ASSESSMENT OF WINDOW SYSTEMS CONSIDERING SOLAR AND … · 2018-08-30 · IES VE Integrated...

ASSESSMENT OF WINDOW SYSTEMS CONSIDERING SOLAR AND THERMAL PERFORMANCE

Frank Gergaud & Evangelos Marios Liaros

Master Thesis in Energy-efficient and Environmental BuildingsFaculty of Engineering | Lund University

Lund UniversityLund University, with eight faculties and a number of research centers and specialized institutes, is the largest establishment for research and higher education in Scandinavia. The main part of the University is situated in the small city of Lund which has about 112 000 inhabitants. A number of departments for research and education are, however, located in Malmö and Helsingborg. Lund University was founded in 1666 and has today a total staff of 6 000 employees and 47 000 students attending 280 degree programmes and 2 300 subject courses offered by 63 departments.

Master Programme in Energy-efficient and Environmental Building DesignThis international programme provides knowledge, skills and competencies within the area of energy-efficient and environmental building design in cold climates. The goal is to train highly skilled professionals, who will significantly contribute to and influence the design, building or renovation of energy-efficient buildings, taking into consideration the architecture and environment, the inhabitants’ behavior and needs, their health and comfort as well as the overall economy.

The degree project is the final part of the master programme leading to a Master of Science (120 credits) in Energy-efficient and Environmental Buildings.

Examiner: Henrik Davidsson (Energy and Building Design)Supervisor: Niko Gentile (Energy and Building Design), Harris Poirazis (Inform Design)

Keywords: Window systems, solar energy, thermal transmittance, Dynamic Thermal Modelling tools

Thesis: EEBD–18/05

Assessment of window systems considering solar and thermal performance

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Abstract The solar and thermal performance of window is subject to a wide range of parameters. To simulate the energy performance is therefore difficult, and limitations might occur depending on the complexity of each case. This study presents some of the current limitations related to the assessment of window considering solar and thermal performance into the policy framework (standards, regulations and certifications) and two dynamic thermal modelling (DTM) tools. It is observed that the regulations and certifications referred to various standards regarding the assessment of solar and thermal performance that could affect the results. The study concluded that for more complex constructions, the difference between the software resulted in bigger deviation among the simulation outputs. By investigating some of the limitations in the DTM tools, this study also provides possible warnings for the assessment of window. Preface The present document represents the final project of the master’s degree, promotion 2018, in the field of Energy-efficient and Environmental Building Design from Lund University. We would like to take this opportunity to express our sincere thanks to our supervisor, Dr. Niko Gentile from Lund University and the company Inform Design AB. They supported us in every stage of the thesis. Secondly, we would like to thank Medina Deliahmedova for her help and precious advices during the learning process of the software (WIS 3 and IDA ICE). Finally, yet importantly, a special thanks to Dr. Bengt Hellström for his technical support regarding the equation used in IDA ICE. Moreover, our gratitude goes to Scheldebouw B.V. for giving us further details about the manufacturing process of glazing systems (Scheldebouw Middelburg, Netherlands). Evangelos Marios Liaros & Frank Gergaud 2018


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Glossary of terms and definitions Actual building The examined building being simulated as-built with the relevant

information about size, shape, orientation, shading, activities, zoning, building fabric, fittings and services.

ASHRAE American Society of Heating, Refrigerating and Air-Conditioning Engineers BER BER (actual Building CO2 Emission Rate) is the actual energy performance

of the building examined (HM Government, 2016)BRE Building Research EstablishmentDGU Double Glazed Unit DTM Dynamic Thermal ModellingEVC External Ventilated Cavityg-value Solar transmittance through the glazed construction at specific boundary

conditions set by the standard. g-value is unitless. HVAC Heating, Ventilation and Air-ConditioningIB Integrated Blind Iterative method An iteration procedure or iterative method is a mathematical procedure that

is used to solve nonlinear system equations based on initial guess. Low-E Low emissivity coatings are used on glass panes to reduce the shortwave

infrared transmitted by the pane.MRT Mean Radiant TemperatureNCM National Calculation MethodologyNonlinear equation Equation wheren the output change without proportionality with the input Notional building A hypothetical building of the same size and shape as the actual building, but

with pre-defined specified properties for the building fabric, fittings and services..” (BRE Global Ltd., 2017)

SBEM Software tool which provides simplified energy calculations for buildings to comply with UK regulations

SC (Solar Control) Solar control coatings are selective coatings used on glass panes to reduce significantly the shortwave infrared transmitted by the pane, while allowing for the biggest portion of light to pass through.

TGU Triple Glazed UnitTime step It is the set accuracy of the dynamic simulation.U-value Thermal transmittance through the construction at specific boundary

conditions set by the standard and without solar radiation. The unit is in W/(m²ꞏK)

Symbols and units hcv,ex External convective heat transfer coefficient W/(m²∙K) he/hi Surface coefficient of heat transfer (he = external; hi = internal) W/(m²∙K) Is Incident solar radiation W/m² Ji Radiative heat flow W/m² Q total heat transfer through the window W/m² Tint Internal temperature °C Top Operative room temperature °C Tout External Temperature °C Uf U-value of the frame area W/(m²∙K) Ugv U-value of the glazed area W/(m²∙K) Ut Total U-value W/(m²∙K)


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Table of content

1 Introduction ............................................................................................... 6 1.1 Background information 6 1.2 Goals 6 1.3 Problem statement 6 1.4 Research questions 6 1.5 Work structure 7 1.6 Software tools 8 1.7 Limitations 8

2 Methodology ............................................................................................. 9 2.1 Policy framework 10

2.1.1 Standards 10 2.1.1.1 ISO 15099 10 2.1.1.2 EN 13363-2 11

2.2 Regulation and certification systems 12 2.2.1.1 The Building Regulation - Part L 12 2.2.1.2 BREEAM 13 2.2.1.3 Miljöbyggnad 13

2.3 Description of the cases 13 2.4 Phase 1 - Steady state calculations 16 2.5 Phase 2 - Dynamic thermal modelling 17 2.6 Phase 3 – Annual heating and cooling need 21

3 Results ..................................................................................................... 22 3.1 Preliminary phase 22

3.1.1 Standard comparison (ISO 15099 and EN 13363-2) 22 3.2 Phase 1 – Steady state calculations 23 3.3 Phase 2 - Dynamic thermal modelling 25 3.4 Phase 3 – Annual heating and cooling need 30

4 Discussion ............................................................................................... 33 5 Conclusion ............................................................................................... 36 6 References ............................................................................................... 37 Appendix A ..................................................................................................... 38 Appendix B ...................................................................................................... 39 Appendix C ...................................................................................................... 40 Appendix D ..................................................................................................... 41 Appendix E ...................................................................................................... 52


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1 Introduction

1.1 Background information

Window systems play a primary role on the solar and thermal performance of the buildings. During the last few decades, the growing popularity of transparent constructions in office buildings worldwide propelled a rapid and fascinating development of this type of constructions. With the increasing complexity of window constructions, the performance becomes difficult to assess with simulation software. Not surprisingly, the current scientific literature shows that there is a discrepancy between the empirically collected data and the simulations performed (Kalyanova et al., 2009).

1.2 Goals

The M.Sc. thesis makes an investigation of the solar and thermal performance of exemplary type of windows used in office buildings. The performance is calculated by means of different tools, namely standards and Dynamic Thermal Modelling tools (DTM). The M.Sc. thesis identifies the relationships and differences between the results and aims to explain the underlying reasons of divergences.

By explaining the reasons of divergences, this thesis fulfills its main goal, which is to raise awareness about possible limitations in this field.

1.3 Problem statement

How the solar and thermal performance of window systems is currently assessed in the policy framework and software?

1.4 Research questions

The thesis followed three research questions complementary to the problem statement.

Which are the current limitations related to the solar and thermal performance of glazing systems in the DTM tools and policy framework?

How could those limitations affect the solar and thermal simulation of buildings with such constructions?

What could be proposed to enhance the current assessment of solar and thermal performance of window?


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1.5 Work structure

The standards ISO 15099 and EN 13363-2 were used as fundamental reference for describing the physics and the calculation process for the solar and thermal performance of the window. An investigation regarding the difference between those standards was carried out and the results can be observed in the section 3.1 Preliminary phase.

Later, an overview analysis of national building regulations of United Kingdom, as well as of two voluntary energy certification systems used in Sweden and in UK, was carried out. The study aimed to investigate differences in the current way of examining window performance (indices used, categorization and thresholds).

In the next stage, the solar and thermal performance of several exemplary window cases were simulated with one well-defined software for assessment of window systems in steady-state: WIS 3 and two widespread DTM software: IDA ICE and IES VE.

Finally, in order to highlight possible divergences in the results and their causes, an investigation of the software structure – some equations used, assumptions made, limitations of the inputs or outputs - was carried out.

More specifically, the work was divided as follow.

Defining the field of action, consisting of

a. Preliminary phase, which included an in-depth study of selected standards, certifications and regulations concerning the solar and thermal performance of windows. Moreover, the preliminary phase included the selection of the five exemplary cases.

Modelling, calculating, and understanding, consisting of three phases for the five

cases.

a. Phase 1 comprised the steady state U-value and g-value calculations. b. Phase 2 consisted of the DTM simulations. c. Phase 3 focused on the analysis of a single room, examining the findings from

Phase 2 in terms of energy need.


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1.6 Software tools

Three different software tools were used in this thesis: WIS 3 (Window Information System), IDA ICE (IDA Indoor Climate and Energy) and IES VE (Integrated Environmental Solutions Virtual Environment), where WIS 3 was used for Phase 1 only. The software was selected because it is validated, well established in the industry and commonly used in Europe.

WIS 3

WIS 3 is a software tool, developed under the Thematic Network WinDat (2001-2004). It uses the methodology and the formulas described in ISO 15099 to assess the solar and thermal characteristics of window systems and components in steady-state. The version used for this study was WIS 3.0.1 SP2 (winDat Thematic Network, 2006).

IDA ICE

IDA ICE is a DTM tool developed by EQUA, and it is widely used in Sweden, among other countries. The mathematical model used in IDA ICE is traceable. The software provides a wide range of variables that can be logged and analyzed. IDA ICE was used in Phases 1, 2 and 3. The version of IDA ICE used in this study was the 4.7.1 in expert mode (EQUA, 2018).

IES VE

Integrated Environmental Solutions – Virtual Environment (IES VE) is a software suite that unifies several specialized modules related to the energy analysis and performance of the buildings. Integrated Environmental Solutions Limited develops this tool, which is widely used in the UK among other countries. IES VE was used in Phases 1, 2 and 3. The version used for this study was VE 2017.3.0.0.

1.7 Limitations

The study was limited to the solar and thermal performance of only the glazed part of the window. Therefore, the effect of the frames or the thermal bridges on the solar gains and the heat transfer was excluded from the analysis.

Newly built office was the building type selected for this study, as it is among the building types with the highest applicability for glazing systems. Therefore, the study considered the national regulations and the most common certifications in Sweden and in UK related to this typology of building. In addition, the conclusions are drawn based on the five examined cases that cover a variety of window solutions.

The shading device was simulated as a pane in order to follow the standard procedure from ISO 15099 and to be able to compare the results between the software. Indeed, it was noticed that IDA ICE and IES VE had different options for the settings of the shading devices and the comparison was therefore not possible if the shading device was not simulated as a pane. The characteristic properties of them were calculated accordingly and were assigned as properties of the panes. Consequently, there was no ventilation between the pane with the shading device properties and the rest layers of the window system for each case.

Finally, windows’ performance was not compared against measured values in, for example, experimental testbeds. Therefore, the findings refer to the difference between software and the conclusions are drawn considering the standard as benchmark.


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2 Methodology

The work consisted of a preparatory review of the policy framework, followed by software simulation and data analysis. The overall workflow is presented in Figure 1.

ASSESSMENT OFWINDOW

CONSIDERING SOLARAND THERMALPERFORMANCE

INPUTS(Policy framework)

SELECTIONOF CASES

- PHASE 1 -Steady-statecalculation

4# TGU2 LowE

5# TGULowE + SC

- PHASE 3 -Energy simulation

1# DGUclear

2# DGULowE

3# TGULowE + IB

- PHASE 2 -DTM simulation

U-value g-valueInner

surfacetemperature

Total solargain

Heattransfer

Workflow Legend

Research fieldMonitored output

Subject of thesis

Heatingenergyneeds

Coolingenergyneeds

OUTPUTS

IDA-ICE IES VEWIS

Although part of the work itself, the outcomes of the review are rather presented in this section, as they shaped the following methodology used for simulation and data analysis. In particular, the review helped in finding the possible criticalities of software, and, therefore, suggested the choice of the specific cases.

Figure 1: Workflow summary


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2.1 Policy framework

2.1.1 Standards

The theoretical framework of the study was based on the current international and European standards used to assess the solar and thermal performance of windows. Specifically, ISO 15099 and EN 13363-2 were studied, as they were the most commonly used at the time of writing. The ISO 15099 was reviewed in 2016 and remained up-to-date, while EN 13363-2 was replaced by EN ISO 52022-3:2017.

The main focus of this study was to understand the affecting factors considered and the overall methodology, rather than the mathematical formulas used to describe the physical phenomena taking place.

2.1.1.1 ISO 15099 ISO 15099 is an international standard for the detailed calculation of solar and thermal transmission properties of fenestration systems. ISO 15099 describes a detailed procedure for calculating the thermal performance of windows, doors and shading devices and is the theoretical framework for validated software tools. Specifically, WIS 3 and IDA ICE use this international standard as a base for the solar and thermal performance calculations. ISO 15099 specifies the calculation methodology for:

The thermal transmittance of window. The solar energy transmittance of window.

In details, the calculation procedure takes into account:

Single and multiple glazed unit systems. Coatings (low-E and solar-control). Pane spacing with gas and mix of gases. Four types of gases are included: Air, Argon,

Krypton, and Xenon. Shading devices: only screens, curtains, and venetian blinds. For example, overhangs

shading systems (e.g. brise-soleil) are excluded from the international standard. According to ISO 15099 the main physics properties that affects the overall performance of windows, are the thermal transmittance, the solar energy transmittance, the effect of shading devices, and the effect of ventilation. The energy balance quantifies the thermal balance through the window regarding any effect of the surrounding environment.


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Energy balance

The energy balance in a window system is summarized in Figure 2.

Figure 2:Energy balance on double pane window

Three variables affect the performance of glazing systems:

Thermal transmittance Solar absorptance, transmittance and reflectance

The energy balance equation is calculated with an iterative procedure until a certain accuracy threshold is reached. The four variables are interconnected to each other.

2.1.1.2 EN 13363-2 The European standard EN 13363-2 describes the detailed calculation for the total solar energy transmittance of the glazing system. EN 13363-2 is also used for the assessment of the thermal performance of window systems and the calculation methodology is similar to ISO 15099. This standard refers to EN 673:2011 and EN 410:2011 for U-value and g-value calculations respectively.


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2.2 Regulation and certification systems

The regulation in UK and certification in Sweden and UK were analyzed. This section explained briefly the criteria to comply with the regulation and certification and how the validation process is described.

2.2.1.1 The Building Regulation - Part L Part L is the part of the Building Regulation in UK that guides stakeholders from the building industry to design and construct energy-efficient buildings.

Part L2A:

Part L2A is one of the four parts from Part L of the Building Regulation in United Kingdom that focuses on the energy issues for new buildings. Therefore, as the study focuses on solar and thermal performance of window used in office buildings, Part L2A must be taken into consideration for UK. Part L2A comply with EN 410 and indirectly to EN 673 which is a normative reference for EN 13363-2. The design standards should comply with five criteria:

1. Achieving the TER (Target CO2 Emission Rate) 2. Limits on design flexibility 3. Limiting the effects of heat gains in summer 4. Building performance consistent with the BER 5. Provisions for energy-efficient operation of the building

Criteria 2 and 3 are the most interesting in this study, as they are linked to solar gains and thermal transmittance respectively. In order to comply with the building regulation and these criteria, the calculation must be done according to the NCM (National Calculation Methodology) with a specific software tool called SBEM. Other software tools, such as IES VE, can be used as well after being approved by BRE (Building Research Establishment).

NCM:

The National Calculation Method (NCM) comprises the methodology and the data sets for demonstrating compliance with the Building Regulations for buildings other than dwellings. The process is carried out by calculating the annual energy use for a proposed building and comparing it with the energy use of a comparable 'notional' building, which has pre-defined specified properties for the building fabric, fittings and services. (“UK NCM,” 2016)

In NCM, a real building is defined as to be the building designed with all the information required (construction, location, orientation, systems set up, fabric parameters). The building should only be modelled through accredited simulation programs or Simplified Building Energy Model (SBEM) software tool. Subsequently, an actual building is created with the same information as the real building but with the NCM database instead. This actual building is used for comparison purposes with the notional building. Finally, a notional building is automatically created from the real building and it is determined by the information input to the SBEM calculation (Building fabric, Zones, Air permeability, HVAC system, Lighting, General input data).

NCM describes a methodology for the overall energy simulation of an office building and set the threshold that must be respected for compliance. However, no specific guidelines or methodology were provided for the modeling of window except from SBEM.


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2.2.1.2 BREEAM Building Research Establishment's Environmental Assessment Method (BREEAM) is a sustainability rating scheme for the built environment, developed and owned by Building Research Establishment (BRE). It focuses on sustainability in building design, construction and use. It assists the measuring and reduction of the environmental impact of buildings and creates higher value and lower risk assets for the clients (“BREEAM International New Construction 2016 - SD233 2.0,” 2016).

BREEAM certification follows the same methodology described in Part L, meaning that in order to evaluate the performance of the examined building, it compares it to the notional building. BREEAM follows the same standard as part L.

BREEAM certification does not explicitly refer to the solar and thermal performance of the window. This type of construction is regarded together with the rest of the building envelope only through the NCM.

2.2.1.3 Miljöbyggnad Miljöbyggnad is a Swedish environmental certification system for buildings (Sweden Green Building Council, 2018). The certification is oriented towards ensuring a safe and energy efficient construction with emphasis on the quality of the indoor environment.

The performance of the buildings is ranked (bronze, silver and gold), and the ranking depends upon the satisfaction of specific minimum requirements. The methodology is based on examining the worst case possible (harsh environmental conditions) rather than the realistic annual performance of the building.

The certification system does not seem to go into detailed examination of the characteristics of each case or reward an innovative design. The performance of window is not examined, even though, its influence on the overall performance is evident through the considered indices in all the relevant categories.

Miljöbyggnad refers to ISO 15099 and EN 13363-2 for solar energy transmittance and ISO 6946 for the thermal transmittance. Noticed that for the standard related to the thermal transmittance, the calculation methodology is for thermal transmittance of windows without the effect of solar radiation.

2.3 Description of the cases

For this study, the analysis was limited to five types of window constructions. The selection was based on the categorization of the window by the industry and the common practice (cavity width, properties of the layers), as well as, on the factors affecting the solar and thermal performance (mainly gas type, coatings and shading). The complexity was built up gradually from the first to the last case. All the cases were tested with and without the selected three shading devices, namely, two venetian blinds (reflective and absorptive), and a reflective roller shade. The five cases are illustrated in Table 1. Appendix A provides all the input parameters used for the pane and blinds.


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Table 1: Description of the constructions Legend:

: Blind (Venetian absorptive/reflective blind at 85°) or Roller Blind : Low-E coating : Solar control coating

CASE #1 Double Glazed Unit (DGU) with clear panes and Argon The first case was the touch base case used to calibrate the software tools.

CASE #2 Double Glazed Unit (DGU) with Low-E coating and Argon The second case introduced a Low-E coating, emphasizing on the simulation of long-wave radiation and how it affected the examined outputs.

CASE #3 Triple Glazed Unit (TGU) with Low-E coating and Integrated Blind (IB). Following the cases one and two, the choice was to investigate the integrated blind with Low-E coating. Indeed, the interstitial shading would directly affect the solar gains, stressing more on the secondary transmission and pointing out any subtle differences in the thermal performance of the construction.

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CASE #4 Triple Glazed Unit (TGU) with two Low-E coatings and Argon The use of two Low-E coatings in a TGU was selected to further investigate the bounce effect on radiation in between the coatings. The temperature, especially in the gap between the coatings, would be highly depended on the way the calculations would be carried out.

CASE #5 Triple Glazed Unit (TGU) with Solar Control and Low-E coatings and Argon Changing one of the Low-E coatings on the previously examined TGU to a Solar Control (SC) coating, would add the subtle details of selectivity. In that way, the mapping of the solar performance, hence, the overall thermal performance, would be complete.

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2.4 Phase 1 - Steady state calculations

The first phase of the study focused on the steady state calculations. The standard used was ISO 15099, while the boundary conditions were set according to EN 673:2011 and EN 410:2011 for U-value and g-value calculations respectively (Table 2). It is worthwhile noting that the aforementioned indices were also examined and compared both in WIS 3 and the two DTM tools.

Table 2: Boundary conditions (steady-state calculation)

Description Unit For U-value calculation

For g-value calculation

Air temperature outdoor °C 0 30

Air temperature indoor °C 20 25

Radiant temperature outdoor °C 0 30

Radiant temperature indoor °C 20 25

Direct solar radiation (W/m2) 0 500

Convection coefficient outdoor (W/m2ꞏK) 20 8

Convection coefficient indoor (W/m2ꞏK) 3.6 2.5

The comparison aimed mainly at investigating whether, given the same properties in all software tools, there would be any divergences among the U-value and g-value calculations. Those deviations could have a significant effect on the dynamic performance of the constructions, thus they should be monitored from the beginning.


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2.5 Phase 2 - Dynamic thermal modelling

The second phase focused on the analysis of the used DTM tools. This section describes the methodology followed to perform the assessment of the solar and thermal performance of the five window cases with the two DTM software tools, IDA ICE and IES VE.

Location

The climate file selected to assess the difference between the two DTM tools was London (GBR - Gatwick Airport) from ASHRAE IWEC 2. The climate data was selected as indicative of the northern European climate from the database of each DTM tool.

Individual cases

The five examined cases - with and without shading - were analyzed for the most representative days of the year with two orientations (see Table 3). The orientations were chosen according to the sun altitude, which could drastically affect the output analyzed in Phase 2 and 3 of the window. In details, south orientation was mainly accounted for the high sun altitudes, while west orientation for the low ones. East orientation was discarded due to the limited time of the study and west orientation was considered alone as it is the worst-case scenario for overheating. North was discarded, as its contribution to the study would have been minimal. Concerning the date and for the scope of accentuating the possible divergence between the software tools, the days with the highest and lowest beam and diffuse radiation were selected for the assessment of the solar performance. Similarly, the days with the highest and lowest outdoor dry bulb temperature were selected for the assessment of the thermal transmittance. Table 3 summarizes all the individual subcases analyzed for each window constructions. In definitive, each of the five cases were tested for the six subcases, using the three different type of blinds. A total of ninety cases were analysed.

Table 3: Details of Subcases SUBCASE ORIENTATION DETAIL DATE

SUBCASE#A West Highest beam radiation 24-April SUBCASE#B West Highest diffuse radiation 23-July SUBCASE#C South Highest beam radiation 24-April SUBCASE#D South Highest diffuse radiation 23-July SUBCASE#E South Highest temperature 30-june SUBCASE#F South Lowest temperature 07-december


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Construction and calibration process

The building model used to perform the simulation with IDA ICE and IES VE was a shoebox model with a floor area of 5 m × 5 m (25 m²) and a centered window of 3 m × 2 m (6 m²). Figure 3 presents the geometry of the model. The goal was to analyze the performance of the glazing system alone. A calibration process was carried out to define the input parameters that could bias the analysis and then discard their effect. The first case without blinds was used for this purpose. The impact of each parameter was analyzed one at a time in both IDA ICE and IES VE. Appendix B presents the table which summarizes every input used after the calibration process.

o Other specific inputs

In IDA ICE, the window type was set to “window detailed”, as the setting “simply window” did not fit the thesis purposes. Regarding the climate data, the daylight-saving time was removed from the results in both programs.

An investigation about the sky diffuse model was performed as different models could be used. In IDA ICE, the sky diffuse model (Perez or ASHRAE) recalculates the diffuse solar radiation on a window from the horizontal irradiation given by the weather data. IES VE, instead, simulates the solar radiation as isotropic or anisotropic. After several trials before the actual thesis work, it was found that the ASHRAE sky diffuse model in IDA ICE resembled more to the anisotropic solar radiation in IES VE. Therefore, the ASHRAE sky diffuse model and the anisotropic solar radiation model were used in IDA ICE and IES VE respectively.

The mathematical models of the zone was set to Climate in IDA ICE as according to the software developer, it is the most detailed physical model that IDA ICE offers (Sahlin, 2018). In IES VE, the model type was unknown. The convection models, external and internal, selected in IES VE were “ASHRAE simple” and “CIBSE fixed values” respectively. The sky and ground long-wave radiation model in IES VE was set to “Black body at air temperature”, while internal air emissivity was set to “off” and the starting temperature to 21°C. The same inputs were not explicit in IDA ICE. Nonetheless, all the settings were tested during the calibration process of the two software tools. The gas properties in IDA ICE and IES VE were input differently. IDA ICE followed ISO 15099 and considered conduction, dynamic viscosity, density and heat capacity of the gas in different temperatures (see Appendix C), while in IES VE only resistance and convection coefficient were used. In both cases, the values were calculated according to ISO 15099.

Figure 3: Shoebox model

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Output parameters

Three output parameters were included in the comparison: inner surface temperature, total solar gains, and heat transfer through the glazed surface.

∙ Inner surface temperature.

This variable provides information about the temperature on the internal surface of the innermost pane of the construction. The Inner surface temperature is indicative of the thermal performance of the window. In the standards, the glass temperature is affected by the absorption of the incident solar radiation and the heat transfer through the glass.

∙ Total solar gains

Total solar gains comprise of all the heat gains indoors passing through the window. The heat gains can be either directly transmitted through the glazed part (primary transmittance) or through radiation and convection when the heat is absorbed (secondary transmittance).

∙ Heat transfer through the glazed area

The heat transfer due to temperature difference between indoors and outdoors could be by conduction, convection and radiation. This output assesses the thermal performance of the windows, thus, a significant index monitoring any possible differences in the study. The inputs used for calculating the heat transfer were according to hourly weather data for each subcase (i.e. hourly dry bulb temperature, hourly solar radiation).

Data analysis

The output data were analyzed in Excel. The analysis of the results was carried out using dynamic charts in relation to equations. The entire set of numerical output and equations used for the data analysis are reported in Appendix D.

o Dynamic chart

The dynamic charts were created for every case and updated automatically with the outputs from the simulations in IDA ICE and IES VE. The dynamic charts had, on the first y-axis (left side) the temperature of the inner surface temperature and on the second y-axis (right side) the power per window area (W/m²). The power per window area was used to describe the power from the total solar gains and the heat transfer per square meter of window area (6m²).


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Detailed analysis of heat transfer

This section aimed to describe the equations used in IDA ICE and IES VE for the heat transfer in order to explain the difference observed in the results section for Phase 2. The equation used in IES VE was unknown, so assumption was necessary and confirmed by the results of this analysis. Hand calculations and values from the software were monitored and analyzed in the Figure 11 and Figure 13. Since the solar gains were quite similar between the two programs and the calculations used were quite advanced, no hand calculations were done for this index. The inner surface temperature as explained in the ISO 15099 depends on the energy balance equations and is related to the heat transfer. Thus, due to the complexity of the calculations for the former (iterative calculations needed), only heat transfer was calculated in detail. The equations for hand calculations in IDA ICE and IES VE are provided below:

o IDA ICE

Calculation of heat transfer that passes through the glazed area of the window (QGlassTrans was given by the software as follow:

QGlassTrans=UGlasswoSurfRes ꞏ aGlassꞏ(TASide-TBSide) W/m² (1) Where, QGlassTrans is the total heat transfer through the window in W/m². aGlass is the glazed area in m². TASide-TBSide is the difference of inner and outer surface temperature in K.

o IES VE

The equation in IES VE was unknown, therefore, it was assumed that the equation of the heat transfer was derived from IDA ICE as follow:

QGlassTrans=Uwindowꞏ aGlassꞏ Toutside-Toperative room W/m² (2)

Where, QGlassTrans is the total heat transfer through the window in W/m². aGlass is the glazed area in m². Toutside - Toperative room is the difference of temperature between the outside

environment and operative room temperature in K. The operative temperature is an output from IES VE for the relevant case and subcase.

Uwindow is the U-value of the glazed area of the window in W/(m²ꞏK) assuming the external and internal surface heat transfer coefficients are included.


21

2.6 Phase 3 – Annual heating and cooling need

Framework

Phase 3 focused on monitoring the impact of the deviation noticed in Phase 2 on heating and cooling needed to keep the zone conditioned at 21°C, without the use of any active systems other than ideal air conditioning. The constructions comprising the zone were the same as in Phase 2, meaning that only heat gains or losses through the window were considered. The particular difference from Phase 2 was the dynamic use of the shading device throughout a whole year. Specifically, a threshold of 150 W/m² external incident solar radiation was set in order to draw the shading down. By considering the incident instead of the transmitted solar radiation, the impact of the coatings on the frequency of shading use was disregarded. Since the aim of the study was not the evaluation of the different constructions, but rather the assessment of their performance under the same settings, the use of external incident solar radiation threshold seemed to serve it better.


22

3 Results

3.1 Preliminary phase

3.1.1 Standard comparison (ISO 15099 and EN 13363-2)

ISO 15099 and EN 13363-2 were both use for the assessment of the solar and thermal performance of windows. Even though regulations and certifications refer to both, a divergence was noticed. EN 13363-2 is a simplified alternative of ISO 15099. This comparison study pointed out these differences between the calculation methodologies of the analyzed parameters as listed below:

Absorbed heat by conduction/convection: It is part of the heat flow rate equation and a difference was noticed in the calculation process between the standard when the glazing is in the vertical axis (90 degrees inclination). - ISO 15099 provides specific equations for vertical glazing. - EN 13363-2 provides two constant values that are integrated in a simplified

equation.

Boundary conditions: Boundary conditions are used to perform the U-value calculation for steady-state calculation. - ISO 15099: The temperature difference used for calculating the U-value under

winter condition in ISO 15099 is 20Kand the external convective heat transfer coefficient, i.e. hcv,ex=20 W/(m2ꞏK).

- EN 13363-2: The temperature difference used for calculating the U-value under winter condition in EN 13363-2 is 15K, having an external convective heat transfer coefficient of hcv,ex=18 W/(m2ꞏ𝑲 .

Gas properties: Gas properties are used to calculate the heat transfer in the glazing cavity. Differences were observed between ISO 15099 and EN 13363-2, an example is shown below: - ISO 15099: Argon gas has a specific heat capacity of 521.928 J/(kgꞏK) at 10 °C. - EN 13363-2: Argon gas has a specific heat capacity of 519 J/(kgꞏK) at 10 °C.

In definitive, the comparison shows that: In regard to thermal transmittance, the two standards follow slightly different procedures

especially in terms of input parameters for the convective heat transfer in the glazing cavity, the boundary condition, and the gas properties.

In regard to solar energy transmittance, EN 13363-2 follows the same methodology as ISO 15099.

ISO 15099 and EN 13363-2 aim to provide detailed calculation methodologies for the assessment of solar and thermal performance of the windows. However, a slight variation between the standards was noticed. This variation in the inputs used in both standards could affect the assessment.


23

3.2 Phase 1 – Steady state calculations

Phase 1 presents the results from the steady-state calculations in terms of U-value (Figure 4) and g-value (Figure 5). The Appendix E presents the numerical values. The analysis was performed in WIS 3, IDA ICE and IES VE. Figure 4 presents the U-value of the five selected cases and it seems that WIS 3 and IES VE are always very close to each other. A slight deviation was noticed for IDA ICE, the U-value was always higher especially for the case with high U-value and without blind. However, the difference in between the program was negligible in this phase.

Figure 4: U-values for steady-state conditions of the five cases, calculated with three different software.

0.0

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1. DGU clear 2. DGU LowE 3. TGU LowE+ IB

4. TGU 2 LowE 5. TGU 1LowE + SC

U-v

alue

/ W

/(m²ꞏK

)

WIS IDA IES


24

Figure 5 presents the results for the g-value of the five cases. The same trend was observed as for the U-value, IDA ICE was slightly different than WIS 3 and IES VE especially in the case of no blind where the g-value is usually higher. The case 3 is much more affected by the shading device.

Figure 5: g-values - for steady-state conditions of the five cases, calculated with three different software.

To conclude with Phase 1, the steady-state calculation does not differ significantly between WIS 3, IDA ICE and IES VE.

00.10.20.30.40.50.60.70.8

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1. DGU clear 2. DGU LowE 3. TGU LowE+ IB

4. TGU 2 LowE 5. TGU 1LowE + SC

g-va

lue

/ 1

WIS IDA IES


25

3.3 Phase 2 - Dynamic thermal modelling

Phase 2, based on IDA ICE and IES VE, generated a number of results for the cases analyzed. Some of those were interesting to highlight divergences between the two DTM software. Those cases are reported below, while Appendix D presents the data analyzed of every simulated cases. The same trend of results as CASE#1 were observed in CASE#2,3,4 and 5, it was decided not to show the graphs. The legend for all the graphs is shown only in Figure 6. It is noteworthy that in the cases where internal shading is used, the inner surface temperature referred to the temperature on the surface of the internal shading device.

CASE #1 - DGU with clear panes and Argon

Figure 6 presents the DGU with clear panes and Argon without shading. Subcase#C (south orientation and highest beam radiation) showed that in case of without blind, IDA ICE and IES VE performs the same. Similar results were observed between IDA ICE and IES VE. The other cases analyzed did not present different results either when there is no blind.

Figure 6: Case#1 / Subcase#C / Without blind

Figure 7, Figure 8, and Figure 9 show the results for inner surface temperature, heat transfer and solar gains in the case of blinds for a DGU clear filled with Argon. It was noticed, that the results differed between IDA ICE and IES VE especially for the heat transfer and inner surface temperature. Indeed, IDA ICE had higher heat transfer but lower inner surface temperature than IES VE. This difference became significant when the absorptive venetian blind was applied. Regarding, the solar gains, it was found out that the slight variation observed was due to weather data interpolation in IDA ICE. Figure 10 shows that the solar gain is the same in IDA ICE and IES VE when smaller time step was used. The solar gains were considered to be the same.

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er p

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ind

ow a

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/ (W

/m²)

IDA Solar Gain IES Solar Gain

IDA Heat transfer IES Heat transfer

IDA Inner surface temperature IES Inner surface temperature


26

Figure 7: Case#1 / Subcase#C / With reflective venetian blind at slat inclination of 85°

Figure 8: Case#1 / Subcase#C / With absorptive venetian blind at slat inclination of 85°

Figure 9: Figure 5: Case#1 / Subcase#C / With roller blind

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27

Weather data interpolation

In all the cases, a slight deviation was noticed between the programs for the solar gains. This deviation was due to interpolation of the weather data in IDA ICE which were calculated according to the used time step. In IES VE, the values were not interpolated, as the data were taken directly from the weather file. A time step of less than 0.05 hours in the simulation increased the accuracy of the interpolation and the results were closer between IDA ICE and IES VE. Nevertheless, the results of this thesis were calculated with 1.5 hour time-step, to maximize the speed of the calculations, as the difference in the results was minor for the examined cases. Figure 10 shows that with a lower time step (i.e. larger amount of data) the interpolation of data was not significant and the difference between IDA ICE and IES VE regarding the solar gains was the same. The results from the heat transfer and the inner surface temperature were slightly affected but this difference was neglected. The time step in IDA ICE used was set to 0.01h. This explains the difference observed in Figure 6 regarding the solar gains.

Figure 10: Case#1 / Subcase#C / Without blind / Time step set to 0.01h

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Temperature / °C

Power per window area / (W

/m²)

IDA Solar Gain IES Solar Gain

IDA Heat transfer IES Heat transfer

IDA Inner surface temperature IES Inner surface temperature


28

Detailed analysis of heat transfer The analysis of heat transfer was performed for the CASE#1.C with absorptive venetian blind where the biggest difference was observed in order to explain the difference noticed in the Figure 8 for the heat transfer in IDA ICE and IES VE.

o Heat transfer in IDA ICE

Figure 11 presents the results for the U-value used in IDA ICE (UglasswoSurfRes) combined with the total solar gains. The period analyzed is the same as the CASE#1.C with absorptive venetian blind in the Figure 8. The U-value of the window was logged from IDA ICE. The U-value varied from 2.4 W/m²ꞏK to 3.2 W/m²ꞏK.

Figure 11: Combined U-value of the window and solar gain in IDA ICE

The logged U-value seems to be affected by the solar gains. The heat transfer of the window was calculated with the U-value of the window as can be seen in Equation 1. Therefore, this analysis shows that the thermal transmittance in IDA ICE is affected by the solar gains as U-value is affected by solar gains.

o Heat transfer in IES VE

Figure 12 presents the results for the U-value of the window in IES VE combined with the total solar gains for the same three days in April as in IDA ICE. The U-value of the window was logged from IES VE and seems to be steady-state (no change during the day).

Figure 12: Combined U-value and solar gain in IES VE

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29

According to the results of the detailed analysis of heat transfer, it seems that IDA ICE considers a heat transfer that is affected by the solar radiation while the heat transfer in IES VE does not seem to vary. This could be the reason of the difference observed for the heat transfer in the Figure 8. The assumption made regarding the equation used in IES VE was proved with Figure 13. The figure compared the results from IES VE and the hand calculation made with assumed equation for IES VE. “IES hand calculation with inner surface temperature” show the hand calculation values when instead of operative room temperature, the inner surface temperature is considered in the equation. The assumed equation for IES VE with room operative temperature (“IES hand calculation”) seemed to be close to the results given by IES VE (“IES monitored value from software”).

Figure 13: Comparison between heat transfer from monitored value and hand calculated value.

00:00 12:00 00:00 12:00 00:00 12:00


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IES hand calculation with operative room temperatureIES monitored value from softwareIES hand calculation with inner surface temperature


30

3.4 Phase 3 – Annual heating and cooling need

The results showing the impact of each construction on heating and cooling need for both programs are presented in Figure 14. The diagram shows that the differences between software increase as the complexity of the constructions increases. Indeed, the differences in cases #4 and #5 were higher than the other, respectively 15% and 11%. The relative difference in the total energy needs between the two software tools was examined, as can be seen on the upper part of Figure 14 with the percentage of difference.

Figure 14: Energy need comparison between IDA ICE and IES VE

37.7 36.5 37.7 36.5 37.7 36.5

15.4 15.2 12.8 11.9 8.4 6.8 8.5 7.0 13.1

43.5 41.2

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CASE#1 CASE#2 CASE#3 CASE#4 CASE#5 DSF

Ener

gy n

eed

per f

loor

are

a / (

kWh/

m²)

Heating (kWh/m²) Cooling (kWh/m²)


31

Even though an overall increase of the difference was noticed, as the construction had gradually lower U-value and g-value (cases #1 to #5), further investigation was carried out to examine the results in cases #4 and #5. Subcase#C with venetian absorptive blind was examined for both cases, as the effect of solar radiation was more obvious. Figure 15 illustrates the difference between heat transfer for IDA ICE and IES VE. The latter seemed to have a very similar performance between cases #4 and #5, while IDA ICE differed significantly. As noticed in Phase 2, the heat transfer in IDA ICE was affected by the solar gains, while in IES VE did not.

Figure 15: Heat transfer for case #4 and 5 in IDA ICE and IES VE

To highlight the impact, that the consideration of solar radiation could have in the energy calculations (difference noticed between IDA ICE and IES VE), Figure 16, presented below, illustrates the difference between the thermal balance (solar gains - heat transfer) in IDA ICE and IES VE for three days related to Subcase C. It was shown that the difference between the results was taking place during the day, when there were solar gains. Case #4 was always higher than case #5, explaining the relative difference between those cases shown in Figure 14 (15% and 11% respectively).

Figure 16: Absolute difference between thermal balance (solar gain - heat transfer) in IDA ICE and IES VE

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CASE #4 (IDA ICE - IES VE) CASE #5 (IDA ICE - IES VE)


32

It was clearly shown that for the two cases (#4 and #5), where the U-value was almost the same, while the g-value had a difference ratio of approximately 1:2, the significance of solar transmittance grew a lot. Specifically, in case #4 where the g-value was almost double the relative difference in energy need between IDA ICE and IES VE was significantly higher (15% instead of 11%). As a result:

Cooling energy needs had bigger difference between IDA ICE and IES VE, as the two programs had different U-value during daytime, but the same during night. During daytime there was solar radiation and cooling was necessary to compensate the heat gains.

Heating energy needs were not significantly affected by the different thermal transmittance observed in Phase 2.

Figure 17 presents the results for case#1 when the time step was set to 0.01h for the inputs. This shows that the difference of heating energy needs could be neglected (For further details, refer to chapter: Weather data interpolation).

Figure 17: Energy need comparison between IDA ICE and IES VE (only case#1 with 0.01h time step)

The remaining difference in decimals between the results, was due to the way the two programs output the results. In details, the amount of values produced in IDA ICE and IES VE was not the same, due to the available time step selected. Minor differences in decimals were noticed, due to averaging the outputs.

The minor differences in the absolute values shown in Figure 14 andFigure 17, were not examined in great detail, as the relative difference was the main objective. Since the method followed for all the cases was the same, the absolute differences were negligible.

37.7 37.0 37.7 37.0 37.7 37.1

43.4 41.1 61.5 59.3

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020406080

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lect

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CASE#1

Ener

gy n

eed

per f

loor

are

a / (

kWh/

m²)

Heating (kWh/m²) Cooling (kWh/m²)


33

4 Discussion

This thesis examined part of the existing policy framework in the field of solar and thermal performance of windows. The outcomes of this study shaped the later simulation work, which aimed to highlight differences in software tools (IDA ICE and IES VE).

Policy framework

U-value and g-value indices, which are commonly used as criteria of window performance in the regulations and certifications, describe the performance of windows under predefined, steady-state conditions. Even though this serves adequately the comparison of different constructions under “lab conditions”, when it comes to monitoring the performance in real, transient conditions, the indices might not be sufficient and the results may differ from reality. Indeed, in ISO 15099, the assessment of solar and thermal performance of window constructions, including the dynamic behavior that window systems could have, is described in detail. The absorption of solar radiation, as well as, the rise of temperature on all the layers of the construction and the comprehensive analysis of shading are some of them.

Even though, ISO 15099 specify that the calculation process with shading devices may differ from the reality, the standard proposes a methodology for integrating the dynamic effect of the shading while the regulations and certifications do not consider it. Specifically, to comply with UK regulation (Part L), the energy consumption of office buildings needs to be simulated with specific, validated software such as SBEM to pass the criteria of the regulation and be certified. However, SBEM is a simplified tool for energy simulation and only considers steady-state U-value and g-value for the assessment of the performance of the windows.

Regarding Miljöbyggnad, the certification evaluates the performance of a building in terms of the total energy demand for the worst-case scenario. This certification could allow for the dynamic effect of the window systems, as the requirement is based on the total energy demand. However, this certification does not explicitly state that the energy demand should be calculated including the solar radiation and its impact on heat transfer. The results can be delivered even if solar radiation is excluded when U-value alone is considered (U-value, exclude solar radiation by definition).

The extended use of steady-state indices, such as U-value and g-value, by the regulations and the certifications seems to neglect the detailed process for calculating the solar and thermal performance of window systems. As shown in this study, the impact of this simplified way could distort significantly the performance of such constructions, affecting, consequently, the overall performance of the building (Figure 14), as well as its evaluation.

Software IDA ICE & IES VE

Phase 1 (steady-state calculation) presented no major differences between the software WIS 3, IDA ICE and IES VE (Figure 4 and Figure 5), as the steady-state U-value was well defined in the policy framework. Phase 2 (Dynamic thermal modelling), however, pointed out a divergence when dynamic simulation was applied to the same construction (Figure 8) as in Phase 1 in IDA ICE and IES VE. Phase 3 (Annual heating and cooling need) showed the impact of this divergence in terms of heating and cooling needs. The difference was noticed to be bigger as for the thermal transmittance and inner surface temperature, especially for the more complex window constructions. Smaller variations were also noticed due to the inputs.


34

o Thermal transmittance

The study of the DTM tools showed that the thermal transmittance was calculated in a different way in IDA ICE and IES VE. The former first calculated the U-value before the dynamic simulation, however, for the dynamic thermal simulations it used a thermal transmittance which considered solar radiation. Figure 11 showed the thermal transmittance considering the solar gains. It was clear that the U-value varied due to the rising temperature from the solar radiation. The solar radiation influenced the temperature of the panes and increased the difference of temperature between the inner and outer surfaces. Therefore, the heat transfer which considered, as explained in Equation 1, varied when solar radiation passed through the window. This was evident in every graph in Phase 2 and especially for the absorptive venetian blind as the heat was trapped in the window.

Regarding IES VE, a different pattern was observed. The thermal transmittance was the same for both, with or without applied blinds. This could be seen in every graph in Phase 2. The steady-state U-value used in Equation 2 of the thermal transmittance and the difference of temperature between the operative room temperature and the outside temperature seemed to give similar results regarding the thermal transmittance. Figure 13 presented the results from the hand calculation in comparison to the given output from the program. The assumptions made in Equation 2 seemed to lead to the conclusion that IES VE considered the thermal transmittance for windows as for an opaque construction. The thermal transmittance did not change when there was solar radiation. However, this was simply a reasoned assumption based on the results, as the software code was proprietary, and the underlying equations were not provided.

To conclude, the results suggested that: ∙ IDA ICE considered the heat transfer for the fenestration system alone (from inner to

outer pane, excluding the external and internal convective heat transfer coefficients) with a thermal transmittance that changed due to solar radiation.

∙ IES VE calculated the heat transfer for the whole zone (between indoors and outdoors including the external and internal convective heat transfer coefficients) with a steady-state U-value.

o Inner surface temperature

Solar radiation and heat transfer both affected the inner surface temperature. In Figure 8 when the absorptive blind was drawn down, the heat transfer was higher at noon in IDA ICE than in IES VE. The inner surface temperature consequently was lower in IDA ICE than in IES VE because more heat was transferred. The inner surface temperature, therefore, was smaller than in IES VE. The difference of the inner surface temperature between IDA ICE and IES VE could be further investigated if the developer of IES VE provided the equations used.

o Solar gains The solar gains in IDA ICE and IES VE were similar, no major deviations were noticed in this analysis in the results of Phase 2.


35

Phase 3

Figure 14 presented the annual heating and cooling energy needs, only due to heat gains and losses through the window, without ventilation and only using ideal air conditioning, for the five glazing cases with IDA ICE and IES VE. For the same input parameters, in IDA ICE and IES VE, cases #1 to #2 performed almost similarly (IDA ICE was always lower than IES VE approximately 5% in total energy need). The heat transfer when the absorptive blind was drawn, was higher in IDA ICE because of solar radiation. The solar radiation increased the temperature of the shading devices and as the heat transfer was impacted by the solar radiation, the heat transfer also increased in IDA ICE. In IES VE as explained, the heat transfer did not seem to vary as much as in IDA ICE and therefore, when absorptive blind was added, the heat transfer was steady and more cooling energy was needed as less heat transferred outdoors. To notice, in IDA ICE and IES VE, the U-value is the same during night time (without solar radiation). As a result, heating energy needs are not that much affected as cooling energy needs. More heat is lost in IDA ICE than in IES VE, when there is solar radiation (higher thermal transmittance). Moreover, the difference observed between IDA ICE and IES VE for the heating energy needs is due to the time step used in IDA ICE (Interpolation of weather data, Figure 17). Cases #3 and #5, due to low g-value, resulted in “similar” difference for the heating and cooling energy needs, while CASE#4 had a higher g-value, and the difference in the heat transfer between IDA ICE and IES VE was important as shown in the Figure 15. Phase 3 in comparison with Phase 1 shows that CASE#3 without blind did not perform as good as CASE#5 (higher U-values and g-values) but in Phase 3, CASE#3 and #5 performed similarly. The shading device was drawn when solar radiation was high which avoid overheating and lower the cooling needs for CASE#3. It is worthwhile noting that the relative difference in the total energy need calculated between IDA ICE and IES VE seemed to increase as the complexity of the constructions increased. The percentage of difference almost doubled for cases #3 to #5, showing that when thermal transmittance was low the impact of solar radiation increased significantly, resulting in higher difference in energy calculations between the two programs.


36

5 Conclusion

The assessment of solar and thermal performance of windows is heavily dependent on the assumptions made by policies and/or simulation software. Different regulations and certifications evaluate their performance and validate the results in different ways. Software tools might be developed according to those policy frameworks, which could result in even bigger divergence. This, most probably, leads to differences between calculated and actual solar and thermal performance of new buildings, which has been highlighted in this study.

The study showed that using steady-state indices to assess the performance of windows is insufficient. Indeed, this way the dynamic effect of solar radiation is excluded, which is of primary importance in glazed constructions. ISO 15099 seems to be the most detailed reference standard when it comes to this subject, but other simplified standards are also used in the regulations and certifications which could increase the possibility of having inconsistent results.

Considering the total annual energy need, seemed to account for all the phenomena taking place in such constructions. Nonetheless, even this way of assessing the performance was shown that might result in inaccuracies, if the DTM tools used do not consider for the dynamic behavior of the construction (consideration of solar radiation).

To enhance the current assessment of solar and thermal performance of window, few proposals were suggested:

- Regulations and certifications already provide a compliance methodology (NCM for part L) however, a guideline for modelling this complicated type of construction could be handy for all approved software tools. For example, guidelines could refer to the maximum acceptable simplifications used during the modelling process (shading devices could be modelled as panes) or the assumptions regarding the characteristic properties of the materials used (gas types). In this way consistency, as well as, validation of the whole process could be further ensured.

- Regulations and certifications should adjust their validation tools (e.g. SBEM) to integrate the dynamic effect of solar radiation for instance in the energy calculation for this typology of buildings.

- Dynamic calculation should be considered as the standard way to comply with the regulations instead of steady state for glazed constructions.

The study investigated only a limited set of constructions, policies and software. Further analysis extending the material examined would be necessary in order to form a holistic approach to the problem. The collaboration with the software developers and practitioners, as well as, the use of mock-ups or real-world measurements would be strongly recommended.


37

6 References

BRE Global Ltd., 2017. BREEAM International New Construction 2016. BRE Group, 2017. SBEM: Simplified Building Energy Model. CEN - European Committee for Standardization, 2005. EN 13363-2 Solar protection

devices combined with glazing - Calculation of total solar energy transmittance and light transmittance - Part 2: Detailed calculation method.

CEN - European Committee for Standardization, 2011. EN 410 - Glass in building determination of luminous and solar characteristics of glazing.

CEN - European Committee for Standardization, 2011. EN 673 - Glass in building determination of thermal transmittance (U value) - Calculation method.

CEN - European Committee for Standardization, 2016. ISO 15099 - Thermal performance of windows, doors and shading devices - Detailed calculations.

Dick (H.A.L.) van Dijk and Henk (H.P.) Oversloot, 2003. WIS detail calculation. EQUA, 2018. IDA ICE - Simulation Software [Online]

Available at: https://www.equa.se/en/ [Accessed 04 02 18].

Greenspec, 2018. Windows: Heat loss & Heat gain. [Online] Available at: http://www.greenspec.co.uk/building-design/windows/ [Accessed 10 01 2018].

HM Government, 2016. The Building Regulations 2010, Approved Document L2A: Conservation of fuel and power in new buildings other than dwellings, 2013 edition incorporating 2016 amendments.

Kalyanova, O., Heiselberg, P., Felsmann, C., Poirazis, H., Strachan, P., Wijsman, A., 2009. An empirical validation of building simulation software for modelling of double-skin facade (dsf) 9.

Sahlin, P., 2018. IDA ICE 4.8 - Getting Started 75. STDEV.P function - Office Support [Online]

Available at: https://support.office.com/en-us [Accessed 04 11 18].

Sweden Green Building Council, 2018. Miljöbyggnad 3.0. Department for Communities and Local Government (DCLG), 2016, UK National

Calculation Methodology. winDat Thematic Network, 2006. WIS - Window Information System [Online]

Available at: http://www.windat.org/ [Accessed 04 02 18].


38

Appendix A

The detailed characteristics of all the components or layers were:

Table 4: Detailed characteristics of all the layers

Panes 6mm clear pane 6mm clear pane with

Low-E (coating on the exterior side)

6mm clear pane with Solar Control (coating on the interior side)

Solar reflectance outdoors / 1

0.073 0.285 0.366

Solar reflectance indoors / 1

0.073 0.226 0.464

Solar transmittance / 1 0.810 0.597 0.302 Visual reflectance outdoors / 1

0.079 0.046 0.093

Visual reflectance indoors / 1

0.079 0.058 0.055

Visual transmittance / 1 0.888 0.874 0.654 Corrected infrared emissivity outdoors / 1

0.840 0.038 0.837

Corrected infrared emissivity indoors / 1

0.840 0.840 0.012

Properties of shading devices

Slat venetian blinds (reflective)

Slat venetian blinds (absorptive)

Roller blind

Thickness / mm 0.2 0.2 1.0 Slat chord width / mm 13 13 - Crown height / mm 0 0 - Slat pitch / mm 12 12 - Slat angle / º 85 85 - Conductivity / [W/(mꞏK)]

100 100 20

Solar reflectance outdoors / 1

0.71 0.36 0.83

Solar reflectance indoors / 1

0.71 0.36 0.83

Solar transmittance / 1 0.00 0.00 0.06 Visual reflectance outdoors / 1

0.79 0.43 0.82

Visual reflectance indoors / 1

0.79 0.43 0.82

Visual transmittance / 1 0.00 0.00 0.06 Corrected infrared emissivity outdoors / 1

0.8 0.8 0.6

Corrected infrared emissivity indoors / 1

0.8 0.8 0.6


39

Appendix B

Table 5: Inputs used for DTM tools (IDA ICE & IES VE)

DESCRIPTION UNIT IDA ICE IES VE WEATHER FILE

Location - London (GBR - Gatwick Airport) Climate file - ASHRAE IWEC 2

THERMAL PROPERTIES EXCEPT WINDOW1 Walls/roof/floor

U-value W/(m²ꞏK) 0.0005 (quasi-adiabatic) Thickness m 2.0 Material properties

Thermal conductivity W/(mꞏK) 0.001 Density kg/m³ 550 Specific heat capacity J/(kgꞏK) 10

Window frame U-value Frame W/(m²ꞏK) 0.01 No frame Size Frame % 0.1 No frame

Thermal bridge W/K/lm 0 0 Inflitration rate (fixed) ACH 0 0

THERMAL COMFORT Heating setpoint °C 21.0 Cooling setpoint °C 21.1 Type of heater Ideal air conditioning Type of cooler Ideal air conditioning Power cooler W 10 000 Power heater W 10 000 Ventilation ‐ No ventilation

INTERNAL GAINS Lighting W/m² 0 Occupancy u/m² 0 Equipment W 0 Internal mass - 0

MISCELLANEOUS Ground reflectance - 0.2

All other inputs that are not listed in the table or explained in the section: “Other specific inputs”, were not affecting the output and set by default in IDA ICE and IES VE.

1 The thermal properties of window are described in the section “Description of the cases”


40

Appendix C

The gas properties used in IDA ICE for the DTM simulation are shown below. The gas properties are the same as the one used in ISO 15099. In IES VE the gas properties were added differently. Table 6: Gas properties

Air -10 ºC 0 ºC 10 ºC 20 ºC

Conduction / W/(mꞏK) 0.023 0.024 0.025 0.026 Dynamic Viscosity / kg/(mꞏs) 1.67ꞏ10-05 1.72ꞏ10-05 1.77ꞏ10-05 1.82ꞏ10-05 Density / (kg/m3) 1.205 1.205 1.205 1.205 Cp / J/(kgꞏK) 1005.984 1006.108 1006.231 1006.355 Argon

-10 ºC 0 ºC 10 ºC 20 ºC

Conduction / W/(mꞏK) 0.016 0.016 0.017 0.017 Dynamic Viscosity/ kg/(mꞏs) 2.04ꞏ10-05 2.10ꞏ10-05 2.17ꞏ10-05 2.229ꞏ10-05 Density / (kg/m3) 1.661 1.661 1.661 1.661 Cp / J/(kgꞏK) 521.929 521.929 521.929 521.929


41

Appendix D

This appendix presents in detail the equation used for the data analysis in Phase 2 (average value, noted MEAN and Correlation coefficient noted CORREL). The entire set of data analyzed for every subcases are presented in the next pages.

Data analysis o Equations

Equations to calculate the average and the correlation coefficient in the data series were used. The average is the sum of the values divided by the number (quantity) of values. The correlation coefficient is used to determine the statistical relationship between two variables. It was used to compare the values between IDA ICE and IES VE.

Correl(X,Y)=∑ (x-x)(y-y)

∑ (x-x)² ∑ (y-y )² [%] (3)

Where, n is the number of values from the series. x and y are the values of the series. x and y are the mean value for the series. The sparkline represents the shape of the output per day analyzed. The % MEAN presents the difference of average value between the two software. When the % MEAN is close to 100% the output from IDA ICE and IES VE does not differ.


42

Data analyzed for every cases

Table 7: Evaluation of results case#1-no.blind

Table 8: Evaluation of results case#1.blind#1.reflective

IDA IES

IDA IES MEAN MEAN

Subcase#A W ↑ BEAM 24‐Apr °C 18.19 17.94 98.6% 101.4%

Subcase#B W ↑ DIFFUSE 23‐Jul °C 22.00 22.53 99.0% 97.6%

Subcase#C S ↑ BEAM 24‐Apr °C 18.26 18.15 99.1% 100.6%

Subcase#D S ↑ DIFFUSE 23‐Jul °C 21.79 22.31 98.7% 97.7%

Subcase#E S ↑ TEMP. 30‐juin °C 22.18 22.65 98.8% 97.9%

Subcase#F S ↓TEMP. 07‐déc °C 15.94 16.01 96.9% 99.5%

IDA IES

IDA IES MEAN MEAN

Subcase#A W ↑ BEAM 24‐Apr W/m² 84.75 86.41 99.93% 98.1%

Subcase#B W ↑ DIFFUSE 23‐Jul W/m² 84.79 88.20 99.94% 96.1%

Subcase#C S ↑ BEAM 24‐Apr W/m² 88.97 91.86 99.87% 96.8%

Subcase#D S ↑ DIFFUSE 23‐Jul W/m² 71.80 74.93 99.87% 95.8%

Subcase#E S ↑ TEMP. 30‐juin W/m² 82.98 86.04 99.93% 96.4%

Subcase#F S ↓TEMP. 07‐déc W/m² 25.23 26.40 99.88% 95.6%

IDA IES

IDA IES MEAN MEAN

Subcase#A W ↑ BEAM 24‐Apr W/m² 41.40 ‐ 40.73 ‐ 95.0% 101.6%

Subcase#B W ↑ DIFFUSE 23‐Jul W/m² 10.27 ‐ 7.97 ‐ 98.6% 128.8%

Subcase#C S ↑ BEAM 24‐Apr W/m² 42.06 ‐ 40.89 ‐ 95.2% 102.9%

Subcase#D S ↑ DIFFUSE 23‐Jul W/m² 9.63 ‐ 7.55 ‐ 99.0% 127.6%

Subcase#E S ↑ TEMP. 30‐juin W/m² 9.09 ‐ 7.62 ‐ 99.3% 119.2%

Subcase#F S ↓TEMP. 07‐déc W/m² 44.97 ‐ 42.20 ‐ 99.0% 106.6%

INNER GLASS TEMPERATURE

% MEAN

% MEANDATEOR. DETAIL

UNITOR. DETAIL DATESUBCASES

SUBCASES

% MEAN

SPARKLINE 24 HOURSCORREL

SOLAR GAIN

HEAT TRANSMISSION

SUBCASES OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS

SPARKLINE 24 HOURSDATE

IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN








% MEAN



SUBCASES

% MEAN


SOLAR GAIN

HEAT TRANSMISSION

SUBCASES OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



43

Table 9: Evaluation of results case#1.blind#2.absorptive

Table 10: Evaluation of results case#1.blind#3.roller-shade

IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN

Subcase#A W ↑ BEAM 24‐Apr W/m² 35.33 ‐ 26.46 ‐ ‐7.9% 133.5%

Subcase#B W ↑ DIFFUSE 23‐Jul W/m² 13.91 ‐ 4.66 ‐ ‐8.0% 298.4%

Subcase#C S ↑ BEAM 24‐Apr W/m² 36.13 ‐ 26.51 ‐ ‐15.1% 136.3%





% MEAN



SUBCASES

% MEAN


SOLAR GAIN

HEAT TRANSMISSION

SUBCASES OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS


IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN








% MEAN



SUBCASES

% MEAN


SOLAR GAIN

HEAT TRANSMISSION

SUBCASES OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



44



IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN








% MEAN



SUBCASES

% MEAN


SOLAR GAIN

HEAT TRANSMISSION

SUBCASES OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS


IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN








% MEAN



SUBCASES

% MEAN


SOLAR GAIN

HEAT TRANSMISSION

SUBCASES OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



45



IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN

Subcase#A W ↑ BEAM 24‐Apr W/m² 18.97 ‐ 13.84 ‐ ‐1.2% 137.0%


Subcase#C S ↑ BEAM 24‐Apr W/m² 19.25 ‐ 13.87 ‐ ‐12.9% 138.8%





% MEAN



SUBCASES

% MEAN


SOLAR GAIN

HEAT TRANSMISSION

SUBCASES OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS


IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN








% MEAN



SUBCASES

% MEAN


SOLAR GAIN

HEAT TRANSMISSION

SUBCASES OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



46

Table 15:Evaluation of results case#3-no.blind


IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS

SPARKLINE 24 HOURSDATE % MEAN


SOLAR GAIN

HEAT TRANSMISSION

SUBCASES


% MEAN



SUBCASES

IDA IES

IDA IES MEAN MEAN

CASE#A W ↑ BEAM 24‐Apr °C 20.60 20.46 89.5% 100.7%

CASE#B W ↑ DIFFUSE 23‐Jul °C 21.90 21.92 91.6% 99.9%

CASE#C S ↑ BEAM 24‐Apr °C 20.62 20.55 93.4% 100.3%

CASE#D S ↑ DIFFUSE 23‐Jul °C 21.70 21.79 92.3% 99.6%

CASE#E S ↑ TEMP. 30‐juin °C 21.90 21.94 92.8% 99.8%

CASE#F S ↓TEMP. 07‐déc °C 19.43 19.57 83.3% 99.2%

IDA IES

IDA IES MEAN MEAN

CASE#A W ↑ BEAM 24‐Apr W/m² 10.43 8.52 99.57% 122.4%

CASE#B W ↑ DIFFUSE 23‐Jul W/m² 10.38 9.54 99.75% 108.7%

CASE#C S ↑ BEAM 24‐Apr W/m² 11.06 9.07 99.67% 121.9%

CASE#D S ↑ DIFFUSE 23‐Jul W/m² 8.73 8.25 99.71% 105.8%

CASE#E S ↑ TEMP. 30‐juin W/m² 10.14 9.29 99.42% 109.2%

CASE#F S ↓TEMP. 07‐déc W/m² 3.00 3.11 98.65% 96.7%

IDA IES

IDA IES MEAN MEAN

CASE#A W ↑ BEAM 24‐Apr W/m² 9.12 ‐ 11.46 ‐ 84.6% 79.6%

CASE#B W ↑ DIFFUSE 23‐Jul W/m² 0.40 1.80 ‐ 92.7% ‐22.2%

CASE#C S ↑ BEAM 24‐Apr W/m² 9.18 ‐ 11.46 ‐ 85.9% 80.1%

CASE#D S ↑ DIFFUSE 23‐Jul W/m² 0.08 1.79 ‐ 93.4% ‐4.4%

CASE#E S ↑ TEMP. 30‐juin W/m² 0.69 1.65 ‐ 92.3% ‐41.8%

CASE#F S ↓TEMP. 07‐déc W/m² 12.76 ‐ 12.99 ‐ 91.2% 98.2%


% MEAN


UNITOR. DETAIL DATECASES

CASES

% MEAN


SOLAR GAIN

HEAT TRANSMISSION

CASES OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



47



IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN


Subcase#B W ↑ DIFFUSE 23‐Jul W/m² 1.58 1.84 ‐ 88.7% ‐85.9%


Subcase#D S ↑ DIFFUSE 23‐Jul W/m² 1.08 1.82 ‐ 89.7% ‐59.4%

Subcase#E S ↑ TEMP. 30‐juin W/m² 1.87 1.70 ‐ 88.3% ‐110.0%


OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



SOLAR GAIN

HEAT TRANSMISSION

SUBCASES


% MEAN



SUBCASES

IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



SOLAR GAIN

HEAT TRANSMISSION

SUBCASES


% MEAN



SUBCASES


48



IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



SOLAR GAIN

HEAT TRANSMISSION

SUBCASES


% MEAN



SUBCASES

IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN








% MEAN



SUBCASES

% MEAN


SOLAR GAIN

HEAT TRANSMISSION

SUBCASES OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



49



IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



SOLAR GAIN

HEAT TRANSMISSION

SUBCASES


% MEAN



SUBCASES

IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



SOLAR GAIN

HEAT TRANSMISSION

SUBCASES


% MEAN



SUBCASES


50



IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



SOLAR GAIN

HEAT TRANSMISSION

SUBCASES


% MEAN



SUBCASES

IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN



Subcase#C S ↑ BEAM 24‐Apr W/m² 1.71 7.79 ‐ 82.1% ‐22.0%

Subcase#D S ↑ DIFFUSE 23‐Jul W/m² 3.63 1.26 ‐ 93.5% ‐287.1%

Subcase#E S ↑ TEMP. 30‐juin W/m² 3.77 1.17 ‐ 91.3% ‐320.7%

Subcase#F S ↓TEMP. 07‐déc W/m² 0.77 8.83 ‐ 82.7% ‐8.7%

OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



SOLAR GAIN

HEAT TRANSMISSION

SUBCASES


% MEAN



SUBCASES


51



IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN








% MEAN



SUBCASES

% MEAN


SOLAR GAIN

HEAT TRANSMISSION

SUBCASES OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS


IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN







IDA IES

IDA IES MEAN MEAN








% MEAN



SUBCASES

% MEAN


SOLAR GAIN

HEAT TRANSMISSION

SUBCASES OR. DETAIL

CORREL

CORREL

UNIT

UNIT

SPARKLINE 24 HOURS



52

Appendix E

Appendix E presents the numerical value (U-value and g-value) from WIS 3, IDA ICE and IES VE. Table 27: Steady state results from IDA ICE and IES VE

U‐value / W/(m²∙K) g‐value

WIS 3 IDA ICE IES VE WIS 3 IDA ICE IES VE

1. D

GU clear

No shading 2.58 2.61 2.58 0.75 0.72 0.74

VB ‐ reflective 1.73 1.711 1.7259 0.316 0.293 0.2827

VB ‐ absorptive 1.73 1.711 1.7259 0.451 0.416 0.4301

Roller blind 1.55 1.562 1.6306 0.278 0.206 0.194

2. D

GU LowE No shading 1.11 1.199 1.0743 0.599 0.577 0.59

VB ‐ reflective 0.89 0.939 0.8908 0.338 0.324 0.3124

VB ‐ absorptive 0.89 0.939 0.8908 0.439 0.418 0.4216

Roller blind 0.834 0.886 0.8647 0.325 0.254 0.2394

3. C

losed

cavity

window No shading 0.85 0.912 0.8909 0.535 0.487 0.511

VB ‐ reflective 0.73 0.757 0.7574 0.0908 0.087 0.0769

VB ‐ absorptive 0.73 0.757 0.7574 0.131 0.129 0.111

Roller blind 0.688 0.714 0.7204 0.0829 0.073 0.068

4. TGU 2

LowE

No shading 0.562 0.59 0.5932 0.471 0.414 0.421

VB ‐ reflective 0.509 0.525 0.5326 0.29 0.28 0.2755

VB ‐ absorptive 0.509 0.525 0.5326 0.347 0.331 0.3391

Roller blind 0.493 0.509 0.5232 0.286 0.237 0.2296

5. TGU 1

LowE + 1 SC

No shading 0.534 0.564 0.566 0.266 0.242 0.226

VB ‐ reflective 0.545 0.504 0.5102 0.18 0.173 0.1618

VB ‐ absorptive 0.545 0.504 0.5102 0.206 0.198 0.1921

Roller blind 0.526 0.489 0.5016 0.179 0.151 0.1387

ASSESSMENT OF WINDOW SYSTEMS CONSIDERING SOLAR AND … · 2018-08-30 · IES VE Integrated...

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