Energy Performance of Dynamic Windows in Different Climates

Transcription

1 Energy Performance of Dynamic Windows in Different Climates HANNES E. REYNISSON Master s Degree Project Royal Institute of Technology SE Stockholm SCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT June 2015, Sweden School of Architecture and the Built Environment Division of Building Technology Supervisor: Kjartan Guðmundsson Examiner: Kjartan Guðmundsson TRITA-BYTE Master Thesis 437, 2015

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3 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 Hannes Ellert Reynisson Supervisor: Kjartan Guðmundsson TRITA-BYTE Master Thesis 437, 2015 ISSN ISRN KTH/BYTE/EX-437-SE

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5 iii Abstract The European Union (EU) has expressed determination of reducing its energy consumption and the EU s 2010 Energy Performance of Buildings Directive states that all new buildings must be nearly zero energy by the end of the year Dynamic or smart windows have been shown to be able to reduce HVAC energy consumption, lighting energy and peek cooling loads in hot climates in the US but it is difficult to find any work concerned with colder climates. This study is intended to capture the performance of dynamic windows in a variety of European climates to explore potential contributions to reaching the EU s energy goals. The building energy simulations of this study have been conducted in IDA ICE for an office section with a large window. Three model variants are compared: without a window shading, with an external window blind and with a dynamic window. This comparison is repeated for six different locations; Kiruna, Reykjavik, Stockholm, Copenhagen, Paris and Madrid. The results of this study show that the dynamic window can reduce the total consumed energy for lighting, heating and cooling in the range of 10%-30% more than the external blind, depending on location. The reduction is 50%-75% when compared to the unshaded window. This level of performance can move Europe a step closer to zero energy buildings. Keywords: IDA ICE, Building Energy Simulation, Electrochromic Window, Smart Window, Window Shading.

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7 Essentially, all models are wrong, but some are useful. GEORGE E. P. BOX ( )

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9 vii Acknowledgements First of all, I would like to express my gratitude towards my supervisor, Kjartan Guðmundsson. He was always available for discussions and he helped me see things in a wider perspective. I want to send my regards to EQUA Simulation AB for providing me with a licence for IDA ICE. Without this powerful and flexible tool I would not have been able to conduct the research in the way I wanted and compute the outputs I needed. I furthermore want to thank Bengt Hellström at Equa for guidelines on standards for various fenestration parameter calculations. I would also like to thank D. Charlie Curcija, Ph.D. at Lawrence Berkeley National Laboratory for assisting me with the computer software Window 7 and for giving me comments on the window parameter results from that software.

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11 ix Abbreviations AHU air handling unit. ASHRAE American Society of Heating, Refrigeration, and Air-Conditioning Engineers. CEN Comité Européen de Normalisation. COP coefficient of performance. EU European Union. GSA U.S. General Services Administration. IGU insulated glass unit. IWEC International Weather for Energy Calculations. LBNL Lawrence Berkeley National Laboratory. NFRC National Fenestration Rating Council. PMV predicted mean vote. PPD predicted percentage dissatisfied. SPD suspended-particle devices. TMM typical meteorological months. TMY typical meteorological year. USA United States of America.

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13 xi Nomenclature λ Wavelength in meters [m]. σ SB The Stefan-Boltzmann constant ( )[W/m 2 /K 4 ]. b W The Wien s displacement constant (2, )[m K]. c Speed of light in vacuum [m/s]. F Planck spectral radiant exitance [(W/m 2 )/m or W/m 3 ]. f cl Clothing surface area factor [ ]. g Center of glass solar heat gain factor (see SHGC). h The Planck s Constant [J s]. h c Convective heat transfer coefficient [W/(m 2 K)]. M Metabolic rate [W/m 2 ]. m g Multiplier for the fully clear solar heat gain coefficient to represent the fully shaded state. P Total power per square meter emitted by a black body at temperature T [W/m 2 ]. p a Water Vapour partial pressure [Pa]. S signal Shading signal for the window model. T Temperature in Kelvin [K]. t a Air temperature [ o C]. t cl Clothing surface temperature [ o C]. t r Mean radiant temperature [ o C]. I cl Clothing insulation [m 2 K/W]. v a Relative air velocity [m/s]. k B The Boltzmann s constant [J/K]. W Effective mechanical power [W/m 2 ].

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15 xiii Glossary AU The mean distance from the Sun to the Earth is 1 AU (1, m). HVAC Heating, ventilating and air conditioning unit. illuminance The luminous flux incident 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 light source. The ratio of the luminous flux emitted to the electrical power used ([lm/w]). luminous flux Output of light source in all directions [lm]. operative temperature The average of the mean radiant and ambient air temperatures, weighted by their respective heat transfer coefficients. SHGC Solar heat gain coefficient. The ratio of solar radiation energy directly and indirectly transmitted through glazing assembly of the total incident solar radiation energy. solar irradiation The total amount of solar radiation energy received on a given surface area during a given period [W/m 2 ]. T sol Shortwave radiation transmission factor through a glazing unit. U-value Heat transfer coefficient. It denotes the rate of heat loss through a component. VLT or VT. Visible light transmittance. The ratio of the visible light directly transmitted through a glazing assembly of the incident visible light. WWR Window to wall ratio. The ratio of window (glazed) area to the total wall area.

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17 xv Contents Frontmatter Abstract Acknowledgements Abbreviations Nomenclature Glossary Contents i iii vii ix xi xiii xv 1 Introduction Background Research Topics of Interest Related Work Dynamic Windows in Application Simulation of Dynamic Windows in IDA ICE Theory Solar Radiation Glazing Properties Dynamic Glazing Indoor Climate Method The Model Base Building Geometry Structural Elements and Boundary Conditions Occupancy, Internal Loads and Lighting Room Heating and Cooling Units Zone Lighting and Thermal Setpoints Model Variations Window Without Shading Window With External Blind Dynamic Window Dynamic Window (CEN Conditions) for Sensitivity Analysis Shading Controls Dynamic Window Shading Controls External Blind Shading Controls Shading Signal Example Weather Files and Locations Results Duration of Shading Levels Energy Consumption

18 xvi 3.3 Thermal Comfort Tinting/Bleaching Cycles Sensitivity Analysis of SHGC Discussion Scope and Limitations Conclusions Future work Bibliography 47 Appendix A Full Results 49 A.1 Shading duration A.2 Supplied energy Appendix B Matlab Codes 55 B.1 Code for Shading Cycles

19 Chapter 1 Introduction 1.1 Background According to the European Commission ([n.d.]), buildings are responsible for 40% of the total energy consumption in the European Union (EU) and 36% of the total CO 2 emissions. Required heating energy for new EU buildings is around 12-25% of what is required for older buildings and around 35% of the current building stock in the EU is over 50 years old. Large energy improvements can be achieved by upgrading these old buildings to today s performance standards but even then, buildings will continue to be large energy consumers. This will further push legislators to tighten energy demands and force building constructors, owners and operators to continue to develop with regard to energy efficiency. The EU s 2010 Energy Performance of Buildings Directive for example states that all new buildings must be nearly zero energy by the end of the year 2020 and public buildings by the end of Windows are used in buildings to achieve a certain level of natural light at internal spaces and to give the occupants a view to the outside. They generally have inferior thermal performance in comparison to the surrounding wall and their maximum size is limited by the potential solar radiation heat gain and thermal conduction through the window. The solar radiation heat gain can however be used to the advantage of heating buildings in colder climates when needed and to some extent counterbalance the poor thermal conduction properties of the window. Window glazing composition, coatings and shading can be optimised to obtain a desired balance between thermal gains and losses. In recent years windows with a dynamic range of shading properties have been becoming commercially available for the building sector. They are commonly referred to as smart or switchable but will herein be called dynamic. Dynamic windows provide a control of heat gains and daylight and are believed to have the potential to become net energy producers, thus requiring less building energy to counteract heat gains and losses than through an insulated wall. (Lee et al., 2014) These type of windows have been shown to be able to reduce HVAC energy consumption (e.g. Lee et al. (2014)), lighting energy compared to well controlled 1

20 2 CHAPTER 1. INTRODUCTION blinds and peak cooling loads. These studies have mostly been made for hot climates in the USA while research is missing for colder climates, for example Nordic climates. (Baetens et al., 2010) To determine whether dynamic windows can assist in reaching the EU s building energy goals, more studies for the variable climate conditions within Europe need to be carried out. 1.2 Research Topics of Interest One of the advantages of dynamic windows when compared to mechanically shaded windows is that the shading level is adjustable, that is the shading does not have to be in the two extreme levels, fully shaded (on) or clear (off), but it can have an intermediate value. That way the dynamic windows can maintain certain levels of natural light indoors and provide outside view, even when in a shaded state. In light of that, it is important to evaluate in what states the dynamic windows will be during occupancy over the year when comparing to a window with an operable external blind. Another reason for evaluating the states of the window is that during manufacturing of electrochromic windows, the shading levels are set to predefined steps. These steps need to correspond to the most common states of shading that provide the best performance for that particular climate. This leads to the following research question. For one year, what is the duration of different shading levels for a dynamic window during occupancy compared to the duration of on/off states for an operable external blind? The very fact that dynamic windows provide an intermediate level of shading allows a shading control strategy to increase the number of (partly) shaded hours during occupancy while maintaining acceptable levels natural indoor lighting and outside view. The dynamic windows might consequently be able to reject more unwanted solar heat for a whole year than the operable external blind even though the external blind might be able to reject more heat when both are compared in the fully shaded state. What is the annual heating and cooling energy consumption of a building with a dynamic window compared to a static window with an operable external blind? When comparing design options with regard to energy efficiency, the effects on the indoor climate and occupants need to be controlled or monitored. For the two options in the previous research question, is there a difference in the predicted occupant comfort? The expected lifetime of dynamic windows might be dominated by the frequency

21 1.3. RELATED WORK 3 of tinting/bleaching cycles (Baetens et al., 2010). When evaluating the option of installing dynamic windows, the number of tinting/bleaching cycles should thus be an important measurement for the climate condition and the window shading control strategy. For one year, what is the number of tinting/bleaching cycles for a dynamic window? 1.3 Related Work A considerable amount of literature has been published on the potential energy savings of dynamic windows. The most relevant methods and results will be discussed in this chapter, as well as their limitations and possible improvements Dynamic Windows in Application Lee et al. (2014) published a paper on a pilot project of the U.S. General Services Administration (GSA) Region 8 for application of electrochromic and thermochromic windows in a federal office building. The technical objectives of the projects were to characterise and understand how dynamic windows work, estimate HVAC energy consumption reduction, to understand the effects on occupant comfort, satisfaction and acceptance of the technology and finally to estimate the economical feasibility of the technology. The building chosen for the pilot project was building 41 in the Denver Federal Center, a low-rise office building in Denver, Colorado (latitude 38,75 o N). The existing single pane clear windows on the west facing (orientation 67 o west of south), second floor were replaced with thermochromic, electrochromic and low-e windows respectively in three defined zones from south to north. The Window to Wall ratio (WWR) of the building was 0,27. (Lee et al., 2014) For the first part of the study, weather and window conditions were monitored at site in order to characterise dynamic windows. Additionally for the thermochromic windows, thermal infra-red cameras monitored their condition for detailed evaluation of their switching patterns. The HVAC energy reduction was evaluated with a building energy simulation conducted using the EnergyPlus 1 software. The artificial lighting was not dimmable so the simulation does not account for potential energy variations for the lighting. Economical feasibility of the technology was evaluated from the simulated energy savings and the additional installation cost. (Lee et al., 2014) The monitored behaviour of the thermochromic windows shows that they switch based on both outdoor air temperature and the incident solar radiation (absorbed radiation). For example on a sunny winter day in Denver when the external temperature was 5-15 o C the windows were tinted for 4 hours in the afternoon. Since 1 EnergyPlus is available free of charge from the U.S. Department of Energy s website.

22 4 CHAPTER 1. INTRODUCTION office buildings with hight internal loads from lighting, occupancy, equipment often require cooling throughout winter, this switching pattern does therefore not necessarily contradict the goal of HVAC energy reduction in office buildings. On the negative side, the switching pattern can be inconsistent across the pane as the pane temperature might be variable due to edge thermal bridges or partial external shading for example. Energy savings achieved by the thermochromic windows tested in this project (type B-TC) showed to be the same as for static double-pane low-e windows (13% and 14% annual HVAC cooling electricity reduction respectively and 26% and 28% zone cooling energy reduction for example), compared to the originally installed, single pane, clear windows. Another type of windows (type C-TC) was simulated where the thermochromic film properties were combined with the low-e glazing. The annual result was 1% increase in zone heating energy, 48% decrease in zone cooling energy and 22% decrease in HVAC cooling electricity consumption compared to the original, single pane, clear windows. (Lee et al., 2014) The result of the electrochromic window energy simulation is very similar to the type C-TC thermochromic window result. Annual result shows 3% increase in zone heating energy, 45% decrease in zone cooling energy and 22% decrease in HVAC cooling electricity consumption compared to the original, single pane, clear windows. The most common write-in comment from the occupants was that the electrochromic window changed the occupant s perceptions of the outdoor weather patterns. No comments were made on the blue colour of the light through the electrochromic window. (Lee et al., 2014) This project by Lee et al. (2014) was conducted in a relatively warm climate. Mean minimum and maximum temperatures in Denver are around -6 o C and +8 o C in winter and +16 o C and +31 o C in summer. The project was limited to this one location and this particular building with customised HVAC units. It is therefore difficult to make inferences from this project of the performance of dynamic windows in other, different climates. The building energy performance for the different fenestration systems in this research was obtained from building energy simulations. Even for an as extensive, scientific renovation project as this, the difference in energy performance before and after is very difficult to measure in reality and computer simulations were believed to be the best option to evaluate the difference Simulation of Dynamic Windows in IDA ICE Mäkitalo (2013) explored the simulation of electrochromic windows in the IDA ICE software and constructed new control algorithms for more accurate simulation from the previously available window and shading controls. The shading controls that are currently available by default in IDA ICE are mainly intended to be used for shading devices that use an on/off input signal. The software allows for a customisation to create intermediate shading signals between 1 and 0, 1 for the window in its fully shaded state and 0 for the window in a fully clear state. For more information about the shading signal in IDA ICE, see section 2.3.

23 1.4. THEORY 5 The three custom shading control algorithms created by Mäkitalo (2013) will be introduced here as they provide a foundation for the combined shading strategy in this study that will be discussed in section 2.3. Schedule, façade and window This algorithm is designed to prevent excessive global solar radiation through the window. It uses direct and global radiation outside the window as controls for the shading signal while allowing for a manual schedule. The non-manual control is not active unless direct radiation hitting the façade is above 50 W/m 2. The shading signal is set to 0,5 if the global solar radiation is above 225 W/m 2 and to 1 (full shading) if the global solar radiation is above 450 W/m 2 on the façade. The setpoints of 50 W/m 2 direct radiation and 450 W/m 2 global radiation were obtained from a study by Reinhart and Voss (2003). Operative temp The internal operative temperature is used to control the shading signal in this algorithm. When a defined maximum temperature is reached, the shading signal is turned to 1 (shading on). Mäkitalo (2013) used 24,5 o C (0,5 o C below the cooling setpoint) as the defined operative temperature. Workplane The Workplane algorithm strives to maintain a fixed level of natural illumination at the chosen location of the occupant workplane by tuning the shading signal. This control method can provide the maximum energy savings possible as it can maintain the minimum amount of natural light needed by the occupants, thus maintaining as much natural light so artificial lighting is not needed but rejecting solar heat from the excess natural light that is not needed. The illumination setpoint for this control algorithm of 500 lx was obtained from SS-EN :2011 (Swedish Standards Institute, 2011) for a typical office building. 1.4 Theory Solar Radiation The solar radiation is composed of multiple frequencies with different energy intensities for each frequency. This is referred to as spectral properties of solar radiation (Smith and Granqvist, 2011). Various factors influence the spectral properties of solar radiation reaching the indoors of a building, e.g. sky cloud cover, solar radiation incident angle and glazing composition. When designing and evaluating a glazing unit it is essential to realise what the incident radiation s spectral properties are and how the transmission of solar energy can be controlled. This section will

24 6 CHAPTER 1. INTRODUCTION explain in details how the solar radiation spectrum is affected from the emittance of the sun until it reaches the indoors of a building. All objects that are above absolute zero in temperature emit thermal radiation. The ideal object to describe thermal radiation is the black body. A black body absorbs all incident electromagnetic radiation but emits, isotropically, as much energy as is theoretically possible for any body at all frequencies. Planck s law states the spectral radiant exitance of a black body as a function of temperature (T ) (Smith and Granqvist, 2011): 2πhc 2 F (λ, T ) = ( ) ]. (1.1) hc λ [exp 5 1 λk B T If the radiant exitance is integrated over all frequencies we will get the total power emitted by a black body at temperature T. This equation is known as the Stefan-Boltzmann equation: P (T ) = σ SB T 4. (1.2) Figure 1.1 shows the spectrum from Equation (1.1) graphically for black bodies at different temperatures. The figure shows that with increased temperature, the total emitted power (the area under the curve) will increase and the peak of the curve will slide to lower wavelengths. The spectrum peak for a black body at variable temperature T shifts according to Wien s displacement law: λ max (T ) = b W T. (1.3) The Sun s exitance spectrum is similar to a black body at temperature T = K (Smith and Granqvist, 2011). According to the Stefan-Boltzmann equation the total emitted power of that black body is P(6 274 K) = 89 MW/m 2 and according Wien s displacement law the peak of the spectrum is around λ max = 462 nm. That wavelength falls inside the visible spectrum and if we take a look at Figure 1.2 we see that the colour of that wavelength is light-blue. If, on the other hand, we look at an object at room temperature of T = 20 o C = 294 K the total emitted power is P(294 K) = 418 W/m 2 and the exitance spectrum peak for that object is λ max =9 856 nm according to Wien s displacement law (assuming black body radiation). That wavelength falls outside the visible spectrum (see Figure 1.2) but inside the infra-red range. In reality, the Sun is not a perfect black body and the total exitance power of the Sun has been measured to be 63,3 MW/m 2 at the Sun s surface. The radiation decreases with the distance squared as it spreads out spherically. The mean distance from the Sun to Earth is 1 AU = 1, m and when the radiation reaches the Earth s atmosphere, the total power has reduced down to W/m 2. (Stine and Geyer, 2001) Gases and particles in the Earth s atmosphere affect the solar radiation passing through it. The radiation can be affected by the three following processes in the

25 1.4. THEORY 7 Spectral Intensity (W/m 2 /µm) x K 1000 K 2000 K 3000 K 4000 K 5000 K 6000 K Wavelength [µm] Figure 1.1: Spectral exitance radiation data for perfect black bodies at different temperatures according to Planck s law. The peaks shift towards shorter wavelengths with increasing temperatures according to Wien s displacement law. The curve for K is close to the solar radiation surface exitance radiation spectrum. Figure 1.2: The electromagnetic wave spectrum. The visible range is highlighted with blue light at around 410 nm to the left, green at 520 nm, yellow at 600 nm and red at 710 nm. (Stangor, 2014) atmosphere (Pidwirny, 2006). Solar radiation that is not affected by these processes and reaches the Earth s surface is called direct solar radiation.

26 8 CHAPTER 1. INTRODUCTION Scattering Scattering is the process when gas molecules or particles randomly change the direction of the radiation on impact. This process does not affect the wavelength of the radiation but it can reduce the amount of radiation reaching the Earth s surface. The solar radiation that is affected by scattering and reaches the Earth s surface is called diffused solar radiation. Reflection When the direction of the radiation changes 180 o (back the same path) on impact with particles in the atmosphere the insolation is reduced by 100%. This process is called reflection and it mostly occurs in clouds when radiation hits particles of liquid and frozen water. Absorption Some gases and particles in the atmosphere have the ability to absorb incoming solar radiation. The radiation will then convert to thermal energy stored in the substance. This process will reduce the energy in the initial solar radiation but the substance will start to emit its own radiation. That emitted radiation is on the infra-red band according to Wien s law for the temperatures in the atmosphere. The radiation occurs in all directions so a part of the energy is lost back to space Glazing Properties When the solar radiation hits a glass pane surface, a fraction of the beam is reflected back. The size of that proportion is dependent on the window surface, incident angle and wavelength of the radiation. A part of the radiation that is not reflected off the pane is absorbed as heat but the remaining proportion is directly transmitted through the pane. The energy absorbed in the pane as heat is then transferred out of the pane to both sides by convection, conduction and radiation. The heat transferred in that manner to the inside of the pane, opposite side of the source, is called indirect transmittance. (University of Minnesota and Lawrence Berkeley National Laboratory, 2014) Figure 1.3 shows a drawing of the radiation energy losses through a glass pane. The following four properties of windows are of most interest when quantifying their thermal performance: Heat Transfer Coefficient (U-value) Solar Heat Gain Coefficient (SHGC) Visible Light Transmittance (VLT) Air Leakage The U-value is a measure of the insulation value with regard to conduction, convection and long-wave infra-red radiation of heat through the component. The

27 1.4. THEORY 9 Figure 1.3: Simplified image of solar radiation energy losses through a single glass pane. Remake from University of Minnesota and Lawrence Berkeley National Laboratory (2014). U-value can affect both heat gains/losses due to temperature differences between the inside and the outside of a window, and also the indirect transmission of solar radiation energy absorbed by outermost pane, although the SHGC is used to quantify the solar radiation energy transmission, both direct and indirect, as a ratio of the total incident solar energy. The VLT coefficient is a measurement of the visible radiation directly transmitted. The Light-to-Solar-Gain (LSG) ratio (VLT/SHGC) is often used as a measurement of how much heat will be generated by the daylight, affecting the cooling load. (University of Minnesota and Lawrence Berkeley National Laboratory, 2014) The glazing industry has standardised methods of calculating these parameters for performance comparison of different products. Both Comité Européen de Normalisation (CEN) and U.S. National Fenestration Rating Council (NFRC) have each developed their own method for determining these parameters. Not only do they have different calculation procedures and reported partial properties, but they use different boundary conditions in the calculations. (RDH Building Engineering Ltd., 2014) This can result in mismatching parameters when using both methods or unfair comparison between two products evaluated with the separate methods. NFRC uses calculation procedures from the international standard ISO Thermal 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 determination of luminous and solar characteristics and EN 673 for U-value according to GEPVP or Glass for Europe s ([n.d.]) code of practice. These methods use CEN defined boundary conditions. D. Charlie Curcija, Ph.D. at the Lawrence Berkeley

28 10 CHAPTER 1. INTRODUCTION National Laboratory (LBNL) claims that the standards for the European methods are outdated and inaccurate (personal communication, May 8, 2015). Hanam et al. (2014) state that neither the NFRC nor CEN method can be considered better, they both have different sets of limitations. The NFRC method is said to use more accurate algorithms that are able to compare all products under the same conditions but the CEN method is said to use more realistic environmental conditions. Table 1.1 displays the environmental conditions assumed for the different methods and Table 1.2 shows the corresponding surface heat transfer film coefficients assumed. The surface film coefficients for calculations of SHGC are for summer conditions opposite to the winter conditions used for U-value calculations. (RDH Building Engineering Ltd., 2014) Table 1.1: Environmental conditions for different methods of determining window parameters. Temperatures are in o C and solar radiation in W/m 2. Method Exterior Interior Solar temperature temperature radiation NFRC (Winter) NFRC (Summer) CEN (Winter) CEN (Summer) Table 1.2: Surface heat transfer film coefficients for different methods of determining window parameters. Method Film Coefficient [W/m 2 K] Exterior Interior Comments NFRC (Winter) 26,0 Convection only. Radiation model used. Interior coefficients depend on frame system. NFRC (Summer) 15,0 Convection only. Radiation model used. Interior coefficients depend on frame system. CEN (Winter) 25,0 7,7 Combined convection and radiation coefficient (ISO for center of glass simulations). CEN (Summer) 8,0 2,5 For SHGC calculations.

29 1.4. THEORY Dynamic Glazing Three different technologies are commonly used to achieve the dynamic nature of the shading for these types windows in buildings: chromic materials, liquid crystals and electrophoretic or suspended-particle devices (SPD). The chromic materials can be divided in four categories based on their control mechanism: electrochromic, gasochromic, photochromic and thermochromic. Photochromic and thermochromic devices are controlled by light and heat respectively so, in general building application, their state cannot be controlled by a building management system or manually adjusted by the user. This lack of controllability renders photochromic and thermochromic less feasible to the others and their control system will not be simulated in this research. (Baetens et al., 2010) Indoor Climate When comparing building energy performance for different building components or different control strategies the occupant comfort levels need to be within the same range or they need to be registered and evaluated for a fair comparison. For case studies, energy savings obtained at the cost of lower comfort levels need to be subjectively justified. The indoor climate affects products, processes and the occupant comfort, health and productivity. For office building the effects of the indoor climate on the occupant is more relevant than on products and processes and since the research is aimed at office buildings this chapter will focus on effects on the occupant. One of the most common methods in Europe for evaluating the thermal indoor climate is stated in the EN ISO 7730:2005 standard (Hegger et al., 2008). This standard provides analytical methods to numerically grade the indoor thermal climate according to occupant impression. It also specifies local thermal comfort criteria considered acceptable both for general- and local thermal discomfort. (Swedish Standards Institute, 2006). EN ISO 7730:2005 uses the Fanger indexes, predicted mean vote (PMV) and predicted percentage dissatisfied (PPD), to analyse and interpret the occupant thermal comfort. The PMV index predicts the mean value of votes of a large group of people on a 7-point scale (see Table 1.3) for the experience of the thermal comfort, based on the heat balance of the human body. The PPD index is a function of the PMV index that establishes a quantitative prediction of the percentage of thermally dissatisfied occupants. (Swedish Standards Institute, 2006)

30 12 CHAPTER 1. INTRODUCTION Table 1.3: The 7-point thermal sensation scale of the PMV index. PMV (Predicted Mean Vote) Explanation 3 Hot 2 Warm 1 Slightly warm 0 Neutral -1 Slightly cool -2 Cool -3 Cold The PMV index is calculated according to equations 1.4 to 1.7. The PMV index is a function of a number of different variables. The variable notations in the formulas are explained in the nomenclature. P MV = [ ] 0, 303 e 0,036 M + 0, 028 (M W ) 3, [5733 6, 99 (M W ) p a ] 0, 42 [(M W ) 58, 15] 1, M (5867 p a ) 0, 0014 M (34 t a ) [ 3, f cl (t cl + 273) 4 ( t r + 273) 4] f cl h c (t cl t a ) (1.4) where t cl = 35, 7 0, 028 (M W ) I cl [ [ 3, f cl (t cl + 273) 4 ( t r + 273) 4] ] + f cl h c (t cl t a ), (1.5) and h c = { 2, 38 tcl t a 0,25 for 2, 38 t cl t a 0,25 > 12, 1 v a 12, 1 v a for 2, 38 t cl t a 0,25 < 12, 1 v a (1.6) f cl = { 1, , 290 Icl for I cl 0, 078 m 2 K/W 1, , 645 I cl for I cl > 0, 078 m 2 K/W. (1.7) When the PMV index has been evaluated, the PPD can be calculated according to the following equation: PPD = exp( 0, PMV 4 0, 2179 PMV 2 ) (1.8)

31 Chapter 2 Method The best approach to this project was considered to be the usage of computer models as physical models require much more effort, time and cost. The computer software chosen for the task was IDA Indoor Climate and Energy or IDA ICE (2014). IDA ICE is a whole year, dynamic, multi-zone simulation application for indoor thermal climate and energy consumption of entire buildings. The mathematical models in IDA ICE reflect the latest research and the results fit well with measured data. At the start of this work the EnergyPlus building simulation engine was tried out for the task as it has a built in feature of simulating a dynamic window and it has been used in other studies (e.g. Lee et al. (2014)). The transparency of IDA ICE made it much easier to understand and its extremely flexible nature made it possible to customise the models to needs and to build the dynamic behaviour of a smart window, even though it is not available by default in the software. To minimise the calculation time and simplify the results a Shoe Box model of a defined section of a building is simulated, see Figure 2.1. All loads and schedules resemble activities for an office building with operation hours from 07:00 to 18:00 every weekday. All the simulated cases are based on the model foundation that is described in section 2.1. For estimating the impact of different window shading methods, three model variations are created: one variation with a dynamic window, one with an operable external blind for comparison and one variation with an unshaded window as a reference. The model variations are described in section 2.2. Simulations are run for six locations within Europe to see the dynamic window performance in various climates representing different latitudes. The Shoe Box is turned with the window facing south, east and west in separate simulations for each location and for each direction the three model variations are simulated. For Madrid, one extra model variation is run for a sensitivity analysis of the dynamic window SHGC. 13

32 14 CHAPTER 2. METHOD 2.1 The Model Base Building Geometry The geometry of the Shoe Box is taken from the EN 15265:2007 standard (Swedish Standards Institute, 2007) for validation tests of building simulation software. That geometry has a high window to wall ratio (WWR) and that was considered optional for emphasising the impact of different fenestration systems on the building s performance because energy savings from electrochromic windows should be greater with larger windows (Lee et al., 2014). It should be kept in mind when evaluating the results of this study that the level of energy savings obtained in buildings with as large WWR as the Shoe Box might not be reached in buildings with smaller WWR. Figure 2.1: The Shoe Box model used for the simulations. The dimensions of the Shoe Box are the following: depth 5,5 m, width 3,6 m and height 2,8 m. That gives an external surface of 10 m 2, floor area of 20 m 2 and a zone volume of 55 m 3. The window has a height of 2 m and a width of 3,5 m with a 0,05 m wall margin on the sides and the top. The window surface is therefore 7 m 2 and the window to wall ratio for the external wall is close to 0, 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.

33 2.1. THE MODEL BASE 15 Table 2.1: External wall materials used for the all models. Materials and thickness External wall Outside Render Light insulation L/W concrete Render Inside Total thickness [cm] 52 Total U-value [W/m 2 K] 0, cm 25 cm 25 cm 1 cm The internal structural elements have adiabatic boundary conditions so the net heat transfer across them is zero but they are able to store heat. The internal walls are made of gypsum and the floor and ceiling are made of concrete. The internal element materials are displayed in Table 2.2. Table 2.2: Internal structural components used for all models. Materials and thickness Internal walls Internal floor/ceiling Outside Gypsum 2,6 cm Concrete 15 cm Air 7 cm L/W Concrete 2 cm Gypsum 2,6 cm Floor Coating 1 cm Inside Total thickness [cm] 12,2 18 Total U-value [W/m 2 K] 1,707 2, 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/m 2 for sedentary activity (Swedish Standards Institute, 2006) and IDA ICE assumes the surface area of 1,8 m 2 /person that corresponds to Nilson s (2007) 1,77 m 2 /person for the average Scandinavian population. This means that the occupant generates 126 W of heat in the model. For the thermal comfort calculations clothing insulation is assumed 85 ± 25 clo = 0,13 ± 0,04 m 2 K/W. Variable clothing levels represent the person s ability to change clothing according to temperature. Variable clothing level can also influence the power emitted by the person. The occupant is placed at the centre of the Shoe Box, about 2,3 m away from the window. The workplane height is set to 0,8 m. Equipment in the Shoe Box

34 16 CHAPTER 2. METHOD is assumed to use 150 W of electricity power and generate 150 W of heat. The equipment is only turned on during occupancy. Two lighting units are in the ceiling, each with 50 W input power. Their luminous efficacy is set to 20 lm/w thus able to produce in total 2000 lm luminous flux at full power Room Heating and Cooling Units For simplification, the Shoe Box model has idealised local room units for heating and cooling. The units are assumed to have no power limitations, thus able to maintain setpoint temperatures even at high thermal loads. Coefficient of performance (COP) for both the ideal heater and ideal cooler is assumed equal to 1 and no emission losses are registered. By having this configuration, the registered supplied energy can be used as a measurement of the thermal energy flows required to maintain the heating and cooling setpoints. For the ideal heater, the supplied energy equals the sensible heat provided for the zone as the heater does not add or remove moisture from the air. The supplied energy for the ideal cooler equals the sum of the latent and the sensible heat removed from the zone as the cooler can remove moisture from the air. An air handling unit (AHU) is not connected to the model as air changes and thermal recovery are not of interest in the study Zone Lighting and Thermal Setpoints The artificial lighting in the model is dimmable, controlled by occupancy and natural illuminance at workplane. The minimum natural illuminance for full artificial lighting to be active is set to 100 lx and the artificial lighting is turned of at above 500 lx natural illuminance. Between these points the artificial lighting is given a linearly interpolated value. The lighting is turned off when the office is vacant. During vacancy there is no requirement of natural illuminance so at that time the measured level of natural light does not affect the shading signal. The thermal setpoints for the zone are set for the air temperature as it is more common in reality than to use the operative temperature. The heating setpoint is set to 20 o C and the cooling setpoint is set to 26 o C. A setpoint shift of ±6 o C is used during vacancy so the building is not heating or cooling when it is not needed. These setpoints are determined with reference to EN ISO 7730 (Swedish Standards Institute, 2006) (see Table 2.3). The values in that table are for the operative temperature and to use them for the air temperature will have an impact on the occupant comfort. The occupant comfort will therefore have to be evaluated when comparing the results.

35 2.2. MODEL VARIATIONS 17 Table 2.3: Operative temperature requirements for sedentary activities according to EN ISO 7730 (Swedish Standards Institute, 2006). Category Summer (cooling period) Winter (heating period) A 24,5 o C ± 1,0 22 o C ± 1,0 B 24,5 o C ± 1,5 22 o C ± 2,0 C 24,5 o C ± 2,5 22 o C ± 3,0 2.2 Model Variations All simulated models are based on the model described in section 2.1. The window shading parameters and the shading strategies are the only things that change between the different models. Four model variations are used for the simulations, each described in the following sections Window Without Shading This first model variation is used as a reference case. The window in the model is without any type of shading. The window parameters used for this case are displayed in Table 2.4. The values are obtained from the clear state of the dynamic window product introduced in section 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 T sol T vis U-value Clear state 0,413 0,331 0,602 1, Window With External Blind The second model includes an external, operable, window blind. The same clear state values are used for this window as for the window without shading and the dynamic window. The shaded state values of this window are obtained by built in multipliers that represent an active external blind. In IDA ICE the shaded state values are not entered directly but they are calculated by multiplying the clear state values and the relevant multipliers (see Equation 2.1 in section 2.3). The window parameters for this case are displayed in Table 2.5. The shading control strategy is custom made for the external blind using the same setpoints as the shading control strategy for the dynamic window in the following section. More information on the shading controls and the shading control strategies may be found in section 2.3.

36 18 CHAPTER 2. METHOD Table 2.5: Window parameters used in IDA ICE for the externally shaded window. SHGC T sol T vis U-value Clear state 0,413 0,331 0,602 1,56 Shaded state 0,058 0,030 0,054 1, Dynamic Window In a literature review of properties, requirements and possibilities of dynamic windows for daylight and solar energy control in buildings published by Baetens et al. (2010) it is stated that electrochromic windows seem to be the most promising stateof-the-art technology for daylight and solar energy purposes. Based on that, the dynamic window properties chosen for the energy simulation model in this study are representative for a high-end electrochromic window product. Even though it is not the purpose of this research to model a specific product or technology of dynamic glazing, the properties that are chosen for the dynamic glazing needed to be realistic and show the potentials for products in the near future. The extreme state parameters used for the dynamic window in the model are displayed in Table 2.6. They represent an actual electrochromic window product by SAGE Electrochromics: SageGlass Clear w/sr2.0. These values are available in the product specifications from the manufacturer and the same values can be obtained by using the computer program Window 7 (2014) to calculate the combined insulated glass unit (IGU) parameters with NFRC environmental conditions and ISO method for thermal and optical calculations (see section 1.4.2). Information on the shading controls may be found in section 2.3. Table 2.6: Window parameters used in IDA ICE for the dynamic window (NFRC conditions). SHGC T sol T vis U-value Clear state 0,413 0,331 0,602 1,56 Shaded state 0,087 0,005 0,009 1, Dynamic Window (CEN Conditions) for Sensitivity Analysis As mentioned in the previous section the dynamic window parameters provided by the manufacturer are calculated with NFRC environmental conditions and ISO method for thermal and optical calculations. These methods are used in North America but other methods are commonly used in Europe as discussed in section Table 2.7 displays the window parameters for the same product but calculated according to European, CEN defined, methods in Window 7 (2014).

37 2.3. SHADING CONTROLS 19 Table 2.7: Window parameters used in IDA ICE for the sensitivity analysis of the SHGC (CEN conditions). SHGC T sol T vis U-value Clear state 0,431 0,346 0,602 1,47 Shaded state 0,116 0,005 0,009 1,47 The two methods produce slightly different SHGC for the extreme states as may be seen when Table 2.6 and Table 2.7 are compared. The NFRC method gives a shaded state SHGC value that is 25% lower than that obtained by the CEN method. That means that the NFRC method provides a shaded state SHGC that is more in favour of the dynamic window product and makes it look like it is able to reject more heat in a shaded state than according to CEN methods. The NFRC calculated values are chosen to represent the dynamic window in this research (see section 1.4.2) but a sensitivity analysis is conducted to see the effect of using the CEN values in parallel. 2.3 Shading Controls The CeWind or Simple Window Model in IDA ICE uses input values of SHGC, U-value and T sol to represent the window in a fully clear state (when the shading signal is 0). In this model, the shaded state values are obtained by multiplying the clear state values with relevant multipliers (m g in equation 2.1). So the shaded state values are not entered directly to the model but calculated from the clear state values and their multipliers. When the shading signal takes on a value between 0 and 1 the parameters will take on a linearly interpolated value between the two extreme states. This can be described mathematically by the following equation as the center of glass SHGC is used as an example. The center of glass SHGC is represented by g in the equation, m g represents the multiplier, S signal is the shading signal and the subscript 0 (e.g., g 0 ) denotes the original, fully clear state value. g = (m g g 0 ) S signal + g 0 (1 S signal ) (2.1) Dynamic Window Shading Controls An electrochromic window unit has predefined shading steps built in. The number of steps and their levels is defined by the manufacturer and it cannot be changed after production. (Lee et al., 2014) The control strategy in this energy simulation does not account for these predefined shading steps, but assumes the window can take on any shading state linearly interpolated between the two extreme shading states (on/off). Dynamic windows have a certain response time and the pane can appear non-uniform while changing states. Mäkitalo (2013) made a sensitivity analysis of the response time of a dynamic window on the simulated HVAC energy consumption

38 20 CHAPTER 2. METHOD and the result was that the response time has very little effect. With that in mind, the dynamic window control strategy in this study does not account for a shading response time and all requested changes in shading occur instantly. The combined shading strategy used for the dynamic window in this research is largely based on the three different components created by Mäkitalo (2013) discussed in section but with few additional components. A flowchart of the combined shading strategy for the dynamic window is displayed in Figure 2.2. The component Free solar heat wanted? evaluates if the solar heat is to be rejected (during cooling periods) or harvested (during heating periods). It uses the mean internal air temperature and a 24 hour sliding average of the external ambient air temperature as controls. During occupancy, if either the sliding average external air temperature exceeded 8 o C or the internal air temperature exceeded 23,5 o C the solar heat is to be rejected. During vacancy these setpoints are different. During vacancy the setpoint for the sliding average of the external air temperature is 7 o C and the setpoint for the air temperature is 22 o C on weekdays and 21 o C during weekends. These setpoints are obtained by trial and error for trying to find the balance temperature for the building and its thermal loads. Global radiation of 225 W/m 2 on façade is used as a setpoint for the window to turn to 50% shading when the solar heat was wanted (during heating periods). Global radiation of 450 W/m 2 on façade is used as a setpoint for the dynamic shading to turn to maintaining 800 lx at workplane when the solar heat is wanted. This strategy is a combination of Mäkitalo s (2013) Schedule, façade and window algorithm and the Workplane algorithm. The setpoints are the same except the 800 lx at workplane. Here it is raised from 500 lx when the solar heat is wanted to increase positive solar heat gain yet still providing protection from excessive solar heat gain. When no direct radiation hits the façade, these setpoints are inactive, also when the solar heat is not wanted (during cooling periods) the shading maintains 500 lx at workplane at all times during occupancy so the 225 W/m 2 and 450 W/m 2 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.