation (see solar irradiation).

low-e Low-emissivity.

LSG Light to solar gain ratio,VLT/SHGC.

luminous efficacy Efficiency of a lightsource. The ratio of the luminousflux emitted to the electrical powerused ([lm/W]).

luminous flux Output of light sourcein all directions [lm].

operative temperature The averageof the mean radiant and ambi-ent air temperatures, weighted bytheir respective heat transfer coef-ficients.

SHGC Solar heat gain coefficient. Theratio of solar radiation energy di-rectly and indirectly transmittedthrough glazing assembly of the to-tal incident solar radiation energy.

solar irradiation The total amount ofsolar radiation energy received ona given surface area during a givenperiod [W/m2].

Tsol Shortwave radiation transmissionfactor through a glazing unit.

U-value Heat transfer coefficient. It de-notes the rate of heat loss througha component.

VLT or VT. Visible light transmittance.The ratio of the visible light di-rectly transmitted through a glaz-ing assembly of the incident visiblelight.

WWR Window to wall ratio. The ra-tio of window (glazed) area to thetotal wall area.

xv

Contents

Frontmatter iAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiAbbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixNomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiGlossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiContents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Research Topics of Interest . . . . . . . . . . . . . . . . . . . . . . . 21.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3.1 Dynamic Windows in Application . . . . . . . . . . . . . . . 31.3.2 Simulation of Dynamic Windows in IDA ICE . . . . . . . . . 4

1.4 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.4.1 Solar Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . 51.4.2 Glazing Properties . . . . . . . . . . . . . . . . . . . . . . . . 81.4.3 Dynamic Glazing . . . . . . . . . . . . . . . . . . . . . . . . . 111.4.4 Indoor Climate . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2 Method 132.1 The Model Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.1.1 Building Geometry . . . . . . . . . . . . . . . . . . . . . . . . 142.1.2 Structural Elements and Boundary Conditions . . . . . . . . 142.1.3 Occupancy, Internal Loads and Lighting . . . . . . . . . . . . 152.1.4 Room Heating and Cooling Units . . . . . . . . . . . . . . . . 162.1.5 Zone Lighting and Thermal Setpoints . . . . . . . . . . . . . 16

2.2 Model Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2.1 Window Without Shading . . . . . . . . . . . . . . . . . . . . 172.2.2 Window With External Blind . . . . . . . . . . . . . . . . . . 172.2.3 Dynamic Window . . . . . . . . . . . . . . . . . . . . . . . . 182.2.4 Dynamic Window (CEN Conditions) for Sensitivity Analysis 18

2.3 Shading Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.3.1 Dynamic Window Shading Controls . . . . . . . . . . . . . . 192.3.2 External Blind Shading Controls . . . . . . . . . . . . . . . . 222.3.3 Shading Signal Example . . . . . . . . . . . . . . . . . . . . . 23

2.4 Weather Files and Locations . . . . . . . . . . . . . . . . . . . . . . 23

3 Results 273.1 Duration of Shading Levels . . . . . . . . . . . . . . . . . . . . . . . 273.2 Energy Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

xvi

3.3 Thermal Comfort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.4 Tinting/Bleaching Cycles . . . . . . . . . . . . . . . . . . . . . . . . 393.5 Sensitivity Analysis of SHGC . . . . . . . . . . . . . . . . . . . . . . 40

4 Discussion 434.1 Scope and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . 434.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.3 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Bibliography 47

Appendix A Full Results 49A.1 Shading duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49A.2 Supplied energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Appendix B Matlab Codes 55B.1 Code for Shading Cycles . . . . . . . . . . . . . . . . . . . . . . . . . 55

Chapter 1

Introduction

1.1 Background

According to the European Commission ([n.d.]), buildings are responsible for 40% ofthe total energy consumption in the European Union (EU) and 36% of the total CO2emissions. Required heating energy for new EU buildings is around 12-25% of whatis required for older buildings and around 35% of the current building stock in theEU is over 50 years old. Large energy improvements can be achieved by upgradingthese old buildings to today’s performance standards but even then, buildings willcontinue to be large energy consumers. This will further push legislators to tightenenergy demands and force building constructors, owners and operators to continueto develop with regard to energy efficiency. The EU’s 2010 Energy Performance ofBuildings Directive for example states that all new buildings must be nearly zeroenergy 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 atinternal spaces and to give the occupants a view to the outside. They generallyhave inferior thermal performance in comparison to the surrounding wall and theirmaximum size is limited by the potential solar radiation heat gain and thermalconduction through the window. The solar radiation heat gain can however beused to the advantage of heating buildings in colder climates when needed and tosome extent counterbalance the poor thermal conduction properties of the window.Window glazing composition, coatings and shading can be optimised to obtain adesired balance between thermal gains and losses.

In recent years windows with a dynamic range of shading properties have beenbecoming commercially available for the building sector. They are commonly re-ferred to as “smart” or switchable but will herein be called dynamic. Dynamicwindows provide a control of heat gains and daylight and are believed to have thepotential to become net energy producers, thus requiring less building energy tocounteract 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 energyconsumption (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 climatesin 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 reachingthe EU’s building energy goals, more studies for the variable climate conditionswithin Europe need to be carried out.

1.2 Research Topics of Interest

One of the advantages of dynamic windows when compared to mechanically shadedwindows is that the shading level is adjustable, that is the shading does not haveto be in the two extreme levels, fully shaded (on) or clear (off), but it can have anintermediate value. That way the dynamic windows can maintain certain levels ofnatural light indoors and provide outside view, even when in a shaded state. Inlight of that, it is important to evaluate in what states the dynamic windows willbe during occupancy over the year when comparing to a window with an operableexternal blind. Another reason for evaluating the states of the window is that duringmanufacturing of electrochromic windows, the shading levels are set to predefinedsteps. These steps need to correspond to the most common states of shading thatprovide the best performance for that particular climate. This leads to the followingresearch question.

For one year, what is the duration of different shading levels fora 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 shadingallows a shading control strategy to increase the number of (partly) shaded hoursduring occupancy while maintaining acceptable levels natural indoor lighting andoutside view. The dynamic windows might consequently be able to reject moreunwanted solar heat for a whole year than the operable external blind even thoughthe external blind might be able to reject more heat when both are compared inthe fully shaded state.

What is the annual heating and cooling energy consumption of abuilding 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 onthe indoor climate and occupants need to be controlled or monitored.

For the two options in the previous research question, is there adifference 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 ofinstalling dynamic windows, the number of tinting/bleaching cycles should thusbe an important measurement for the climate condition and the window shadingcontrol strategy.

For one year, what is the number of tinting/bleaching cycles fora dynamic window?

1.3 Related WorkA considerable amount of literature has been published on the potential energy sav-ings of dynamic windows. The most relevant methods and results will be discussedin 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 Ad-ministration (GSA) Region 8 for application of electrochromic and thermochromicwindows in a federal office building. The technical objectives of the projects were tocharacterise and understand how dynamic windows work, estimate HVAC energyconsumption reduction, to understand the effects on occupant comfort, satisfactionand acceptance of the technology and finally to estimate the economical feasibilityof the technology.

The building chosen for the pilot project was building 41 in the Denver FederalCenter, a low-rise office building in Denver, Colorado (latitude 38,75oN). The ex-isting single pane clear windows on the west facing (orientation 67o west of south),second floor were replaced with thermochromic, electrochromic and low-e windowsrespectively 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 atsite in order to characterise dynamic windows. Additionally for the thermochromicwindows, thermal infra-red cameras monitored their condition for detailed evalua-tion of their switching patterns. The HVAC energy reduction was evaluated with abuilding energy simulation conducted using the EnergyPlus1 software. The artificiallighting was not dimmable so the simulation does not account for potential energyvariations for the lighting. Economical feasibility of the technology was evaluatedfrom the simulated energy savings and the additional installation cost. (Lee et al.,2014)

The monitored behaviour of the thermochromic windows shows that they switchbased on both outdoor air temperature and the incident solar radiation (absorbedradiation). For example on a sunny winter day in Denver when the external tem-perature was 5-15oC the windows were tinted for 4 hours in the afternoon. Since

1EnergyPlus is available free of charge from the U.S. Department of Energy’s website.


office buildings with hight internal loads from lighting, occupancy, equipment oftenrequire cooling throughout winter, this switching pattern does therefore not nec-essarily contradict the goal of HVAC energy reduction in office buildings. On thenegative side, the switching pattern can be inconsistent across the pane as the panetemperature might be variable due to edge thermal bridges or partial external shad-ing for example. Energy savings achieved by the thermochromic windows tested inthis project (type B-TC) showed to be the same as for static double-pane low-ewindows (13% and 14% annual HVAC cooling electricity reduction respectively and26% and 28% zone cooling energy reduction for example), compared to the originallyinstalled, single pane, clear windows. Another type of windows (type C-TC) wassimulated where the thermochromic film properties were combined with the low-eglazing. The annual result was 1% increase in zone heating energy, 48% decreasein zone cooling energy and 22% decrease in HVAC cooling electricity consumptioncompared to the original, single pane, clear windows. (Lee et al., 2014)

The result of the electrochromic window energy simulation is very similar tothe type C-TC thermochromic window result. Annual result shows 3% increasein zone heating energy, 45% decrease in zone cooling energy and 22% decrease inHVAC cooling electricity consumption compared to the original, single pane, clearwindows. The most common write-in comment from the occupants was that theelectrochromic window changed the occupant’s perceptions of the outdoor weatherpatterns. No comments were made on the blue colour of the light through theelectrochromic 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 -6oC and +8oCin winter and +16oC and +31oC in summer. The project was limited to this onelocation and this particular building with customised HVAC units. It is thereforedifficult to make inferences from this project of the performance of dynamic windowsin other, different climates.

The building energy performance for the different fenestration systems in thisresearch was obtained from building energy simulations. Even for an as extensive,scientific renovation project as this, the difference in energy performance before andafter is very difficult to measure in reality and computer simulations were believedto 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 ICEsoftware and constructed new control algorithms for more accurate simulation fromthe previously available window and shading controls. The shading controls that arecurrently available by default in IDA ICE are mainly intended to be used for shadingdevices that use an on/off input signal. The software allows for a customisation tocreate intermediate shading signals between 1 and 0, 1 for the window in its fullyshaded state and 0 for the window in a fully clear state. For more information aboutthe shading signal in IDA ICE, see section 2.3.

1.4. THEORY 5

The three custom shading control algorithms created by Mäkitalo (2013) willbe introduced here as they provide a foundation for the combined shading strategyin 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 thewindow. It uses direct and global radiation outside the window as controls forthe shading signal while allowing for a manual schedule. The non-manual controlis not active unless direct radiation hitting the façade is above 50 W/m2. Theshading signal is set to 0,5 if the global solar radiation is above 225 W/m2 andto 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 wereobtained from a study by Reinhart and Voss (2003).

“Operative temp”

The internal operative temperature is used to control the shading signal in thisalgorithm. When a defined maximum temperature is reached, the shading signal isturned to 1 (shading on). Mäkitalo (2013) used 24,5oC (0,5oC below the coolingsetpoint) as the defined operative temperature.

“Workplane”

The “Workplane” algorithm strives to maintain a fixed level of natural illuminationat the chosen location of the occupant workplane by tuning the shading signal. Thiscontrol method can provide the maximum energy savings possible as it can maintainthe minimum amount of natural light needed by the occupants, thus maintainingas much natural light so artificial lighting is not needed but rejecting solar heatfrom the excess natural light that is not needed. The illumination setpoint forthis control algorithm of 500 lx was obtained from SS-EN 12464-1:2011 (SwedishStandards Institute, 2011) for a typical office building.

1.4 Theory

1.4.1 Solar Radiation

The solar radiation is composed of multiple frequencies with different energy inten-sities for each frequency. This is referred to as spectral properties of solar radiation(Smith and Granqvist, 2011). Various factors influence the spectral properties ofsolar radiation reaching the indoors of a building, e.g. sky cloud cover, solar ra-diation incident angle and glazing composition. When designing and evaluating aglazing unit it is essential to realise what the incident radiation’s spectral propertiesare and how the transmission of solar energy can be controlled. This section will


explain in details how the solar radiation spectrum is affected from the emittanceof 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 ab-sorbs all incident electromagnetic radiation but emits, isotropically, as much energyas is theoretically possible for any body at all frequencies. Planck’s law states thespectral radiant exitance of a black body as a function of temperature (T ) (Smithand Granqvist, 2011):

F (λ, T ) = 2πhc2

λ5[exp

(hc

λkBT

)− 1

] . (1.1)

If the radiant exitance is integrated over all frequencies we will get the totalpower emitted by a black body at temperature T . This equation is known as theStefan-Boltzmann equation:

P (T ) = σSBT4. (1.2)

Figure 1.1 shows the spectrum from Equation (1.1) graphically for black bodiesat different temperatures. The figure shows that with increased temperature, thetotal emitted power (the area under the curve) will increase and the peak of thecurve will slide to lower wavelengths. The spectrum peak for a black body atvariable temperature T shifts according to Wien’s displacement law:

λmax(T ) = bWT. (1.3)

The Sun’s exitance spectrum is similar to a black body at temperature T = 6 274K (Smith and Granqvist, 2011). According to the Stefan-Boltzmann equation thetotal emitted power of that black body is P(6 274 K) = 89 MW/m2 and accordingWien’s displacement law the peak of the spectrum is around λmax = 462 nm. Thatwavelength falls inside the visible spectrum and if we take a look at Figure 1.2 wesee that the colour of that wavelength is light-blue. If, on the other hand, we lookat an object at room temperature of T = 20oC = 294 K the total emitted power isP(294 K) = 418 W/m2 and the exitance spectrum peak for that object is λmax =9856 nm according to Wien’s displacement law (assuming black body radiation).That wavelength falls outside the visible spectrum (see Figure 1.2) but inside theinfra-red range.

In reality, the Sun is not a perfect black body and the total exitance power ofthe Sun has been measured to be 63,3 MW/m2 at the Sun’s surface. The radiationdecreases with the distance squared as it spreads out spherically. The mean distancefrom the Sun to Earth is 1 AU = 1,496×1011 m and when the radiation reaches theEarth’s atmosphere, the total power has reduced down to 1 367 W/m2. (Stine andGeyer, 2001)

Gases and particles in the Earth’s atmosphere affect the solar radiation passingthrough it. The radiation can be affected by the three following processes in the

1.4. THEORY 7

0 0.5 1 1.5 2 2.5 30

0.2

0.4

0.6

0.8

1

Wavelength [µm]

Sp

ectr

al In

ten

sity (

W/m

2/µ

m)

x 1

08

0 K

1000 K

2000 K

3000 K

4000 K

5000 K

6000 K

Figure 1.1: Spectral exitance radiation data for perfect black bodies at differenttemperatures 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 highlightedwith 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 processesand reaches the Earth’s surface is called direct solar radiation.


Scattering

Scattering is the process when gas molecules or particles randomly change the direc-tion of the radiation on impact. This process does not affect the wavelength of theradiation 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 iscalled diffused solar radiation.

Reflection

When the direction of the radiation changes 180o (back the same path) on impactwith particles in the atmosphere the insolation is reduced by 100%. This processis called reflection and it mostly occurs in clouds when radiation hits particles ofliquid and frozen water.

Absorption

Some gases and particles in the atmosphere have the ability to absorb incomingsolar radiation. The radiation will then convert to thermal energy stored in thesubstance. This process will reduce the energy in the initial solar radiation but thesubstance will start to emit its own radiation. That emitted radiation is on theinfra-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 reflectedback. The size of that proportion is dependent on the window surface, incident angleand wavelength of the radiation. A part of the radiation that is not reflected offthe pane is absorbed as heat but the remaining proportion is directly transmittedthrough the pane. The energy absorbed in the pane as heat is then transferredout of the pane to both sides by convection, conduction and radiation. The heattransferred in that manner to the inside of the pane, opposite side of the source,is called indirect transmittance. (University of Minnesota and Lawrence BerkeleyNational Laboratory, 2014) Figure 1.3 shows a drawing of the radiation energylosses through a glass pane.

The following four properties of windows are of most interest when quantifyingtheir 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 glasspane. Remake from University of Minnesota and Lawrence Berkeley National

Laboratory (2014).

U-value can affect both heat gains/losses due to temperature differences betweenthe inside and the outside of a window, and also the indirect transmission of solarradiation energy absorbed by outermost pane, although the SHGC is used to quan-tify the solar radiation energy transmission, both direct and indirect, as a ratio ofthe total incident solar energy. The VLT coefficient is a measurement of the visibleradiation 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 day-light, affecting the cooling load. (University of Minnesota and Lawrence BerkeleyNational Laboratory, 2014)

The glazing industry has standardised methods of calculating these parametersfor performance comparison of different products. Both Comité Européen de Nor-malisation (CEN) and U.S. National Fenestration Rating Council (NFRC) haveeach developed their own method for determining these parameters. Not only dothey have different calculation procedures and reported partial properties, but theyuse different boundary conditions in the calculations. (RDH Building EngineeringLtd., 2014) This can result in mismatching parameters when using both methodsor unfair comparison between two products evaluated with the separate methods.

NFRC uses calculation procedures from the international standard ISO 15099Thermal performance of windows, doors and shading devices - Detailed calculations(The International Organization for Standardization, 2003) for determination of U-value, SHGC and VLT with NFRC defined boundary conditions (see Table 1.1).European methods, however, follow other standards: EN 410 for SHGC and deter-mination of luminous and solar characteristics and EN 673 for U-value according toGEPVP or Glass for Europe’s ([n.d.]) code of practice. These methods use CENdefined boundary conditions. D. Charlie Curcija, Ph.D. at the Lawrence Berkeley


National Laboratory (LBNL) claims that the standards for the European methodsare outdated and inaccurate (personal communication, May 8, 2015).

Hanam et al. (2014) state that neither the NFRC nor CEN method can beconsidered “better”, they both have different sets of limitations. The NFRC methodis said to use more accurate algorithms that are able to compare all products underthe same conditions but the CEN method is said to use more realistic environmentalconditions.

Table 1.1 displays the environmental conditions assumed for the different meth-ods and Table 1.2 shows the corresponding surface heat transfer film coefficientsassumed. The surface film coefficients for calculations of SHGC are for “summerconditions” opposite to the “winter conditions” used for U-value calculations. (RDHBuilding Engineering Ltd., 2014)

Table 1.1: Environmental conditions for different methods of determining windowparameters. Temperatures are in oC and solar radiation in W/m2.

Method Exterior Interior Solartemperature temperature radiation

NFRC (Winter) -18 21 -NFRC (Summer) 32 24 783CEN (Winter) 0 20 -CEN (Summer) 30 25 500

Table 1.2: Surface heat transfer film coefficients for different methods ofdetermining window parameters.

Method Film Coefficient Comments[W/m2K]Exterior Interior

NFRC (Winter) 26,0Convection only. Radiation model used.Interior coefficients depend on framesystem.

NFRC (Summer) 15,0Convection only. Radiation model used.Interior coefficients depend on framesystem.

CEN (Winter) 25,0 7,7Combined convection and radiation co-efficient (ISO 10292 for center of glasssimulations).

CEN (Summer) 8,0 2,5 For SHGC calculations.

1.4. THEORY 11

1.4.3 Dynamic Glazing

Three different technologies are commonly used to achieve the dynamic nature ofthe shading for these types windows in buildings: chromic materials, liquid crys-tals and electrophoretic or suspended-particle devices (SPD). The chromic materialscan be divided in four categories based on their control mechanism: electrochromic,gasochromic, photochromic and thermochromic. Photochromic and thermochromicdevices are controlled by light and heat respectively so, in general building applica-tion, their state cannot be controlled by a building management system or manuallyadjusted by the user. This lack of controllability renders photochromic and ther-mochromic less feasible to the others and their control system will not be simulatedin this research. (Baetens et al., 2010)

1.4.4 Indoor Climate

When comparing building energy performance for different building componentsor different control strategies the occupant comfort levels need to be within thesame range or they need to be registered and evaluated for a fair comparison. Forcase studies, energy savings obtained at the cost of lower comfort levels need to besubjectively justified.

The indoor climate affects products, processes and the occupant comfort, healthand productivity. For office building the effects of the indoor climate on the occupantis more relevant than on products and processes and since the research is aimed atoffice buildings this chapter will focus on effects on the occupant.

One of the most common methods in Europe for evaluating the thermal indoorclimate is stated in the EN ISO 7730:2005 standard (Hegger et al., 2008). This stan-dard provides analytical methods to numerically grade the indoor thermal climateaccording to occupant impression. It also specifies local thermal comfort criteriaconsidered acceptable both for general- and local thermal discomfort. (SwedishStandards Institute, 2006).

EN ISO 7730:2005 uses the Fanger indexes, predicted mean vote (PMV) and pre-dicted percentage dissatisfied (PPD), to analyse and interpret the occupant thermalcomfort. The PMV index predicts the mean value of votes of a large group of peo-ple 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 thePMV index that establishes a quantitative prediction of the percentage of thermallydissatisfied occupants. (Swedish Standards Institute, 2006)


Table 1.3: The 7-point thermal sensation scale of the PMV index.

PMV Explanation(Predicted Mean Vote)3 Hot2 Warm1 Slightly warm0 Neutral-1 Slightly cool-2 Cool-3 Cold

The PMV index is calculated according to equations 1.4 to 1.7. The PMVindex is a function of a number of different variables. The variable notations in theformulas are explained in the nomenclature.

PMV =[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, 7 · 10−5 ·M · (5867− pa)− 0, 0014 ·M · (34− ta)

− 3, 96 · 10−8 · fcl ·[(tcl + 273)4 − (t̄r + 273)4

]− fcl · hc · (tcl − ta)

(1.4)

where

tcl = 35, 7− 0, 028 · (M −W )− Icl

·[3, 96 · 10−8 · fcl ·

[(tcl + 273)4 − (t̄r + 273)4

]+ fcl · hc · (tcl − ta)

],

(1.5)

hc ={

2, 38 · |tcl − ta|0,25 for 2, 38 · |tcl − ta|0,25 > 12, 1 · √va

12, 1 · √va for 2, 38 · |tcl − ta|0,25 < 12, 1 · √va(1.6)

and

fcl ={

1, 00 + 1, 290 · Icl for Icl ≤ 0, 078 m2 ·K/W1, 05 + 0, 645 · Icl for Icl > 0, 078 m2 ·K/W

. (1.7)

When the PMV index has been evaluated, the PPD can be calculated accordingto 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 modelsas physical models require much more effort, time and cost. The computer softwarechosen for the task was IDA Indoor Climate and Energy or IDA ICE (2014). IDAICE is a whole year, dynamic, multi-zone simulation application for indoor thermalclimate and energy consumption of entire buildings. The mathematical models inIDA 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 triedout for the task as it has a built in feature of simulating a dynamic window and ithas been used in other studies (e.g. Lee et al. (2014)). The transparency of IDAICE made it much easier to understand and its extremely flexible nature made itpossible to customise the models to needs and to build the dynamic behaviour of asmart 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 ofa defined section of a building is simulated, see Figure 2.1. All loads and schedulesresemble activities for an office building with operation hours from 07:00 to 18:00every weekday. All the simulated cases are based on the model foundation thatis described in section 2.1. For estimating the impact of different window shadingmethods, three model variations are created: one variation with a dynamic win-dow, one with an operable external blind for comparison and one variation with anunshaded 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 windowperformance in various climates representing different latitudes. The Shoe Box isturned with the window facing south, east and west in separate simulations foreach location and for each direction the three model variations are simulated. ForMadrid, one extra model variation is run for a sensitivity analysis of the dynamicwindow 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 (SwedishStandards Institute, 2007) for validation tests of building simulation software. Thatgeometry has a high window to wall ratio (WWR) and that was considered optionalfor emphasising the impact of different fenestration systems on the building’s per-formance because energy savings from electrochromic windows should be greaterwith larger windows (Lee et al., 2014). It should be kept in mind when evaluatingthe results of this study that the level of energy savings obtained in buildings withas large WWR as the Shoe Box might not be reached in buildings with smallerWWR.

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 mand height 2,8 m. That gives an external surface of 10 m2, floor area of 20 m2 anda zone volume of 55 m3. The window has a height of 2 m and a width of 3,5 m witha 0,05 m wall margin on the sides and the top. The window surface is therefore 7m2 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.

2.1. THE MODEL BASE 15

Table 2.1: External wall materials used for the all models.

External wallOutside

Materials and thicknessRender 1 cmLight insulation 25 cmL/W concrete 25 cmRender 1 cm

Inside

Total thickness [cm] 52Total U-value [W/m2K] 0,1136

The internal structural elements have adiabatic boundary conditions so the netheat transfer across them is zero but they are able to store heat. The internal wallsare made of gypsum and the floor and ceiling are made of concrete. The internalelement materials are displayed in Table 2.2.

Table 2.2: Internal structural components used for all models.

Internal walls Internal floor/ceilingOutside

Materials and thicknessGypsum 2,6 cm Concrete 15 cmAir 7 cm L/W Concrete 2 cmGypsum 2,6 cm Floor Coating 1 cm

Inside

Total thickness [cm] 12,2 18Total U-value [W/m2K] 1,707 2,237

2.1.3 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 ICEassumes the surface area of 1,8 m2/person that corresponds to Nilson’s (2007) 1,77m2/person for the average Scandinavian population. This means that the occupantgenerates 126 W of heat in the model. For the thermal comfort calculations clothinginsulation is assumed 85 ± 25 clo = 0,13 ± 0,04 m2 K/W. Variable clothing levelsrepresent the person’s ability to change clothing according to temperature. Variableclothing 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 fromthe window. The workplane height is set to 0,8 m. Equipment in the Shoe Box


is assumed to use 150 W of electricity power and generate 150 W of heat. Theequipment is only turned on during occupancy.

Two lighting units are in the ceiling, each with 50 W input power. Their lumi-nous efficacy is set to 20 lm/W thus able to produce in total 2000 lm luminous fluxat full power.

2.1.4 Room Heating and Cooling Units

For simplification, the Shoe Box model has idealised local room units for heating andcooling. The units are assumed to have no power limitations, thus able to maintainsetpoint 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 emissionlosses are registered. By having this configuration, the registered supplied energycan be used as a measurement of the thermal energy flows required to maintain theheating and cooling setpoints. For the ideal heater, the supplied energy equals thesensible heat provided for the zone as the heater does not add or remove moisturefrom the air. The supplied energy for the ideal cooler equals the sum of the latentand the sensible heat removed from the zone as the cooler can remove moisture fromthe air. An air handling unit (AHU) is not connected to the model as air changesand 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 nat-ural illuminance at workplane. The minimum natural illuminance for full artificiallighting to be active is set to 100 lx and the artificial lighting is turned of at above500 lx natural illuminance. Between these points the artificial lighting is given alinearly 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 themeasured 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 morecommon in reality than to use the operative temperature. The heating setpointis set to 20oC and the cooling setpoint is set to 26oC. A setpoint shift of ±6oC isused 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 StandardsInstitute, 2006) (see Table 2.3). The values in that table are for the operativetemperature and to use them for the air temperature will have an impact on theoccupant comfort. The occupant comfort will therefore have to be evaluated whencomparing the results.

2.2. MODEL VARIATIONS 17

Table 2.3: Operative temperature requirements for sedentary activities accordingto EN ISO 7730 (Swedish Standards Institute, 2006).

Category Summer Winter(cooling period) (heating period)

A 24,5oC ± 1,0 22oC ± 1,0B 24,5oC ± 1,5 22oC ± 2,0C 24,5oC ± 2,5 22oC ± 3,0

2.2 Model Variations

All simulated models are based on the model described in section 2.1. The windowshading parameters and the shading strategies are the only things that changebetween 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 modelis without any type of shading. The window parameters used for this case aredisplayed in Table 2.4. The values are obtained from the clear state of the dynamicwindow 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.

SHGC Tsol Tvis U-valueClear state 0,413 0,331 0,602 1,56

2.2.2 Window With External Blind

The second model includes an external, operable, window blind. The same clearstate values are used for this window as for the window without shading and thedynamic window. The shaded state values of this window are obtained by built inmultipliers that represent an active external blind. In IDA ICE the shaded statevalues are not entered directly but they are calculated by multiplying the clear statevalues and the relevant multipliers (see Equation 2.1 in section 2.3). The windowparameters for this case are displayed in Table 2.5. The shading control strategy iscustom made for the external blind using the same setpoints as the shading controlstrategy for the dynamic window in the following section. More information on theshading controls and the shading control strategies may be found in section 2.3.


Table 2.5: Window parameters used in IDA ICE for the externally shaded window.


Shaded state 0,058 0,030 0,054 1,56

2.2.3 Dynamic Window

In a literature review of properties, requirements and possibilities of dynamic win-dows 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 state-of-the-art technology for daylight and solar energy purposes”. Based on that, thedynamic window properties chosen for the energy simulation model in this studyare representative for a high-end electrochromic window product. Even though itis not the purpose of this research to model a specific product or technology ofdynamic glazing, the properties that are chosen for the dynamic glazing needed tobe realistic and show the potentials for products in the near future.

The extreme state parameters used for the dynamic window in the model aredisplayed in Table 2.6. They represent an actual electrochromic window productby SAGE Electrochromics: SageGlass® Clear w/SR2.0. These values are availablein the product specifications from the manufacturer and the same values can be ob-tained by using the computer program Window 7 (2014) to calculate the combinedinsulated glass unit (IGU) parameters with NFRC environmental conditions andISO 15099 method for thermal and optical calculations (see section 1.4.2). Infor-mation on the shading controls may be found in section 2.3.

Table 2.6: Window parameters used in IDA ICE for the dynamic window (NFRCconditions).


Shaded state 0,087 0,005 0,009 1,56

2.2.4 Dynamic Window (CEN Conditions) for SensitivityAnalysis

As mentioned in the previous section the dynamic window parameters providedby the manufacturer are calculated with NFRC environmental conditions and ISO15099 method for thermal and optical calculations. These methods are used inNorth America but other methods are commonly used in Europe as discussed insection 1.4.2. Table 2.7 displays the window parameters for the same product butcalculated according to European, CEN defined, methods in Window 7 (2014).

2.3. SHADING CONTROLS 19

Table 2.7: Window parameters used in IDA ICE for the sensitivity analysis of theSHGC (CEN conditions).


Shaded state 0,116 0,005 0,009 1,47

The two methods produce slightly different SHGC for the extreme states as maybe seen when Table 2.6 and Table 2.7 are compared. The NFRC method gives ashaded 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 morein favour of the dynamic window product and makes it look like it is able to rejectmore heat in a shaded state than according to CEN methods.

The NFRC calculated values are chosen to represent the dynamic window inthis research (see section 1.4.2) but a sensitivity analysis is conducted to see theeffect of using the CEN values in parallel.

2.3 Shading ControlsThe 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 shadingsignal is 0). In this model, the shaded state values are obtained by multiplyingthe clear state values with relevant multipliers (mg in equation 2.1). So the shadedstate values are not entered directly to the model but calculated from the clear statevalues and their multipliers. When the shading signal takes on a value between 0and 1 the parameters will take on a linearly interpolated value between the twoextreme states. This can be described mathematically by the following equationas the center of glass SHGC is used as an example. The center of glass SHGC isrepresented by g in the equation, mg represents the multiplier, Ssignal is the shadingsignal and the subscript 0 (e.g., g0) denotes the original, fully clear state value.

g = (mg · g0) · Ssignal + g0 · (1− Ssignal) (2.1)

2.3.1 Dynamic Window Shading Controls

An electrochromic window unit has predefined shading steps built in. The number ofsteps and their levels is defined by the manufacturer and it cannot be changed afterproduction. (Lee et al., 2014) The control strategy in this energy simulation doesnot account for these predefined shading steps, but assumes the window can takeon any shading state linearly interpolated between the two extreme shading states(on/off). Dynamic windows have a certain response time and the pane can appearnon-uniform while changing states. Mäkitalo (2013) made a sensitivity analysis ofthe response time of a dynamic window on the simulated HVAC energy consumption


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 shadingresponse time and all requested changes in shading occur instantly.

The combined shading strategy used for the dynamic window in this researchis largely based on the three different components created by Mäkitalo (2013) dis-cussed in section 1.3.2 but with few additional components. A flowchart of thecombined 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 berejected (during cooling periods) or harvested (during heating periods). It uses themean internal air temperature and a 24 hour sliding average of the external ambientair temperature as controls. During occupancy, if either the sliding average externalair temperature exceeded 8oC or the internal air temperature exceeded 23,5oC thesolar heat is to be rejected. During vacancy these setpoints are different. Duringvacancy the setpoint for the sliding average of the external air temperature is 7oCand the setpoint for the air temperature is 22oC on weekdays and 21oC duringweekends. These setpoints are obtained by trial and error for trying to find thebalance temperature for the building and its thermal loads.

Global radiation of 225 W/m2 on façade is used as a setpoint for the windowto 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 dynamicshading 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 the800 lx at workplane. Here it is raised from 500 lx when the solar heat is wanted toincrease positive solar heat gain yet still providing protection from excessive solarheat gain. When no direct radiation hits the façade, these setpoints are inactive, alsowhen the solar heat is not wanted (during cooling periods) the shading maintains500 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 21

Figure 2.2: Flowchart of the control strategy used for the dynamic window.


2.3.2 External Blind Shading Controls

The control strategy for the external blind uses the same setpoints as the strategy forthe dynamic window. A flowchart of the strategy for the externally shaded windowmay be found in Figure 2.3. The main difference between the two strategies is thatthe components that maintain a fixed level of natural illuminance at workplane arenot present in the one for the external blind due to the fact that the external blindcan only be on or off. The component that measures if the global radiation is above225 W/m2 is also left out so during occupancy the external blind is only turned onif direct solar radiation is above 50 W/m2 on the façade and the global radiation isabove 450 W/m2.

Figure 2.3: Flowchart of the control strategy used for the mechanically, externallyshaded window.

2.4. WEATHER FILES AND LOCATIONS 23

2.3.3 Shading Signal Example

An example of the shading signal output of the two different shading strategiesmay be found in Figure 2.4. The figure displays the shading signal output for oneweekday in April for Stockholm. All numerical output values of the simulationsare registered for half hour intervals so the curves are not completely smooth. Thefigure shows the dynamic window striving to maintain a fixed level of illuminanceat workplane during occupancy between 07:00 and 18:00 but after 18:00 the signaljumps to full shading. The external blind is only able to be “on” or “off” so theshading signal jumps between 1 for shading “on” and 0 for shading “off”. Between12:00 and 14:00 the external blind jumps to “on” to prevent excessive radiation asthe global radiation is above 450 W/m2 and direct radiation hits the façade. Afteroccupancy at 18:00, the signal jumps again to “on” using the same strategy as thedynamic window during vacancy.

0 2 4 6 8 10 12 14 16 18 20 22 240

0.2

0.4

0.6

0.8

1

Time [h]

Shadin

g s

ignal [ ]

StockholmDynamicSouthStockholmExternalSouth

Figure 2.4: Example of the output of the shading strategy. These shading signalsare for one weekday in April for Stockholm.

2.4 Weather Files and Locations

Geographical locations for the different simulations are chosen so that they havean equal latitudinal spread to capture the variety of available daylight hours andtemperature in Europe. Reykjavik and Stockholm are chosen as they are of interestto the author and Madrid is chosen as it has a close latitude to Denver where thepilot project of Lee et al. (2014) was conducted. The other three locations, Kiruna,Copenhagen and Paris, provide equal latitudinal spread as is displayed in Figure2.5.

Two main types of weather files are currently available from American Societyof Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) for building


KirunaReykjavik

Stockholm

Copenhagen

Paris

Madrid

15 ° W

0° 15

° E 30

° E

45° E

30 ° N

45 ° N

60 ° N

75 ° N

Figure 2.5: Location of cities where energy simulations are performed.

energy simulations, both types are typical meteorological year (TMY). The oldones, International Weather for Energy Calculations (IWEC), are derived from 18years (1982-1999) of DATSAV3 hourly weather date from the National ClimaticData Center. 12 typical meteorological months (TMM) are chosen from that periodto compose a TMY. Solar radiation is calculated from cloud cover and Earth-Sungeometry. (U.S. Department of Energy, 2011)

Lundström (2012) found the direct solar radiation in the old IWEC files to beunderestimated of about 20-40% for Northern Europe. He states that those filesshould be used with care if solar radiation has significant effect on the result.

A second version, IWEC2 weather files were developed through ASHRAE Re-search Project RP-1477. The underestimation of direct solar radiation seems tobe fixed, at least for Stockholm and Helsinki. Both IWEC and IWEC2 use thesame Zang-Huang model for global horizontal solar radiation from cloud cover buta new model is used for splitting the global horizontal radiation to diffuse and directnormal solar radiation. In addition the IWEC2 stations use different regression coef-ficients for different Köppen-Geiger zones instead of using the same set of regressioncoefficients for all locations in the old IWEC. (Lundström, 2012)

In light of the above, IWEC2 files are selected for the simulations in this research.

2.4. WEATHER FILES AND LOCATIONS 25

As the IWEC2 weather files are for TMY, the year chosen for the annual simulationsonly affects how weekdays are arranged for the year, for example if 1 January is aMonday. 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 LongitudeKiruna 67,817 N 20,333 EReykjavik 64,132 N 21,9 WStockholm (Bromma) 59,367 N 17,9 ECopenhagen (Kastrup) 55,617 N 12,65 EParis (Orly) 48,717 N 2,383 EMadrid (Getafe) 40,3 N 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 win-dows for the whole simulated period of the different cases. As the dynamic windowwas able to take on any interpolated shading signal and the values varied constantlythroughout the period, the best way to show the most frequent states of the windowis to display a duration diagram. The values used for a duration diagram plot aresorted in an ascending order and plotted in the sorted order. From these plots, onecan choose two points on the Shading signal axis (y-axis) and read the duration ofvalues between these states on the Time duration axis (x-axis) or vice versa.

The following duration diagrams for the shading signal are for occupant hoursonly. As the shading signal during vacancy could only either be “on” or “off” forboth 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 barchart in Figure 3.7 where the vacant shading signal duration is compared to theoccupied shading signal duration.

Each duration diagram includes results for one location. The externally shadedwindow results will appear as vertical lines through the graph as the signal can onlybe either 0 or 1. The time duration above that line is therefore the shaded stateduration. The total number of occupied hours is 2871 for all cases, thus being themaximum value on the Time duration axis. Total number of hours for the 365 dayssimulated is 8760 and the unoccupied hours are 5889.

The shading duration diagram for Kiruna in Figure 3.1 shows that for eachdirection the dynamic window was in its clearest state for about half of the occupiedtime. The external shading was active for around 300 hours for both south and eastfacing directions. The west facing window required less shading, with the dynamicwindow clear for about two thirds of the occupied time and the external shadingwas almost never required.

27

28 CHAPTER 3. RESULTS

0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

Time duration [h]

Shadin

g s

ignal [ ]

Kiruna, Dynamic, SouthKiruna, External, SouthKiruna, Dynamic, EastKiruna, External, EastKiruna, Dynamic, WestKiruna, External, West

Figure 3.1: Shading duration diagram for Kiruna during occupancy.

0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

Time duration [h]

Shadin

g s

ignal [ ]

Reykjavik, Dynamic, SouthReykjavik, External, SouthReykjavik, Dynamic, EastReykjavik, External, EastReykjavik, Dynamic, WestReykjavik, External, West

Figure 3.2: Shading duration diagram for Reykjavik during occupancy.

Less shading was required for Reykjavik than for Kiruna. Figure 3.2 shows thatsimilar shading duration pattern applied in all directions for Reykjavik with a slightshift. The south facing windows were shaded for the longest and the west facingthe shortest.

3.1. DURATION OF SHADING LEVELS 29

0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

Time duration [h]

Shadin

g s

ignal [ ]

Stockholm, Dynamic, SouthStockholm, External, SouthStockholm, Dynamic, EastStockholm, External, EastStockholm, Dynamic, WestStockholm, External, West

Figure 3.3: Shading duration diagram for Stockholm during occupancy.

The south facing windows in Stockholm had the longest shading duration byfar of the simulated directions (see Figure 3.3). The dynamic window facing southwas in a shading state over 50% in more than half of the occupied time and theexternally shaded window facing south was shaded for 500 occupied hours or about20% of the occupied time. In east and west facing directions the dynamic windowwas in a fully clear state for about half of the occupied time.

0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

Time duration [h]

Shadin

g s

ignal [ ]

Copenhagen, Dynamic, SouthCopenhagen, External, SouthCopenhagen, Dynamic, EastCopenhagen, External, EastCopenhagen, Dynamic, WestCopenhagen, External, West

Figure 3.4: Shading duration diagram for Copenhagen during occupancy.

The shading duration patterns for Copenhagen were similar to the ones forStockholm with a slight shift to the right. One apparent change from the Stockholmdiagram was that the curves for the east facing windows were closer to the curvesfor the south facing windows. Stockholm and Copenhagen are in the same time


zone but Copenhagen lies further west than Stockholm. The occupancy was for thesame hours of the day so for Copenhagen the occupancy started and ended whenthe sun was further east than in Stockholm. That may explain why the east shadingwas active relatively longer in Copenhagen than in Stockholm during occupancy.The same might apply to other locations, i.e., inconsistency between solar time andclock time.

0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

Time duration [h]

Shadin

g s

ignal [ ]

Paris, Dynamic, SouthParis, External, SouthParis, Dynamic, EastParis, External, EastParis, Dynamic, WestParis, External, West

Figure 3.5: Shading duration diagram for Paris during occupancy.

The shading duration diagram for Paris in Figure 3.5 was very similar to theone for Copenhagen except the shading duration curve for the west facing windowsmoved slightly to the left, closer to the curve for the east facing window.

0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

Time duration [h]

Shadin

g s

ignal [ ]

Madrid, Dynamic, SouthMadrid, External, SouthMadrid, Dynamic, EastMadrid, External, EastMadrid, Dynamic, WestMadrid, External, West

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 stateof over 50% shading for about 75% of the occupied time and the external shadingin the same direction was active for around 25% of the occupied time.

Figure 3.7 combines the shading duration from the previous graphs for occu-pancy with the shading duration during vacancy. During vacancy the dynamicwindow was only able to take on the two extreme states, i.e., “on” or “off”, so itbehaved in in the same way as the external shading. The black columns on thegraph therefore represent the on state during vacancy. For the dynamic windowduring occupancy the shading was considered “on” when the shading level reachedabove 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 duringvacancy between the different cases was not significant, although the Madrid casesutilised the shading apparently more than the others during vacancy. The greycolumns for occupancy were transferred from duration diagrams in figures 3.1 to3.6 so we have already compared the levels different cases but on the bar chart wecan 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 stateduring occupancy for as long time as the shading was “on” during vacancy. Theexternally shaded windows in most cases were however in a shaded state duringoccupancy only a fraction of the time they were in a shaded state during vacancy.

0

500

1000

1500

2000

2500

3000

3500

4000

KirunaDynam

icSouth

KirunaExternalSouth

KirunaDynam

icEast

KirunaExternalEast

KirunaDynam

icWest

KirunaExternalWest

ReykjavikD

ynam

icSouth

ReykjavikExternalSou

thRe

ykjavikD

ynam

icEast

ReykjavikExternalEast

ReykjavikD

ynam

icWest

ReykjavikExternalW

est

StockholmDynam

icSouth

StockholmExternalSouth

StockholmDynam

icEast

StockholmExternalEast

StockholmDynam

icWest

StockholmExternalWest

Cope

nhagenDynam

icSouth

Cope

nhagenExternalSouth

Cope

nhagenDynam

icEast

Cope

nhagenExternalEast

Cope

nhagenDynam

icWest

Cope

nhagenExternalWest

ParisDynam

icSouth

ParisExternalSouth

ParisDynam

icEast

ParisExternalEast

ParisDynam

icWest

ParisExternalWest

Madrid

Dynam

icSouth

Madrid

ExternalSouth

Madrid

Dynam

icEast

Madrid

ExternalEast

Madrid

Dynam

icWest

Madrid

ExternalWest

Time du

ratio

n [h]

During vacancy During occupancy

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 vacancythe 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.


3.2 Energy ConsumptionThere were four components in the models that required supplied energy: heating,cooling, lighting and equipment. As number of occupant hours was the same for allmodels, the supplied energy for equipment was the same for all cases according tooccupant schedule. Total sensible heat gain caused by equipment was 430 kWh/yearfor all simulations. The heat generated by the equipment was equal to its suppliedenergy. This energy will not be included in the energy consumption comparison.

Internal gain caused by the occupant was not included in the supplied energyresults as that thermal energy may be considered as free. Even though the occupantactivity level was constant and the same for all simulations the power emitted by theoccupant varied since the clothing level (the thermal resistance) is variable. Thisvariance was not registered in the following results.

The lighting is controlled by illuminance sensors so supplied energy and thermalgains for the lights vary between simulations. As discussed in section 2.1.4, thedelivered energy to the room units was equal to the thermal energy flows provided bythe units. For the heater, the supplied energy was equal to the sensible heat addedto the zone as the latent heat provided by the heater was always zero. Condensationcould occur in the cooler so the supplied energy for the cooler was equal to the sumof the sensible and the latent heat removed from the zone. The results only show thesupplied energy to the ideal heater and the ideal cooler but not distinction betweensensible and latent heat removed from the zone by the cooler.

Table 3.1 displays a summary of the supplied energy result for the simulatedcases. The table does not display the supplied energy for each simulated directionbut it has been summed for all directions for each case. Supplied energy in separatedirections 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 alsodisplayed as a percentage of the base case to the right of the column with the actualresults of the simulations. From the table we can see that the external shading cutthe cooling energy down to 31-49% depending on location but the dynamic windowwould decrease the cooling energy need even further, down to 9-32%. The coolingneed for Reykjavik is close to eliminated with the dynamic window or decreaseddown to 9% and the total energy for heating, cooling and lighting is decreasedby 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 energyconsumption for the external and dynamic window respectively.

3.2. ENERGY CONSUMPTION 33

Table 3.1: Supplied energy for the different cases. The case without shading isused as a base case for the percentage calculations. The percentage number

compares the result to the base case (not the savings). For clearer presentation ofthe 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 External Dynamic(100%) [kWh] [kWh] [%] [kWh] [%]

Kiruna

Lighting 282 292 104 294 104Cooling 2574 810 31 469 18Heating 1610 1653 103 1689 105Total 4466 2755 62 2452 55

Reykjavik


Stockholm


Copenhagen


Paris


Madrid



0

1000

2000

3000

4000

5000

6000

Supp

lied en

ergy [kWh]

Lighting Cooling Heating

Figure 3.8: Total supplied energy, summed for all simulated directions (south, eastand west), for each location and shading type.

Figure 3.8 shows the results displayed in Table 3.1 graphically for a better viewof the composition of utilised energy. Figure 3.9 on page 35 then displays the re-duction in total supplied energy between the different cases. The black bars on thatgraph represent the reduction in total supplied energy when shading was added toa window without shading. The two shades of grey represent the introduction ofa dynamic window. The light grey when a window without shading was replacedwith a dynamic window and the darker grey when a window with external shadingwas replaced with a dynamic window. On that graph we can see that the dynamicwindow decreased the total energy consumption in Reykjavik, Stockholm, Copen-hagen, Paris and Madrid about 20-30% from the cases with external shading, butabout 10% in Kiruna.

3.3. THERMAL COMFORT 35

0

10

20

30

40

50

60

70

80Total sup

plied en

ergy re

duction [%

] A window without shading is replaced with a window with external shadingA window without shading is replaced with a dynamic windowA window with external shading is replaced with a dynamic window

Figure 3.9: Reduction of the total supplied energy when different changes wereintroduced 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 ISO7730:2005 (previously discussed in section 1.4.4) and registered for every half hourof the simulation. To get a good overview of the thermal comfort for the wholeannual simulation, the PPD index will be presented on duration diagrams for eachlocation. The duration diagrams only cover the occupied periods as the comfortindexes 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 thesame trend can be detected on all of them. The cases without shading had muchlonger duration of higher PPD than the cases with external shading. The dynamicwindow then further improved the thermal comfort for all cases.

The temperature setpoints in the model controlled the air temperature of thezone. During summer the operative temperature is generally higher than the airtemperature and therefore the operative temperature can exceed the cooling set-points. The reverse can occur in winter and the operative temperature can reachbelow the heating setpoint. This can cause increased occupant dissatisfaction. ENISO 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 mightcause unreasonable amount of HVAC energy to achieve. PPD < 10 is within Cate-gory B and PPD < 15 is in category C. This should provide a base for evaluatingthe following diagrams. It should be noted that, in reality, the rate of dissatisfac-tion might in some cases be considered to be unacceptable, but for this study thedifference between the cases is of more interest than the levels reached.


0 500 1000 1500 2000 25005

15

25

35

Time duration [h]

PP

D index [%

]

Kiruna, Dynamic, SouthKiruna, External, SouthKiruna, Without, SouthKiruna, Dynamic, EastKiruna, External, EastKiruna, Without, EastKiruna, Dynamic, WestKiruna, External, WestKiruna, Without, West

Figure 3.10: PPD index duration diagram for Kiruna.

0 500 1000 1500 2000 25005

15

25

35

Time duration [h]

PP

D index [%

]

Reykjavik, Dynamic, SouthReykjavik, External, SouthReykjavik, Without, SouthReykjavik, Dynamic, EastReykjavik, External, EastReykjavik, Without, EastReykjavik, Dynamic, WestReykjavik, External, WestReykjavik, Without, West

Figure 3.11: PPD index duration diagram for Reykjavik.

3.3. THERMAL COMFORT 37

0 500 1000 1500 2000 25005

15

25

35

Time duration [h]

PP

D index [%

]

Stockholm, Dynamic, SouthStockholm, External, SouthStockholm, Without, SouthStockholm, Dynamic, EastStockholm, External, EastStockholm, Without, EastStockholm, Dynamic, WestStockholm, External, WestStockholm, Without, West

Figure 3.12: PPD index duration diagram for Stockholm.

0 500 1000 1500 2000 25005

15

25

35

Time duration [h]

PP

D index [%

]

Copenhagen, Dynamic, SouthCopenhagen, External, SouthCopenhagen, Without, SouthCopenhagen, Dynamic, EastCopenhagen, External, EastCopenhagen, Without, EastCopenhagen, Dynamic, WestCopenhagen, External, WestCopenhagen, Without, West

Figure 3.13: PPD index duration diagram for Copenhagen.


0 500 1000 1500 2000 25005

15

25

35

Time duration [h]

PP

D index [%

]

Paris, Dynamic, SouthParis, External, SouthParis, Without, SouthParis, Dynamic, EastParis, External, EastParis, Without, EastParis, Dynamic, WestParis, External, WestParis, Without, West

Figure 3.14: PPD index duration diagram for Paris.

0 500 1000 1500 2000 25005

15

25

35

Time duration [h]

PP

D index [%

]

Madrid, Dynamic, SouthMadrid, External, SouthMadrid, Without, SouthMadrid, Dynamic, EastMadrid, External, EastMadrid, Without, EastMadrid, Dynamic, WestMadrid, External, WestMadrid, Without, West

Figure 3.15: PPD index duration diagram for Madrid.

3.4. TINTING/BLEACHING CYCLES 39

3.4 Tinting/Bleaching CyclesThe shading signal was registered for each simulated case for every half hour. Wehave already seen the duration of different shading levels in section 3.1 but thefrequency of change in shading state of the dynamic windows is also of importanceas it can effect the lifetime of some products.

The tinting/bleaching cycles were calculated from the shading signals with asimple Matlab code that may be found in appendix B.1. The code calculatesthe accumulated positive changes in the shading signal. It therefore does not onlycalculate full shading cycles as one, but also for example two half cycles equal onecycle. The resulting annual cycles for each location ad direction are displayed inTable 3.2. Table 3.3 then displays the expected number of cycles for 25 years as areference for a possible expected lifetime.

Table 3.2: Shading cycles calculated from the shading signal [Cycles/year].

Kiruna Reykjavik Stockholm Copenhagen Paris MadridSouth 357 301 461 443 386 427East 365 264 410 434 377 445West 266 242 352 315 312 365

Table 3.3: Shading cycles calculated from the shading signal. Estimation for 25years [Cycles/25 years].

Kiruna Reykjavik Stockholm Copenhagen Paris MadridSouth 8921 7536 11536 11082 9651 10663East 9128 6604 10250 10850 9416 11129West 6645 6044 8797 7882 7796 9125

The 25 year shading cycles ranged from around 6000 for the west facing windowin Reykjavik to almost twice that value, 11500 for the south facing window inStockholm. Apart from the local climate, the shading strategy effected the numberof shading cycles. During cooling periods, the shading reached almost two wholecycles per day as may be seen in Figure 2.4 on page 23. The first cycle when thewindow is maintaining the illuminance setpoint over the occupied period and thesecond when the shading level jumps to 100% when occupancy ends and down to0% after sunset.


3.5 Sensitivity Analysis of SHGCThe decision was made to use glazing parameters for the dynamic window deter-mined 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 windowwithout shading and the unshaded state of the window with external shading. Astables 2.6 and 2.7 show, there is a slight difference between parameters calculatedaccording to NFRC on one hand an CEN on the other. In particular, the SHGC inthe shaded state of the dynamic window is 25% lower according to NFRC than ac-cording to CEN. This will have an impact on the result and this chapter will displaythe difference for the differently calculated parameters. This sensitivity analysis canalso provide an indication of how the results change if dynamic windows continueto 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 suppliedenergy for Madrid. It repeats the result of Table 3.1 for the cases without shadingand with a dynamic window with parameters according to NFRC and adds theresult for a dynamic window with parameters according to CEN for comparison.As before the supplied energy is summed for all simulated directions. Heating isnot 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 thecooling energy of the case without shading but the dynamic window with the CENparameters utilised 41% of the cooling energy of the same case. The equivalentnumber for the case with external shading in Madrid from Table 3.1 was 47% forcomparison. If the two dynamic window cases for Madrid, NFRC and CEN, arecompared directly, the NFRC case utilised 19% less cooling energy than the CENcase. This shows that the difference in parameters retrieved by the two calculationmethods 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 durationdiagram respectively for the NFRC and CEN cases in all simulated directions. Nosignificant changes are evident but a slight shift occurs in lower levels of shadingwhere the NFRC cases require a little less shading. The same applies for the PPDduration diagram. A shift is evident, especially for the lower levels of PPD wherethe NFRC is providing better thermal comfort by a small margin.

3.5. SENSITIVITY ANALYSIS OF SHGC 41

Table 3.4: Comparison of supplied energy for the dynamic window results inMadrid 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 Dynamic-NFRC Dynamic-CEN(100%) [kWh] [kWh] [%] [kWh] [%]

Lighting 133 143 108 143 108

Cooling 5841 1922 33 2372 41Total 5974 2065 35 2515 42

0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

Time duration [h]

Shadin

g s

ignal [ ]

Madrid, Dynamic, South, NFRCMadrid, Dynamic, South, CENMadrid, Dynamic, East, NFRCMadrid, Dynamic, East, CENMadrid, Dynamic, West, NFRCMadrid, Dynamic, West, CEN

Figure 3.16: Shading duration diagram for Madrid during occupancy. Comparisonfor window parameters calculated with CEN and NFRC methods and

environmental conditions.


0 500 1000 1500 2000 25005

10

15

20

25

Time duration [h]

PP

D index [%

]

Madrid, Dynamic, South, NFRCMadrid, Dynamic, South, CENMadrid, Dynamic, East, NFRCMadrid, Dynamic, East, CENMadrid, Dynamic, West, NFRCMadrid, Dynamic, West, CEN

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 LimitationsBuilding energy simulation programs are becoming a very powerful tool to makepredictions of energy consumption of buildings and occupant comfort. In somecountries building energy simulations are required before a building permit isissued for larger buildings to show the design meets regulations. Results of buildingenergy simulations are also used for classification of buildings in different certifica-tion systems.

An accurate building simulation requires experience and effort to achieve buteven if all input parameters correspond to a case in reality, the software still usesmathematical models as simplification of reality. The mathematical models mightprovide a good representation of reality but, as all models, they are incorrect. Build-ing energy simulation programs are benchmark tested for specific cases to verify thattheir accuracy is within restrictions. IDA ICE has been benchmark tested accordingto relevant standards.

The main limitation of this research is that it was bound to the use of mathe-matical models. The simulated cases will have errors but the important thing is thatthey will all have the same basic errors. This research was set out to be comparativeso if the change between different cases was modelled accurately, the difference inresult should have given a good indication of the effect of the change.

The choices made in the modelling process will bring limitations that effectthe scope of the study. These choices need to be considered when making validdeductive inferences from this research. The most influential choices made duringthis 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 windowtypes. The results may not apply for buildings with low WWR as the solar heatgain from smaller windows is a smaller proportion of the total cooling load of thosebuildings. No adjoining buildings or other objects are assumed to shade the façade somaximum insolation is assumed, calculated from the IWEC2 weather files. Windowsshaded or partly shaded from direct solar radiation by other buildings or externalobjects will not show as much decrease in solar heat gain by operable externalshading blinds or dynamic windows as the model in this study.

The shading strategy produces the energy savings from shading devices. Itchooses the applicable shading signal for each time step and decides the windowparameters. To achieve the same savings in reality, the same shading strategymust be followed. Some factors in reality may require a deviation from the designstrategy. Occupants might override the strategy signal and request either more orless shading effecting the energy performance of the building. Occupant overrideis difficult to predict but a good shading strategy can minimise the frequency ofoverrides. A possibility of glare estimation at workplane would assist in evaluatingthe quality of the shading control strategy but that feature is not available in IDAICE (version 4.6.1). High winds can also require a change in the strategy for theexternal shading devices. Some external shading devices cannot be operated in highwinds 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 COPof 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 temperaturesetpoints. This estimation was made for simplification and variables for a ventila-tion unit were eliminated. Availability of sustainable and efficient energy sourcesvaries between the different locations and this research does not consider where thesupplied energy comes from or how efficient the HVAC units are.

Electrochromic windows consume low voltage electricity when changing shadingstates. The energy consumption of the dynamic window itself is not included inthe results. Its energy consumption can however be estimated for a specific windowproduct by using the shading cycle results. The external shading blind can also bedriven by electricity. The same applies for the external blind as for the dynamicwindow, the energy consumption is not calculated but can be estimated for a specificproduct from the shading cycle results.

4.2. CONCLUSIONS 45

4.2 Conclusions

This research shows that, during occupancy and over a year, the dynamic windowis clearly active for a much longer time than the external blind for the simulatedcases. However, the dynamic window is rarely in its fully shaded state. This in-creased duration of shading makes the dynamic window able to reduce the coolingenergy consumption more than for the external shading and provide slightly betteroccupant comfort. This is possible even though the external shading is able to rejectmore heat when the fully shaded states are compared.

The extent of the measured energy savings for the dynamic window compared tothe external blind is ranging from 10% to 30%, depending on location. If Kiruna isexcluded, this range is from 20% to 30%. This means that the proportional decreasein energy consumption of using a dynamic window instead of an external shadingis very similar for these locations. However, the scale of the total energy reductionin kWh variates with location due to the variation in total cooling requirement.Therefore the results suggest that dynamic windows will save most kWh of energyin warm climates. Numbers for total energy consumption should not be extractedfrom 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 forsizing of HVAC units but still some conclusions can be drawn from the resultsin that regard. The results for Reykjavik for example show that annual coolingrequirement can almost be eliminated with the use of dynamic windows. Smallercooling equipment may be used and that reduces the installation and maintainingcost.

Madrid was chosen as one of the locations as it has similar latitude to Denverwhere the pilot project of Lee et al. (2014) was conducted. The initial goal was tocompare the results for Madrid and the pilot project but that comparison turns outto 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 unshadedwindow. What Lee et al. (2014) are missing in their study is a comparison of thedynamic windows to an external blind. External window blinds can be a goodoption for reducing unwanted solar heat gain.

This research shows that dynamic windows have a potential to help reachingthe 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 con-tinue 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 workA model component for a dynamic window is not available by default in the currentversion of IDA ICE. To create a shading strategy in order to interpolate the shadingsignal to simulate dynamic window is time consuming and requires deep knowledgeof the software. If this feature is made available in a simple interface model com-ponent it would make it easier for users to introduce a dynamic window to theirdesign for checking its impact on the result. If this model component is created byspecialists it could also reduce calculation time and increase the reliability of themodel as probability of computational errors would be minimised.

The Simple Window Model in IDA ICE was used in this research. The input pa-rameters for that model are fixed, calculated according to standard environmentalconditions (see section 1.4.2). The more correct Advanced Window Model in IDAICE calculates these parameters dynamically for the occurring environmental con-ditions in the simulation with a model of the IGU. If an advanced window model fora dynamic IGU would be made available, more accurate results might be obtained.

As mentioned earlier, probability of glare at occupant workplane can not beestimated with IDA ICE (version 4.6.1). IDA ICE is under constant developmentand updates are available at a regular basis. If a beam tracking algorithm will beintroduced to IDA ICE for direct solar radiation prediction within a zone, solarglare estimations could be possible. It would be interesting to add a control to theshading strategy based on glare probability at the occupant workplane. That wouldgive a more accurate shading strategy as it would increase the visual comfort of theoccupant. Increased visual comfort would reduce the need for occupant override forthe shading and the strategy would better correspond to reality.

Bibliography

Baetens, R., Jelle, B. r. P., Gustavsen, A., 2010. Properties, requirements and possi-bilities of smart windows for dynamic daylight and solar energy control in build-ings: 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 [16March 2015].

Glass for Europe, [n.d.]. GEPVP Code of practice. Available from:http://www.glassforeurope.com/images/cont/194_929_file.pdf [9 May2015].

Hanam, B., Jaugelis, A., Finch, G., 2014. Energy Performance of Windows: Nav-igating North American and European window standards. In: 14th CanadianConference on Building Science and Technology. Toronto.

Hegger, M., Fuchs, M., Stark, T., Zeumer, M., 2008. Energy Manual - SustainableArchitecture. 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 Electrochromicand Thermochromic Windows in the Denver Federal Center , Building 41. Tech.rep., General Services Administration.

Lundström, L., 2012. Weather data for building simulation - New actual weatherfiles for North Europe combining observed weather and modeled solar radiation.Master’s thesis.

Mäkitalo, J., 2013. Simulating control strategies of electrochromic windows. Masterthesis, Uppsala Universitet.

Nilson, P. E., 2007. Achieving the Desired Indoor Climate. Studentlitteratur.

47

http://ec.europa.eu/energy/en/topics/energy-efficiency/buildings

http://www.glassforeurope.com/images/cont/194_929_file.pdf

48 BIBLIOGRAPHY

Pidwirny, M., 2006. Atmospheric Effects on Incoming Solar Radiation. In: Funda-mentals 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 andblinds. Lighting Research and Technology 35 (3), 243–260.

Smith, G. B., Granqvist, C. G., 2011. Green Nanotechnology - Solutions for Sus-tainability 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 April2015].

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. Windowsfor 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 NationalLaboratory.

http://www.physicalgeography.net/fundamentals/7f.html

http://opentextbc.ca/introductiontopsychology/chapter/4-2-seeing/#Figure5.6

http://opentextbc.ca/introductiontopsychology/chapter/4-2-seeing/#Figure5.6

http://www.powerfromthesun.net/Book/chapter02/chapter02.html

http://www.commercialwindows.org/technologies.php

http://apps1.eere.energy.gov/buildings/tools_directory/software.cfm/ID=369/pagename=alpha_list

http://apps1.eere.energy.gov/buildings/tools_directory/software.cfm/ID=369/pagename=alpha_list

Appendix A

Full Results

A.1 Shading duration

Table A.1: Full result of the shading duration. Total number of occupied hourswas 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 Shading duration Shading durationon vacancy [h] on occupancy [h]

on [h] % of vacancy on [h] % of occup.KirunaDynamicSouth 1465 25% 1323 46%KirunaExternalSouth 1582 27% 349 12%KirunaDynamicEast 1597 27% 1234 43%KirunaExternalEast 1657 28% 321 11%KirunaDynamicWest 1549 26% 804 28%KirunaExternalWest 1620 28% 23 1%ReykjavikDynamicSouth 1338 23% 913 32%ReykjavikExternalSouth 1446 25% 178 6%ReykjavikDynamicEast 1297 22% 774 27%ReykjavikExternalEast 1335 23% 66 2%ReykjavikDynamicWest 1409 24% 622 22%ReykjavikExternalWest 1499 25% 14 0%StockholmDynamicSouth 1511 26% 1689 59%StockholmExternalSouth 1559 26% 574 20%StockholmDynamicEast 1469 25% 1234 43%StockholmExternalEast 1499 25% 246 9%StockholmDynamicWest 1545 26% 1265 44%StockholmExternalWest 1604 27% 235 8%CopenhagenDynamicSouth 1453 25% 1462 51%CopenhagenExternalSouth 1521 26% 415 14%

Continued on next page

49

50 APPENDIX A. FULL RESULTS

Table A.1 – Continued from previous page

Simulated case Shading duration Shading durationon vacancy [h] on occupancy [h]

on [h] % of vacancy on [h] % of occup.CopenhagenDynamicEast 1422 24% 1276 44%CopenhagenExternalEast 1442 24% 230 8%CopenhagenDynamicWest 1408 24% 924 32%CopenhagenExternalWest 1452 25% 31 1%ParisDynamicSouth 1579 27% 1534 53%ParisExternalSouth 1617 27% 351 12%ParisDynamicEast 1503 26% 1371 48%ParisExternalEast 1523 26% 146 5%ParisDynamicWest 1602 27% 1252 44%ParisExternalWest 1636 28% 139 5%MadridDynamicSouth 1910 32% 2080 72%MadridExternalSouth 1922 33% 650 23%MadridDynamicEast 1769 30% 1932 67%MadridExternalEast 1809 31% 338 12%MadridDynamicWest 1960 33% 1677 58%MadridExternalWest 2040 35% 310 11%

A.2. SUPPLIED ENERGY 51

A.2 Supplied energy

52 APPENDIX A. FULL RESULTSTa

bleA.2:Fu

llresult

ofsupp

lieden

ergy

foralls

imulated

cases.

Simulation

Equ

ipment

Ligh

ting

Coo

ling

Heating

Grand

total

Stockh

olm-D

ynam

ic-Sou

th-N

FRC

430

5234

744

873

Stockh

olm-D

ynam

ic-Sou

th-C

EN43

052

473

2798

2Stockh

olm-E

xterna

l-Sou

th-N

FRC

430

5351

542

1040

Stockh

olm-W

ithou

t-So

uth-NFR

C42

947

1464

1519

55Stockh

olm-D

ynam

ic-E

ast-NFR

C42

973

276

188

966

Stockh

olm-D

ynam

ic-E

ast-CEN

429

7436

115

710

21Stockh

olm-E

xterna

l-East-NFR

C42

973

398

189

1089

Stockh

olm-W

ithou

t-Ea

st-N

FRC

428

6999

318

916

79Stockh

olm-D

ynam

ic-W

est-NFR

C43

071

317

161

979

Stockh

olm-D

ynam

ic-W

est-CEN

430

7141

813

110

50Stockh

olm-E

xterna

l-West-NFR

C43

070

440

150

1090

Stockh

olm-W

ithou

t-West-NFR

C42

966

1254

147

1896

Reykjavik-D

ynam

ic-Sou

th-N

FRC

430

106

4614

572

7Reykjavik-E

xterna

l-Sou

th-N

FRC

430

107

176

138

851

Reykjavik-W

ithou

t-So

uth-NFR

C42

910

346

312

511

20Reykjavik-D

ynam

ic-E

ast-NFR

C43

011

819

248

815

Reykjavik-E

xterna

l-East-NFR

C42

911

815

524

694

8Reykjavik-W

ithou

t-Ea

st-N

FRC

429

115

348

245

1137

Reykjavik-D

ynam

ic-W

est-NFR

C43

012

738

224

819

Reykjavik-E

xterna

l-West-NFR

C42

912

713

122

090

7Reykjavik-W

ithou

t-West-NFR

C42

912

539

221

911

65Kiru

na-D

ynam

ic-Sou

th-N

FRC

429

8816

246

411

43Kiru

na-D

ynam

ic-Sou

th-N

FRC-Small

430

116

313

7993

8Kiru

na-E

xterna

l-Sou

th-N

FRC-Small

430

140

351

7699

7Con

tinuedon

next

page

A.2. SUPPLIED ENERGY 53Ta

bleA.2

–Con

tinuedfro

mprevious

page

Simulation

Equ

ipment

Ligh

ting

Coo

ling

Heating

Grand

total

Kiru

na-W

ithou

t-So

uth-NFR

C-Small

428

113

654

7312

68Kiru

na-E

xterna

l-Sou

th-N

FRC

429

8830

744

412

68Kiru

na-W

ithou

t-So

uth-NFR

C42

984

889

414

1816

Kiru

na-D

ynam

ic-E

ast-NFR

C42

996

175

564

1264

Kiru

na-D

ynam

ic-E

ast-NFR

C-Small

430

127

296

9995

2Kiru

na-E

xterna

l-East-NFR

C-Small

429

147

325

9910

00Kiru

na-W

ithou

t-Ea

st-N

FRC-Small

428

122

688

9713

35Kiru

na-E

xterna

l-East-NFR

C42

895

291

553

1367

Kiru

na-W

ithou

t-Ea

st-N

FRC

428

9110

3654

120

96Kiru

na-D

ynam

ic-W

est-NFR

C42

911

013

266

113

32Kiru

na-D

ynam

ic-W

est-NFR

C-Small

430

160

261

116

967

Kiru

na-E

xterna

l-West-NFR

C-Small

430

161

263

116

970

Kiru

na-W

ithou

t-West-NFR

C-Small

428

157

501

116

1202

Kiru

na-E

xterna

l-West-NFR

C42

910

921

265

614

06Kiru

na-W

ithou

t-West-NFR

C42

810

764

965

518

39Cop

enha

gen-Dyn

amic-Sou

th-N

FRC

430

6431

014

818

Cop

enha

gen-Ex

ternal-Sou

th-N

FRC

430

6448

312

989

Cop

enha

gen-W

ithou

t-So

uth-NFR

C42

960

1209

317

01Cop

enha

gen-Dyn

amic-E

ast-NFR

C43

072

276

7184

9Cop

enha

gen-Ex

ternal-E

ast-NFR

C42

972

433

6910

03Cop

enha

gen-W

ithou

t-Ea

st-N

FRC

428

6794

564

1504

Cop

enha

gen-Dyn

amic-W

est-NFR

C43

195

236

127

889

Cop

enha

gen-Ex

ternal-W

est-NFR

C43

095

368

126

1019

Cop

enha

gen-W

ithou

t-West-NFR

C42

993

709

125

1356

Paris

-Dyn

amic-Sou

th-N

FRC

430

6439

711

902

Con

tinuedon

next

page

54 APPENDIX A. FULL RESULTSTa

bleA.2

–Con

tinuedfro

mprevious

page

Simulation

Equ

ipment

Ligh

ting

Coo

ling

Heating

Grand

total

Paris

-Externa

l-Sou

th-N

FRC

430

6661

79

1122

Paris

-With

out-So

uth-NFR

C42

961

1298

217

90Pa

ris-D

ynam

ic-E

ast-NFR

C42

964

327

4086

0Pa

ris-E

xterna

l-East-NFR

C42

965

531

3910

64Pa

ris-W

ithou

t-Ea

st-N

FRC

428

6192

535

1449

Paris

-Dyn

amic-W

est-NFR

C43

072

380

2991

1Pa

ris-E

xterna

l-West-NFR

C43

072

542

2510

69Pa

ris-W

ithou

t-West-NFR

C42

970

1198

1917

16Mad

rid-D

ynam

ic-Sou

th-N

FRC

430

4967

30

1152

Mad

rid-D

ynam

ic-Sou

th-C

EN43

049

857

013

36Mad

rid-E

xterna

l-Sou

th-N

FRC

430

5197

70

1458

Mad

rid-W

ithou

t-So

uth-NFR

C42

945

2193

026

67Mad

rid-D

ynam

ic-E

ast-NFR

C42

936

578

210

45Mad

rid-D

ynam

ic-E

ast-CEN

429

3669

60

1161

Mad

rid-E

xterna

l-East-NFR

C42

838

852

113

19Mad

rid-W

ithou

t-Ea

st-N

FRC

428

3315

710

2032

Mad

rid-D

ynam

ic-W

est-NFR

C42

958

671

011

58Mad

rid-D

ynam

ic-W

est-CEN

429

5881

90

1306

Mad

rid-E

xterna

l-West-NFR

C43

057

829

013

16Mad

rid-W

ithou

t-West-NFR

C42

955

2077

025

61

Appendix B

Matlab Codes

B.1 Code for Shading Cycles

1 function [Cycles,TimeStep] = IDA_ShadingCycles( Time,ShadingSignal )2 %IDA_SHADINGCYCLES calculates the change in the ShadingSignal and3 % sums up the positive changes. That value denotes the total4 % theoretical full cycles the shading has undergone.5 %6 % INPUT7 % Time is a vector for the time in hours.8 % ShadinSignal is a vector for the shading signal output.9 % OUTUPTS

10 % Cycles is a number for the total number of on/off cycles the11 % shading has undergone.12 % TimeStep shows the timesteps of the data in hours.13 %14 % Hannes Ellert Reynisson15 % KTH, Stockholm16 % March 20151718 %% Time step for the data19 % Assumes equal time steps20 TimeStep=Time(2)−Time(1);2122 %% Shading signal change and cycles23 ShadingSignalChange=ShadingSignal(2:end)−ShadingSignal(1:end−1);24 ShadingSignalOnlyPositive=ShadingSignalChange.*(ShadingSignalChange>0);25 Cycles=sum(ShadingSignalOnlyPositive);2627 end

55

www.kth.se

Energy Performance of Dynamic Windows in Different Climates825465/FULLTEXT01.pdf · 2015. 6....

Documents

Transcript of Energy Performance of Dynamic Windows in Different Climates825465/FULLTEXT01.pdf · 2015. 6....