Master Level Thesis Geographical Mapping of the Building Envelope

Home , Baku, Flying Fish Cove, Hargeisa, Malabo, Mbabane, Port Louis

Master Level Thesis European Solar Engineering School No. 273, Sept. 2017

Geographical Mapping of the Building Envelope Surface Optimal Optical Properties Minimizing the Energy used to Maintain Indoor Conditions

Master thesis 30 credits, 2017 Solar Energy Engineering Author: Alejandro Rodríguez-Urdaneta Supervisors: Chris Bales Harald Svedung Dalarna University Examiner: Ewa Wäckelgård Energy and Course Code: MÖ3032 Environmental Examination date: 2017-09-14 Technology

i Abstract

Several studies have shown that the buildings envelope optical properties are important in terms of energy use and thermal comfort level. However, no study has been found in regard of the optimal optical properties for the building envelope. Moreover, developments in the coil-coating industry have made possible to design cost effective optical selective surfaces for the construction sector. Based on the above mentioned, this study pretends to map the envelope optimal optical properties minimizing the energy use for large-open-volume buildings locates in Stockholm, Copenhagen, Liverpool, Amsterdam, Berlin, Vienna, Bern, Rome, and Madrid.

A building could be seen as a very complex solar energy conversion system, which is very difficult to describe accurately. Nonetheless, it is possible to use Building Energy Simulation (BES) tools to model, to some extent, its thermal performance under many simplistic assumptions. The simulation tool TRNSYS 17 and the optimization tool GenOpt were selected for this study. Additionally, detailed small- open-volume building thermal performance data, obtained during passive measurements from the steel manufacturer SSAB, in Borlänge-Sweden, were used to assess the methodology for the creation of the large-open-volume simulation models. The variations in large-open-volume building design around Europe are not well documented, which constitutes one of the major impediments for this research. However, detailed European historical building U-value data from the European Union project called iNSPiRe made it possible to achieve the objective of this study.

The simulation work showed, that the building envelope optimal optical properties are related to the magnitude of the heating and cooling loads. Consequently, GenOpt was used to plot the sensitivity of the building envelope optimal optical properties to the ratio between the heating demand and the total energy demand (Qheat/Qtotal). In regard to the large-open-volume building optimal optical properties in the selected locations, it was found that the allocation of optimal optical properties does not lead to significant energy savings in locations with relatively low solar availability and high thermal insulation levels. Nonetheless, a final envelope optical properties study for a small-open-volume building model based on three existing buildings differing only on their optical properties was made for 243 world-capital cities. The simulations reinforced the results for the large-open-volume building in the European locations, and additionally showed huge energy savings potential for most of the world capital cities. This investigation restates the results obtained by Joudi (2015), “Possible energy savings by the smart choice of optical properties on the interior and exterior surfaces of the building.”

Keywords: buildings, energy efficiency, energy savings, energy simulations.

ii Síntesis (Abstract)

Varios estudios han demostrado que las propiedades ópticas de las envolturas de los edificios son importantes en términos de consumo energético y de nivel de confort térmico. Sin embargo, no se ha encontrado estudio alguno con respecto a las propiedades ópticas óptimas para las envolturas de los edificios. Conjuntamente, los desarrollos en la industria de bobinas metálicas revestidas han hecho posible diseñar superficies selectivas rentables para el sector de la construcción. Basándose en lo anteriormente expuesto, este estudio pretende mapear las propiedades ópticas óptimas de la envoltura que minimizan el uso de energía para edificios de gran volumen abierto localizados en Estocolmo, Copenhague, Liverpool, Ámsterdam, Berlín, Viena, Berna, Roma y Madrid.

Un edificio podría ser visto como un sistema de conversión de energía solar muy complejo, que es muy difícil de describir con precisión. No obstante, es posible utilizar las herramientas de Simulación de Energía de Edificios (BES) para modelar, hasta cierto punto, su rendimiento térmico bajo una considerable cantidad de suposiciones simplistas. El programa de simulación TRNSYS 17 y el programa de optimización GenOpt fueron seleccionadas para este estudio. Adicionalmente, con el fin de evaluar la metodología utilizada para la creación de los modelos de simulación para edificios de gran volumen abierto, se utilizaron datos detallados de rendimiento térmico de edificios de pequeño volumen abierto, obtenidos durante mediciones pasivas del fabricante de acero SSAB, en Borlänge-Suecia. Las variaciones en el diseño de edificios de gran volumen abierto en toda Europa no están bien documentadas, lo que constituye uno de los principales impedimentos para esta investigación. Sin embargo, los datos detallados y en orden cronológico de los niveles de aislamiento térmico (U-value) en la construcción europea, recopilados por el proyecto de la Unión Europea llamado iNSPiRe, permitieron alcanzar el objetivo de este estudio.

El trabajo de simulación demostró que las propiedades ópticas óptimas de la envoltura del edificio están relacionadas con la magnitud de las cargas de calefacción y refrigeración. En consecuencia, GenOpt fue utilizado para graficar la sensibilidad de las propiedades ópticas óptimas de la envolvente del edificio con respecto a la proporción entre la demanda de calefacción y la demanda total de energía (Qheat/Qtotal). En cuanto a las propiedades ópticas óptimas del edificio de gran volumen abierto en las ubicaciones seleccionadas, se encontró que la asignación de propiedades ópticas óptimas no conduce a ahorros de energía significativos en ubicaciones con disponibilidad solar relativamente baja y altos niveles de aislamiento térmico. Sin embargo, un último estudio de propiedades ópticas de envolvente para un modelo de construcción de pequeño volumen abierto basado en tres edificios existentes que difieren sólo en sus propiedades ópticas se realizó para 243 capitales mundiales. Las simulaciones reforzaron los resultados para el edificio de gran volumen abierto en las localidades europeas, y además mostraron un enorme potencial de ahorro de energía para la mayoría de las capitales mundiales. Esta investigación reitera los resultados obtenidos por Joudi

iii (2015), "Posibles ahorros de energía por la elección inteligente de propiedades ópticas en las superficies interiores y exteriores del edificio".

Palabras clave: edificios, eficiencia energética, ahorro de energía, simulaciones energéticas.

iv Acknowledgment

In first instance, I would like to be grateful to God for giving me the opportunity to expand my knowledge.

I would like to sincerely thank all the people, who have donated their time and energy to support me with the realization of this dissertation:

Thanks to my supervisors Harald Svedung and Chris Bales, who patiently guided me throughout my research project.

Special thanks to Mats Rönnelid for his many pieces of advice.

Thank you, Mohammad Ali Joudi and Marcus Gustafsson.

Muchísimas gracias a Anneline Parodi y a Zulay Rodríguez por haberme apoyado en mi lucha.

Stort tack till den fantastiska Larsson-Soto familjen.

And last but not least, especial thanks to all my current and former teachers, in particular, those from the mechanical engineering school at the University of Zulia.

This thesis is dedicated to:

To the memory of my mother, Lucía Urdaneta.

To the memory of: Elsa Urdaneta, Victor Urdaneta, Rafael Urdaneta, and Alberto Rodríguez.

To all my relatives and friends.

Sorry for the long physical absence! ‘Exitus acta probat’

vi Contents

Abstract ...... ii Síntesis (Abstract) ...... iii Acknowledgment ...... v Contents ...... vii Nomenclature ...... ix 1 Introduction ...... 1 Research motivation ...... 2 Aims ...... 2 Company profile ...... 3 Brief product description ...... 3 Selection of the studied locations ...... 4 Building type definition ...... 5 2 Theoretical Background ...... 7 Previous work ...... 7 Solar radiation ...... 8 Kirchhoff law of thermal radiation ...... 10 Total solar reflectance (TSR) and thermal emittance (εth) ...... 10 Optical properties of organic coated steel sheets ...... 11 Building applications ...... 14 Building design variations at the different locations ...... 14 Simulation based building design ...... 16 TRNSYS 17 ...... 17 Meteorological data ...... 18 2.10.1 Meteonorm 6.1 ...... 18 3 Methodology ...... 20 Research methodology ...... 20 Research methods ...... 21 Modelling ...... 22 3.3.1 Simulation tool testing ...... 22 3.3.2 Large-open-volume building thermal model ...... 26 3.3.3 Large-open-volume building model sensitivity analysis ...... 29 3.3.3.1 Infiltrations and ventilation ...... 30 3.3.3.2 Density driven flow ...... 30 3.3.3.3 Temperature setting ...... 36 3.3.3.4 Convection heat transfer coefficients ...... 37 3.3.3.5 Insulation level ...... 38 3.3.3.6 Ground temperature and ground surface temperature ...... 39 3.3.3.7 Effective sky temperature ...... 44 3.3.3.8 Weather file ...... 45 3.3.3.9 Building orientation ...... 47

vii 3.3.3.10 Indoor air temperature control strategy ...... 47 3.3.3.11 Stratification and internal longwave radiation ...... 48 Data collection ...... 50 Framework for data analysis ...... 51 Quality assurance ...... 51 Limitations and assumptions ...... 51 4 Results and Analysis ...... 54 5 Discussion and conclusions ...... 62 Further studies ...... 63 References...... 64 Appendix 1...... 68 Appendix 2...... 70

viii

Nomenclature

Acronyms

ACH Air changes per hour Bλ Black body spectral AHSS Advanced high strength distribution steel Is Solar spectral intensity AM Air Mass distribution ASHRE American Society of IEA International Energy Heating, Refrigerating Agency and Air Conditioning iNSPiRe Engineers KB Boltzmann’s constant BES Building energy KT Clearness index simulation tool nd Day number of the year c Speed of light NIR Near infrared CFD Computer fluid p Incident radiative power dynamics PV Photovoltaic ECCA European Coil Coating Q&T Quenched & tempered Association Qcool Cooling demand EU European Union Qheat Heating demand EURIMA European Insulation Qtotal Total energy demand Manufacturers S/V Surface area to Association enclosed volume ratio G Irradiance SSAB Swedish Steel AB GenOpt Generic Optimization T Temperature software TMY Typical Meteorological GHG Greenhouse gas Year h Plank’s constant TSR Total solar reflectance HVAC Heating ventilation and UV Ultra Violet air-conditioning VBA Visual Basic for I Irradiation Applications

Symbols

α Absorptance ρ Reflectance ε Emittance τ Transmittance θ Angle of incidence ϕ Latitude in degrees. θz Solar Zenith angle ωs Angular displacement of λ Wavelength the sun in degrees

Subscripts a air mr mean radiative diff diffuse n normal dir direct op operative dry dry bulb set setting ext exterior sw short-wave h horizontal th thermal int interior

1 Introduction

A more rational, innovative, efficient and sustainable use of solar energy in the building sector is mandatory to end with energy poverty. Solar passive technologies can help to achieve that goal. In fact, solar radiation is probably the most important meteorological variable affecting the building energy balance. Furthermore, the United Nations vision depicted in the 2030 agenda for sustainable development foresees a world where human habitats are safe, sustainable and resilient, and with universal access to affordable, reliable and sustainable energy (2015a, p.4). In addition to this, the world population is expected to increase about 30% for 2050 (United Nations, 2015b, p.2). All these indicate that the world energy consumption will significantly increase in the short coming future.

Nowadays, the energy consumed in residential and non-residential buildings account for 40% of the total energy consumption in Europe (European Commission, 2014, p.12). Globally the building sector accounts for 40% of the total energy, 25% of global water, 40% of global resources, and 1/3 of the greenhouse gas (GHG) emissions approximately (United Nations, 2015c). Under the above mentioned circumstances, building energy consumption is undoubtedly one of the concern focuses of our times.

In the last decades, technological innovations within the building sector have been driven by developments in the building simulation field. Under the right assumptions and the right simulation tools, it is possible to model within a reasonable confidence interval the thermal behavior of a building. In fact, new buildings today consume only half as much as typical buildings from the 1980s (European Commission, 2014, p.6).

To facilitate the introduction of solar technologies such as building surface materials with adapted optical properties for large-open-volume buildings into the European markets, it was necessary to map the envelope external optimal optical properties optimizing the conversion of solar radiation into thermal energy for the selected geographical locations and building type. For such endeavor, the simulation tools TRNSYS 17 and the optimization tool GenOpt were used taking as reference U-value data from the European project iNSPiRe.

Research motivation

Firstly, the author of this thesis thinks that buildings do influence or model the human behavior. In the light of that, it is thought that more comfortable and energy efficient buildings, in which the envelope elements (PV modules, solar collectors, selective paints, etc.) remind of energy efficiency and of renewable energy sources, would create energy consciousness among the inhabitants, thus improving the future of the generations to come in both developed and non- developed countries.

Secondly, most of the studies for non-residential buildings are based on small volume buildings such as office buildings, consequently, there is very little information about the energy performance of less common and larger types of buildings, such as: industrial buildings, warehouse halls, thermal power stations, halls for commercial operations, refrigerated premises, and sports halls. Moreover, until now, it has not been found data related to the envelope optical properties minimizing the energy use for the less common types of buildings mentioned above other than the data presented by Joudi et al. (2011), who studied the thermal performance of some sports halls. That represents a gap in the building energy performance field. Hence, this study pretends to take the research one step further using passive solar technologies, selective surface techniques, and state of the art building energy simulation tools, to estimate how the total annual energy used to maintain comfort levels in large-open-volume buildings vary in relation to the building envelope surface optical properties, for the selected. Such study would enhance knowledge about the management of solar radiation towards a reduction of the building energy demand, and the utilization of the coated steel sheets with modified optical properties as surface material for building elements in different climate zones.

Aims

Based on the hypothesis that ‘The optical properties values of a building envelope could be optimized to reduce the total energy consumption according to the geographical location’ the following aim and individual research objectives have been formulated:

Overall research aim

To map the envelope optimal optical properties minimizing the energy use in large- open-volume buildings for the selected geographical locations and building type.

Individual research objectives

• To study the variations in detailed design of large-open-volume buildings depending on building standards at the selected locations.

• To model the thermal behavior of large-open-volume buildings under hourly dynamic climate boundary conditions for the selected locations.

• To analyze how the total annual energy used to maintain indoor conditions in large-open-volume buildings vary with the surface optical properties of the building envelope for the selected locations.

Company profile

SSAB is a highly specialized, global steel company. The company is a leading producer on the global market for advanced high strength steels (AHSS) and Quenched & Tempered Steels, strip, plate and tubular products, as well as construction solutions. SSAB has production plants in Sweden, Finland and the Unites States of America with an annual steel production capacity of 8.8 million tons. (SSAB, 2015a)

SSAB is formed by five divisions:

• SSAB Special Steels – Global steel and service partner in value-added Advanced High Strength Steels (AHSS) and Quenched & Tempered steels (Q&T) • SSAB Europe – The leading Nordic-based steel producer of high quality strip, plate, and tubular products. As well as, coated products • SSAB Americas – Market-leading North American steel producer of high- quality heavy plate • Tibnor – The leading Nordic distributor of steel and non-ferrous metals • Ruukki Construction – European provider of energy-efficient building and construction solutions (SSAB, 2015a)

Brief product description

GreenCoat is a new innovative environmentally-adapted color coated steel product developed by SSAB and branded for building applications among other uses. It is anteceded by the former brand Prelaq. GreenCoat products are environmentally friendly and chromate free. It can be utilized for industrial, commercial, or residential applications where factors such as appearance, fire safety, mechanical resistance and long lifetime are important determining factors. GreenCoat coil- coated products are covered with a polyester-based paint layer. With its special organic coating, the steel sheet enables lower building energy demand and provides better thermal comfort compared to traditional steel sheets. That is achieved by a special special pigmentation in the color, which allows the

3 modification of the total solar reflectance (TSR) and the thermal emittance of the external and internal building envelope elements within wide color ranges. (SSAB, 2015b)

When GreenCoat is used on the external surface of the building envelope, the incoming solar radiation could be significantly reflected, which results in the sheet surface having a lower temperature than traditional paint. Alternatively, is also possible to substantially absorb the incoming solar radiation leading to a higher surface temperature. That means, that by the right choice of solar reflectance, GreenCoat may be able to minimize the total annual net heat flux conducted through the building envelope compared with traditional paints. In other words, the product conserves energy for heating and cooling; and in certain cases eliminates the need for cooling units depending on geographical location. (SSAB, 2015b)

This competitive alternative to zinc or copper can be adapted to any environment: city, maritime, and countryside. Pre-painted steel is recyclable, which means that the steel sheet is an environmentally friendly choice. Common applications include flat panels and sandwich elements for facades. A sandwich panel is a complete wall element extending from the inside to the outside of the wall. Sandwich panels are delivered as single ready-made elements ready for mounting on the frame of the building to produce an entire ready-made curtain wall. They can be used for all types of structures, both simple and exclusive. Common applications include industrial buildings, warehouse halls, thermal power stations, halls for commercial operations, refrigerated premises and sports halls. (SSAB, 2015b)

Selection of the studied locations

The choice of locations was based on the best possible representation of the different European climatic zones. The classification used in the European project, iNSPiRe, based on ambient temperature, ground temperature, humidity ranges and heating degree days for Europe (Inspire, 2014a, p.1), was selected for this study. It distinguishes between seven different climates, Nordic, North continental, Oceanic, Continental, South continental, Mediterranean, and Southern dry. The widely use empirical climatic classification developed by Köppen could have been proposed. However, it is focused on defining climatic boundaries in such way as to correspond to those of the vegetation (Encyclopedia Britannica, 2016). The selected classification better represents the availability of solar energy on the building envelope surface.

The selected locations are: Stockholm, Copenhagen, Liverpool, Amsterdam, Berlin, Vienna, Bern, Rome, and Madrid.

Figure 1.1. Reference European climates and studied locations (adapted from Inspire, 2014a, p.2).

Building type definition

Both the new construction and renovation markets take advantage of surface materials with optical selective properties. However, it was found that the recollection and analysis of the existing building thermal data in some European niches is a very complex task. Previous studies supported by the European Union have recognized the need to improve the reliability of the existing building data, which represent 80% of the European market (Inspire, 2014b). Based on this limitation it was decided to focus on new constructions (Post year 2000) for which, in general, reliable data are available. Regarding the building size, this study focuses on large-open-volume buildings, such as, industrial buildings, warehouse halls, thermal power stations, halls for commercial operations, refrigerated premises, and sports halls. The thermal performance of small-open-volume buildings such as residential and office building, would be used as a reference only. In small-open-volume buildings, the building internal volume is divided up by the internal structure of the building (walls and ceiling), in contrast, large-open- volume buildings have no internal divisions. In other words, in small-open-buildings the envelope surface area to volume ration (S/V) is higher than for large-open- volume buildings.

Lastly, given the lack of thermal data for large-open-volume buildings. This study will assume that the U-values for large-open-volume buildings across Europe vary in the same way as the U-value for office buildings do. Thus, the office building U- values presented in the Inspire project would be used as reference. It is worth to mention that the U-values of old buildings relate to those at the time of construction and are based on literature references. Those U-values do not consider the effect of renovations or upgrades (Inspire, 2014c, p.22). Therefore, this study will focus on post year 2000 U-values (See Appendix 1). The average mayor renovation interval in Europe is about 30 – 40 years (International Energy Agency 2008, p.12), which means buildings constructed after 2000 have not been renovated yet.

2 Theoretical Background

This study is intended to map the building envelope optimal optical properties minimizing the energy use for the selected geographical locations and building type. Therefore, there is a need to review the literature about solar passive technologies, current coil coating technology, European building codes, building energy simulation tools, and meteorological modeling to provide conceptual clarity and to help in the critical evaluation process. A mix of different reliable literature sources was consulted to place the reader at the forefront of the before mentioned fields.

The first part of this chapter will guide the reader through recently conducted studies, which are relevant to the aim and objectives of this research. Finally, the second part of this chapter will develop the conceptual foundations for this study.

Previous work

This study is in the same line of research as the dissertation made by Joudi (2015). His study titled “Radiation properties of coil-coated steel in building envelope surfaces and the influence on building thermal performance” consisted of both numerical and experimental investigations in Borlänge- Sweden for small-open-volume buildings (small cabins) and in Luleå-Sweden for large-open-volume buildings (sports arenas). The numerical methods include comparative simulations by the use of dynamic heat flux models, Building Energy Simulation (BES), Computational Fluid Dynamics (CFD) and a coupled model for BES and CFD. The results indicated that the use of low thermal emittance surfaces could increase the vertical indoor air temperature gradients depending on the time of day and outdoor conditions; and possible energy savings by the smart choice of optical properties on the interior and exterior surfaces of the building. Overall, it was concluded that the interior reflective coatings could contribute to energy savings and to improve the indoor thermal environment.

The report titled “Metal roofing on residential building in Europe: A dynamic thermal simulation study”, written by Chris Kendrick (2009), remarks, based on simulations for residential buildings, that metal roofing construction makes little difference to the thermal performance of housing across Europe when replacing tiled roofs of the same U-value. A small difference becomes apparent in hotter climates where U-values are higher, especially if a solar reflective coating is used. Moreover, Kendrick (2009) states that in hotter climates external envelope surfaces with a high total solar reflectance reduce the cooling loads slightly if the house is air-conditioned, and the hours of overheating are decreased if no cooling is used, however, there is a small penalty of additional heating requirements. Finally, Kendrick (2009) states that the effect upon heat islands is beneficial if solar reflective coatings are used. The report also shows interesting insulation figures from the European Insulation Manufacturers Association (EURIMA).

The scientific article titled “Estimating the effect of using cool coatings on energy loads and thermal comfort in residential buildings in various climatic conditions” written by Synnefa et al. (2007), based on simulations for twenty-seven cities around the world, including Mediterranean, humid continental, subtropical arid, and desert conditions, concluded that the use of cool coating (optical selective surfaces) is an inexpensive and passive solution that contribute to the reduction of cooling loads in air-conditioned buildings and to the improvement of indoor thermal comfort conditions by decreasing the hours of discomfort and the maximum temperatures in non-air-conditioned residential buildings. Synnefa at al. (2007) also states that, increasing the reflectance of residential building roofs would be beneficial regarding energy savings and a reduction of energy cost for lower or no roof insulation levels, as is the case for most old construction buildings.

The Scientific article titled “Heating energy penalties of cool roofs: the effect of snow accumulation on roofs” written by Hosseini M. and Akbari H. (2014), concluded that utilizing a cool roof (optical selective surface) is an efficient way to reduce building cooling energy use and the urban heat island effect. Their simulation results for a small office prototype building at four different cool climate locations, show that the annual building energy expenditure with cool roofs is slightly lower than those of dark roofs.

Solar radiation

In the solar field, the solar terrestrial spectrum is divided up into different intervals namely visible region, near infrared region, and infrared region. The solar extraterrestrial spectrum is substantially in the range of 0.25 μm to 3 μm; while the solar terrestrial spectrum, limited to 0.29 μm to 2.5 μm wavelengths (Duffi and Beckman, 2013, p. 139). The visible region, according to Duffie and Beckman (2013, p. 139) is the interval from 0.38 μm to 0.78 μm. After the visible region to the wavelength of 25 μm the interval is referred to as the near infrared region and from 25 μm to 1 mm the interval is known as the far infrared region (Duffi and Beckman, 2013, p. 139). Nonetheless, in the literature, there are other classifications regarding the infrared region. Thus, for the purpose of this investigation, the visible range would be defined as the interval from 0.38 μm to 0.78 μm. The near infrared (NIR) would be defined as as the interval from 0.78 μm to 2.5 μm. The interval from 2.5 μm to 25 μm would be considered as middle infrared, and from 25 μm to 1 mm as far infrared (See Figure 2.1). The near infrared interval is quite important since proximately 55% of the solar irradiance is actually coming in the NIR (0.78 μm to 2.5 μm) region (Duffie and Beckman, 2013, p. 63).

380 nm 780 nm

Figure 2.1. Near infrared light region (Reprinted from [Shimadzu Corporation, 2016]).

Additionally, there are two commonly mentioned radiation intervals which will be mentioned throughout this study. A first interval is known as solar or short- wave radiation range which corresponds to wavelengths from 0.3 μm to 3 μm and a second interval called long-wave radiation range or thermal range from 3 μm and forward, which is generated by radiation from bodies at temperatures near ordinary ambient temperature according to Plank’s law (Error! Reference source not found.). At near ambient temperature, substantially all emitted wavelengths are greater than 3 μm (Duffie and Beckman, 2013, p. 43) and the peak of the Plank spectrum lies about 10 μm, therefore, there is almost no overlap with the spectra for solar or short-wave radiation (Granqvist, 1991, p. 3) (See Figure 2.2).

2·ℎ·푐2 1 퐵 (휆, 푇) = · Equ. 2.1 휆 휆 ℎ·푐 푒휆·퐾퐵·푇−1

]

1 -

]

- 2

m μ

· 2 -

[W·m

Solar irradiance Solar

Spectralemissive [W·m power

Figure 2.2. Spectral distribution of solar irradiance on Earth (air mass 1.5) and spectral emissive power from a blackbody at 70 °C = 343 K ([Rönnelid, 1998, p.21] with permission from Rönnelid).

Kirchhoff law of thermal radiation

According to Kirchhoff law of thermal radiation the level of emittance can be related to the absorptance as follows:

휀(휆) = 훼(휆) Equ. 2.2

Additionally, the radiation energy balance of any body is given by:

훼(휆) + 휌(휆) + 휏(휆) = 1 Equ. 2.3

Consequently, for a nontransparent body, the Kirchhoff law can be written in the spectral form as:

휀(휆) = 훼(휆) = 1 − 휌(휆) 푓표푟 휏(휆) = 0

Or alternatively, in the integral form as:

휀 = 훼 = 1 − 휌

Where:

퐵 ∫퐴 훼(휆)·퐼푠(휆)·푑휆 훼 = 퐵 Equ. 2.4 ∫퐴 퐼푠(휆)·푑휆

퐵 ∫퐴 휌(휆)·퐼푠(휆)·푑휆 휌 = 퐵 Equ. 2.5 ∫퐴 퐼푠(휆)·푑휆

퐵 ∫퐴 휀(휆)·퐵휆(휆,푇)·푑휆 휀 = 퐵 Equ. 2.6 ∫퐴 퐵휆(휆,푇)·푑휆

Total solar reflectance (TSR) and thermal emittance (εth)

The total solar reflectance is defined as the reflectance in the solar or short- wave radiation range (ρsw)

3 휇푚 휌 ·퐼 ·푑휆 ∫0.3 휇푚 (휆) 푠(휆) 푇푆푅 = 휌푠푤 = 3 휇푚 Equ. 2.7 퐼 ·푑휆 ∫0.3 휇푚 푠(휆)

From the Kirchhoff law of thermal radiation:

푇푆푅 = 휌푠푤 = 1 − 훼푠푤 푓표푟 휏(휆) = 0

Where:

3 휇푚 훼 ·퐼 ·푑휆 ∫0.3 휇푚 (휆) 푠(휆) 훼푠푤 = 3 휇푚 Equ. 2.8 퐼 ·푑휆 ∫0.3 휇푚 푠(휆)

The thermal emittance is defined as the emittance in the long wave radiation range or thermal range (εth)

∞ 휇푚 휀 ·퐵 ·푑휆 ∫3 휇푚 (휆) 휆(휆,푇) 휀푡ℎ = ∞ 휇푚 Equ. 2.9 퐵 ·푑휆 ∫3 휇푚 휆(휆,푇)

From the Kirchhoff law of thermal radiation:

휀푡ℎ = 훼푡ℎ = 1 − 휌푡ℎ

Where:

∞ 휇푚 훼 ·퐵 ·푑휆 ∫3 휇푚 (휆) 휆(휆,푇) 훼푡ℎ = ∞ 휇푚 Equ. 2.10 퐵 ·푑휆 ∫3 휇푚 휆(휆,푇)

∞ 휇푚 휌 ·퐵 ·푑휆 ∫3 휇푚 (휆) 휆(휆,푇) 휌푡ℎ = ∞ 휇푚 Equ. 2.11 퐵 ·푑휆 ∫3 휇푚 휆(휆,푇)

Optical properties of organic coated steel sheets

The layout of the typical coil-coated steel is presented in Figure 2.3. The top coat paint layer thickness could be about 10 μm (Joudy 2015, p.10) or between 18 – 35 μm (Sander, 2014, p.16). For the exterior coatings, it is possible to add reflective pigments in the top coat to improve the reflectance. The reflective particles can either be reflective or transparent for certain wavelengths intervals, which allows defining specific selective characteristics in the short- wave range and in the long-wave range depending on the application. Additionally, the top coating can have pigments or not. That makes possible to have dark colors but with higher TSR (total solar reflectance) values.

Figure 2.3. Paint system components of the coil-coated steel (Reprinted from [Joudi, 2015, p.11] with permission from SSAB).

In cold climates, for instance, exterior coatings can have a low TSR (low reflectance in the solar or short-wave range) and a low thermal emittance (high reflectance in the long wave range) to maximize the solar gain and to reduce the thermal radiation losses simultaneously. Contrary, in hot climates, exterior coatings can have high TSR (high reflectance in the solar or short wave range) to reduce the solar gain and high thermal emittance (low reflectance in the longwave range) to help the surface to cool down faster and lower the equilibrium temperature by radiating out to the surrounding environment, in particular, to the sky which can be up to 40°C lower than ambient air (Joudy 2015, p.10). All these means that for different climatic conditions different selective coatings might be defined.

The use of high-TSR pigments, such as Paliogen Black, which is an organic semi conducting crystalline pigment with an extremely sharp and well tuned transition from highly absorbing in the visible region (0.38 to 0.78 μm) to being highly transparent to NIR (0.78 to 2.5 μm), results in different selective properties in comparison with the use of carbon black pigments as in typical coil coating pigmentations. Carbon black pigments effectively reduce TSR by the strong absorption of electromagnetic energy in the visible range as well as in the NIR. Figure 2.5 shows the reflective response of a coil coated product pigmented with Paliogen Black pigments and Figure 2.6 shows the reflective response of a coil coated product pigmented with Carbon black pigments.

Figure 2.5. Reflective dark grey for Figure 2.4. Normal dark grey for exterior surface Interior or exterior surface ([Joudi, 2015, p.13] with permission ([Joudi, 2015, p.13] with permission from SSAB). from SSAB).

The blue line, represents the reflectance of the surface, the green line and the red line, represent the normalized intensity of radiation from the sun and the blackbody radiation from a surface at 20 °C. Notice that in both of the figures above the thermal emittance is high (low reflectance in the long-wave range), which is a characteristic of coating systems with non-metal particles. Moreover, it is also possible to add metal particles like aluminum flakes with sickness of typically less than 1 μm and diameter about 50 μm (Joudi 2015, p.10) to the top coat to reduce the thermal emittance (increase reflectance in the long-wave range). Figure 2.6 shows the reflective response of a coil coated product with aluminum particles added to the top coat.

Figure 2.6. Reflective silver metallic coating system ([Joudi, 2015, p.13] with permission from SSAB).

Both top coat and primer usually have a polyester binder, which has high absorption in the middle and far infrared range. That gives rise to a high thermal emittance. The coating system thermal emittance is actually the combined effect of all coating layer (top coating, primer and corrosion protection) components (e.g. pigments, binders, metal particles, etc.). Figure 2.7 shows a typical high thermal reflective coating system with aluminum flakes in the binder.

Figure 2.7. Top coating of a surface with high thermal reflective aluminium flakes in the binder (Reprinted from [Joudi, 2015, p.11] with permission from SSAB).

Building applications

The building sector uses 75% of the pre-coated steel in Europe. Indoor and outdoor surfaces constitute the largest market. Pre-coated products are used for wall cladding and roofing of buildings. Most traditional applications include industrial halls, plant buildings, and agricultural buildings like stables, barns, and sheds. With a growing acceptance and appreciation, nowadays also large administration and office buildings are made of pre-coated metal. Domestic buildings are a more recent market to be tackled. External applications also comprise garage doors, roller shutters, and blinds, as well as composite panels with thermal or noise insulation. (Sander 2014, p.21)

Wall cladding is very commonly done with pre-coated metal on large industrial, agricultural, or administration buildings, regardless whether the material is mounted in long strips of corrugated or V-groove panels, or used to fabricate single cassettes built into curtain walls (Sander, 2014, p.23).

Building 74.9%

Auto Stocklists 8.2% 5.7%

Miscellaneous Appliance 2.9% Packaging Furniture 6.1% 0.2% 2.0%

Figure 2.8. Uses of pre-coated steel in Europe (Reprinted from [Sander, 2014, p.22]).

Building design variations at the different locations

There is very little information available about the detailed design of industrial buildings across Europe. Most of the information refers to the residential building sector and in lesser extend to the office building sector. Consequently, this study assumes that the U-values for large-open-volume buildings across Europe vary in the same way as the U-value for office buildings do. Thus, the office building U-values presented in the Inspire project would be used as reference. The Inspire project, is focused on residential and office construction types, and it reported that not all the desired information related to the building stock was gathered from literature, mainly because the data were missing or unreliable, and the “gaps” were being filled in using simulation work (Inspire,

2014b, p.2). Additionally, Inspire (2014c, p.22) reported that the literature review did not find sufficient data to categorize the amount of the EU-27 stock that has been upgraded therefore no correction have been applied to the U- value. The given U-values are related to those at the time of construction. All these shows the general lack of the key parameter statistics in the building sector. In the case of less traditional buildings, the statistics gaps are even bigger, which increases the uncertainty.

Fortunately, various countries have a long tradition of setting rules for the building sector. Often rules were initiated in response to country’s history (Inspire, 2014c, p.33), disasters such as large urban fires, epidemics or natural catastrophes as earthquakes, or just in response to local traditions. According to the International Energy Agency (2008, p.14), compared to the previously mentioned motivations energy efficiency regulations are relatively new in most countries. The first real insulation requirement for U-value, R-value, and specific insulation materials or multi-glazing, date back to the 1950s and the early 1960s in Scandinavian countries. Energy efficiency and comfort were the prime motivation for raising the requirements (International Energy Agency, 2008, p.14).

Regulations between different climates are tough to compare (International Energy Agency, 2008, p.54). However, despite the lack of data, and difficulty in comparing construction standards across different climate conditions, there are some identifiable patterns of relevance for this study. For instance, U-values and indoor temperature settings. The U-values in Europe vary substantially, and there is a significant difference between high insulation levels in the north and more differentiated and lower levels in the south of Europe. Sweden has the highest requirements found of U-values closely followed by Denmark and Norway. (International Energy Agency, 2008, p.62, Inspire 2014b, p.20). On this particular Inspire (2014c, p.21) states that the thermal performance across all EU-27 countries has generally improved since 1945. In some countries, U- values have improved a lot recently, as a result of much more stringent regulations driven in part by EU-wide commitments to improve energy efficiency and reduce carbon emissions. Other countries that would be expected to have good levels of thermal performance, such as Finland and Sweden, have also improved but less dramatically, since their starting point for U-values was much lower than other countries.

In regard to the temperature settings inside the building, generally speaking, the set temperatures are not fixed values (regardless the standard obligations); and the comfort conditions might usually not be met for the entire building for 24 hours/day as a consequence of high energy prices, country economic conditions and severity of the climate (Inspire, 2014b, p.16). Besides, the comfort temperature varies from country to country, for the time of the day, for different living spaces (Inspire, 2014c, p.20), and even from person to person (ASHRAE, 2009, p. 9.1). Which makes it difficult to select a constant set temperature for heating and cooling. Finally, the literature review reveals that despite of the very different climatic conditions all over Europe the statistic average of the heating consumption across Europe does not vary significantly. More strict building regulations avoid heating demands from growing fast

(Inspire, 2014b, p.15). Moreover, the cooling consumption is higher in offices in the southern climates (Inspire, 2014b, p.29).

In regard to the office construction types it is believed that similar styles of construction are generally found throughout all EU-27 countries (Inspire, 2014c, p.33) and in the same line of thoughts, under this study it is assumed that a quasi-homogeneous group of new industrial buildings is found in Europe. On this particular, Archiscapes (2014) denotes that, over time construction materials and technologies changed both for the residential and industrial buildings. However, the structure of the warehouse held up in its core aspects: the structural pattern, wide interiors and large windows are the same, and so are the free design rectangular plan and the pitched roof.

Simulation based building design

A simulation is the imitation of a real world process or system over time. Both existing and conceptual systems can be modeled with simulation (Banks 1998, p.3). Also, Ljung and Glad (2004, p.14) say that a simulation is a “numerical experiment” made on a model, which is inexpensive and non-dangerous in nature and complements system experimentations. Moreover, a simulation could also be defined as a virtual experiment (Aste et al., 2007, p.171; Sinha et al., 2001, p.84).

Simulations help to forecast the thermal performance of a building or a building component, to improve the thermal comfort level, to estimate the investment payback period, and to define best practices which lead to energy consumption reductions and consequently to the reduction of GHG emissions. In fact, simulations have contributed to estimating that a better design of new buildings would result in a 50 – 75% reduction in the energy consumption relative to 2000 levels and that the appropriate intervention in the existing stock would yield 30% reduction (Clark, 2001, p.1). More recently, the European Commission (2014, p.6) claimed that new buildings consume only half as much as typical buildings from the 1980s.

Simulation tools have traditionally been limited by the processing speed of personal computers or workstations. To reduce computational load previous simulation tools reduced the complexity of the underlying equation systems, neglected some physics phenomena (e.g. longwave radiation exchange), assumed as constant some parameters (e.g. thermal properties), and imposed simple boundary conditions (e.g. steady state or steady cyclic) (Clark, 2001, p.3). However, as the computational speed has been increasing the complexity and accuracy of the simulations tools have increased as well.

Currently, there are numerous building simulation tools available. Some are intended for basic analysis and some others for very specific studies. Some follow a component-based approach and others a system-based approach (Köhl, 2004, p.78). However, it is not easy to select among them. Even for the most popular tools the different capabilities are not well stated. In other words, there is not a common language to describe what the different tools can do

(Crawley et al., 2008, p.672). On this particular, Clark (2001) states that it is important to appreciate the limitation of a simulation tool. Building energy simulation (BES) tools are based on many different simplistic assumptions. On the contrary, computer flow dynamics (CFD) tools relay on very complex physical correlations and mathematical models to try to increase the spatial and temporal resolution.

Building simulation is a very complex activity involving transient energy flows and stochastic occupant interactions. A rigorous study implicates a significant effort, which could be worthy, or not. For instance, regarding simulation cost, a simplified model could provide better cost-effective results than a detailed model. About it, Clark (2001) comments that the minimum necessary level of detail from a thermal point of view varies according to the objective of the simulation study. In summary, if the purpose of the simulation is to get to know what happens at the micro level, e.g., a specific point in the building, CFD is the approach to use, if on the contrary, the objective is to evaluate what happens at the macro level, e.g., a whole building or a part of it, BES is the approach to use.

TRNSYS 17

TRNSYS is a transient systems simulation program with a modular structure developed by the Solar Energy Laboratory at the University of Wisconsin with 35 years of commercial availability (Thermal Energy System Specialists, 2015). It recognizes a system description language in which the user specifies the components that constitute the system and the way they are connected. The TRNSYS library includes many of the components commonly found in thermal and electrical energy systems, as well as component routines to handle input of weather data or other time-dependent functions and output of simulation results. The modular nature of TRNSYS gives the program tremendous flexibility and facilitates the addition to the program of mathematical models not included in the standard TRNSYS library. TRNSYS is well suited to detailed analyses of any system whose behavior is dependent on the passage of time. Main applications include solar systems (solar thermal and photovoltaic systems), low energy buildings and HVAC systems, renewable energy systems, cogeneration, fuel cells. (The University of Wisconsin, 2015)

In TRNSYS 17 the components are outlined in a graphical user interface known as TRNSYS Simulation Studio. For multi-zone building modeling with Type 56, which is the only conponent able to analyse the influence of the envelop optical properties, the building description is made in a graphical interface called TRNBUILD and the building geometrical data description has to be done in SketchUp 2016 for detailed internal longwave calculations, or directly in TRNBUILD for standard calculations. In addition, an interface called TRNEdit provides support for parametric runs and text editing of the input file. As stated before TRNSYS is a tremendously flexible simulation tool, however, to use TRNSYS it is required experience and expertise (Köhl, 2004, p.78) to get the most out of it. Lastly, the validation process of TRNSYS17 was based on

17 standardized test procedures such as ASHRAE Standard 140, BESTEST and DIN EN ISO 13791 (Aschaber et al., 2009, p. 1986)

Meteorological data

Solar engineering and building energy performance are intimately related to meteorological data. Many meteorological variables affect the building energy balance. Solar radiation is probably the most important one. Solar radiation influences the ambient temperature, the movement of air mass, the relative humidity and some other variables. The accuracy of meteorological data is of paramount importance for building energy performance modeling. Many companies and institutes provide meteorological data to the market based on different technologies to measure, to estimate and to interpolate meteorological parameters. Besides, it is very common today to listen to satellite meteorological data, and indeed it is a good source of data if there is nothing else available. A study performed by Hagen (2011, p.78) indicates that the increased ground reflectance provoked by the snow is interpreted as solar radiation reflected from clouds by meteorological satellites, which affects the estimation of the cloud index. In fact, it was noticed at the Norwegian stations Bergen and Kise that at clear sky situations with snow covered ground the measurements have shown to experience too low satellite derived global radiation. The same study also mentions that satellites have no information about the horizon and ignore the effect of the topography on the radiation, which results in an overestimated global radiation during hours when the sun is below the actual horizon.

2.10.1 Meteonorm 6.1

Meteonorm is a meteorological database containing climatological data for solar engineering applications at every location on the globe with 8325 meteorological stations in the whole world from which 1325 meteorological stations measure irradiation. In Europe, Meteonorm receives data from 1600 stations. The output are stochastically generated typical meteorological years (TMY) from interpolated long-term monthly means (Meteonorm, 2015b, p.1). The software basically works in two steps. In a first step, surrounding weather stations are searched, and their long-term monthly means are interpolated to the specified location. Data derived from satellite imagery help to improve radiation parameters in regions with a low density of available ground-based data. In a second step, a stochastic weather generator runs on the interpolated monthly data to generate a typical mean year of data in hourly resolution (8760 values per parameter) for most of the commercial output formats. Some of the output formats even require a minute-by-minute time resolution. (Meteonorm, 2015b, p.11) Meteonorm also uses the solar radiation data to calculate additional parameters referred to as supplementary. The supplementary parameters are not of the same quality as the main parameters and were not validated in an equal comprehensive way. The supplementary parameters calculated in Meteonorm 18 are dew point temperature, relative humidity, mixing radio, wet-bulb temperature, cloud cover, global and diffuse brightness, long-wave radiation, wind speed, wind direction, precipitation, driving rain, atmospheric pressure and UV radiation. (Meteonorm, 2015a, p.47) The data time period for the solar radiation and for the ambient temperature and all the other parameters differ. Two climatological time periods are available for each group as well as a future time period for a climate change scenario. Temperature (and all other parameters except for radiation): 1961 – 1990 and 2000 – 2009, and radiation: 1981 – 1990, 1991 – 2010 (Meteonorm, 2015b, p.28).

The uncertainty of the ground measurements ranges between 1 and 10%. In Europe, most radiation is lying between 2 and 4%. For ground interpolation at a distance of 2 km, the uncertainty is at 1% and at 100 km the uncertainty is generally at 6%. For distances bigger than 2000 km the uncertainty is set constant at 8% (Meteonorm, 2015a, p.71). The uncertainty for the satellite data is ranging between 3 and 6% for Europe and Northern Africa for the Meteosat high resolution satellites and between 4 and 6.5% for all other satellites (Meteonorm, 2015a, p.71).

Nowadays Meteonorm is a popular meteorological data source among building energy simulation experts. Somehow meteonorm works as a standardization tool permitting developers to access a comprehensive, uniform meteorological database (Meteonorm, 2015b, p.1), which is an advantage when it comes to comparing results from different studies. Nonetheless, various versions of Meteonorm use different correlations to estimate important parameter such as the effective sky temperature. It may be important to compare based on the same Meteonorm version.

3 Methodology

This chapter depicts the research methods and methodologies used to map the envelope optimal optical properties minimizing the energy use for the selected geographical locations and building type.

For the conduction of this research project, the following phases and milestones have been identified in Figure 3.1.

Aim and individual research objectives definition

Phase 1 Choice of Choice of Building Literature locations simulation type research tools definition

Phase 2 Building model design

Model testing Phase 3 Simulations for all locations (Data collection)

Mapping of the envelop optimal optical properties minimizing Phase 4 the energy use for the selected locations.

Figure 3.1. Schematic of the research phases.

The next sections illustrate the research methodology and methods adopted to plan and steer this research, together with the data collection means, the data analysis, and finally a list of the limitations and assumptions presented in this study.

Research methodology

The methodologies are the processes, followed through the entire research activity and could be divided into two polar opposite categories: Quantitative methodology and Qualitative methodology (Håkansson, 2013, p.68; Murray and Hughes, 2008, p.150; Biggam, 2011, p. 130).

As mentioned in the literature background chapter, a simulation is the imitation of a real world process or system operation over time. Moreover, both existing and conceptual systems can be modeled with simulation (Banks 1998, p. 3). Also, Ljung and Glad (2004, p.14) say that a simulation is a “numerical experiment” made on a model, which is inexpensive and non-dangerous in nature and complements system experimentations. A simulation is also defined as a virtual experiment (Aste et al., 2007, p.171; Sinha et al., 2001, p.84). In the view of Hernández et al. (2010, p.122) an experiment is a controlled situation in which one or more independent variables (causes) are intentionally manipulated to analyze the consequences of such manipulation over one or more dependent variables (effects). In the light of the presented arguments, it

20 could be resolved that from a methodological point of view the modeling and simulation activities could be considered as a kind of experiment of a cognitive nature with no empirical implications.

Based on the above mentioned research methodologies, the research objectives, the data collection technique, and the nature of the academic discipline, this study could be categorized as quantitative. Håkansson (2013, p.69), states that the quantitative research method supports experiments and testing by measuring variables to verify or falsify theories and hypothesis. Also, the quantitative methodology requires large data sets and use of statistics to test the hypothesis and make the research project valid. On this particular, Murray (2008, p.150) argues that a quantitative study is one in which the data you collect and analyze involves the accurate measurement of phenomena and often the application of statistical analysis. Finally, Creswell (2014, p.4) describes a quantitative research as an approach for testing theories by examining the relationship among variables.

This study aims to verify a theory by specifying a hypothesis and collecting numeric data from simulation studies to support or refute the hypothesis. Also, key variables are identified and related to the hypothesis. These, point out that this is a quantitative study.

Research methods

The research methods are “a collection of different methods, ranging from philosophical assumption to data analysis” (Håkansson, 2013, p.68). Usually, a philosophical assumption is made at the starting stage of the research since it represents the standpoint for the study. This study is based on the positivism principle, which assumes that the reality is objectively given and independent of the observer and instruments (Håkansson, 2013, p.69). The selected research method is the experimental with a deductive approach and an experimental design. This method establishes relationships between variables and finds causalities between the relationships and is often used when investigating the performance of a system (Håkansson, 2013, p.70). Biggam (2011, p.124) argues that the experimental research tends to be the domain of the scientist that attempts to test a proposed theory or a hypothesis through some type of experiment.

This research follows a deductive approach. The deductive approach tests theories to verify or falsify hypothesis by using almost always, quantitative methods with large data sets (Håkansson, 2013, p.71). The design is experimental since this study concerns on the control over all factors/variables that may affect the results of an experiment. As the experimental research method, the experimental design verifies or falsifies the hypothesis and provides causes and effect relationships between variables. (Håkansson, 2013, p. 71).

Finally, it is worth to mention that it is difficult to choose well-suited methods (Håkansson, 2013, p.67). In fact, whatever the nature of the study, there is no

21 such thing as a perfect methodology (Murray and Hughes, 2008, p.152). It is also worth to mention that there is not a common agreement among experts regarding the terminology; however, “what matters is not the label that is attached to a particular strategy, but whether it is appropriate for your particular research” (Saunders, Lewis, and Thornhill, 2000, p.92).

Modelling

The modelling process started with the selection of a simulation tool. Nowadays, there are numerous building energy simulation (BES) tools available. Some are intended for basic analysis and some others for very specific studies. However, it is not easy to select among them. Even for the most popular tools, the different capabilities are not well stated, in other words, there is not a common language to describe what the different tools can do (Crawley et al., 2008, p.672). In order to tackle this inconvenient, it was decided in first instance to identify the simulation tool requirements for this project with the help of three building simulation experts.

The following simulation tool requirements were identified for this project.

• Determination of cooling demand. • Determination of heating demand. • Possibility to change insulation values. • Possibility to change building envelope optical properties. • Possibility to change location (climate data). • Possibility to use well-mixed and stratified air volume assumptions. • Possibility to perform multi-zone analysis.

After the simulation requirement identification, it was concluded that some simulation tools could fulfill the project needs. The final selection was based on modeling flexibility and the availability of local experienced supporting simulation experts. As a result, the simulation tool TRNSYS 17 was selected.

3.3.1 Simulation tool testing

To test the simulation tool and the methodology proposed to elaborate the large-open-volume building model, a small-open-volume building model was created with the objective to compare its results with real passive measurement available from a previous project in the same field of study. The measurements come from three single room cabins, namely, Cabin A, Cabin B, and Cabin C (3.87 m x 3.27 m x 2.40 m, each), which differ only on their envelope optical properties and are located in Borlänge–Sweden. The measurements were taken from the 21th to the 28th of June 2007.

Cabin A has low interior emittance, low total solar reflectance (TSR) and high exterior emittance. Cabin B has low interior emittance, high TSR and high exterior emittance. Cabin C has standard optical properties, high interior and exterior emittance, and low TSR. The weather file for the small-open-volume building model simulations was manipulated to include as much weather information as possible from the time of the measurements. The total horizontal radiation recorded during the measurements was decomposed into direct normal horizontal radiation and diffuse horizontal radiation according to equations 3-1 to 3-5 from Duffie and Beckman (2006). The ambient temperature, wind seed, and relative humidity were recorded as well during the measurements. The rest of the necessary weather variables, solar zenith angle and solar azimuth angle, were taken from a typical weather file extracted from METEONORM 6.1, for Borlänge-Sweden.

푐표푠(휃푧) = 푠푖푛(훿푠) ∗ 푠푖푛(∅푠) + 푐표푠(훿푠) ∗ 푐표푠(∅푠) ∗ 푐표푠(휔푠) Equ. 3.1

360∗푛 퐼 = 1367 ∗ (1 + 0.033 ∗ 푐표푠 푑) ∗ 푐표푠휃 Equ. 3.2 푒푥푡푟푎푡푒푟푟푒푠푡푟𝑖푎푙 365 푧

퐼푔푙표푏푎푙 푘푇 = Equ. 3.3 퐼푒푥푡푟푎푡푒푟푟푒푠푡푟𝑖푎푙

1.0 − 0.09푘푇 , 푘푇 ≤ 0.22 퐼푑𝑖푓푓,ℎ 2 3 4 = { 0.955 − 0.1604푘푇 + 4.388푘푇 − 16.638푘푇 + 12.336푘푇 , 0.22 < 푘푇 ≤ 0.8 Equ. 3.4 퐼푔푙표푏푎푙 0.165 , 푘푇 > 0.8 Erbs et al. correlation cited in Duffie and Beckman (2006, p. 76).

퐼푔푙표푏푎푙−퐼푑𝑖푓푓,ℎ 퐼푑𝑖푟,푛 = Equ. 3.5 푐표푠(휃푧)

The final small-open-volume building simulation results for passive conditions show a reasonably good agreement with the passive measurements (see Figure 3.10). The set of key parameter value assumptions described below provided the best fit for the small-open-volume building and was assumed as valid for the analysis of the large-open-volume building model.

Table 3.1. Selected set of key parameter value assumptions for the small- open-volume building passive analysis.

Key parameter Value assumption

Effective sky temperature Tdry - 5 °C

Ground temperature Tdry

Ground surface temperature Tdry Infiltrations and ventilation rate 0.3 Air changes per hour Density driven flow rate 0.5 Air changes per hour Internal convective heat transfer coefficient_ROOF 11 kJ/(h·m²·K) Internal convective heat transfer coefficient_WALL 11 kJ/(h·m²·K) Internal convective heat transfer coefficient_FLOOR 1 kJ/(h·m²·K) External convective heat transfer coefficient_ROOF 32 kJ/(h·m²·K) External convective heat transfer coefficient_WALL 32 kJ/(h·m²·K) External convective heat transfer coefficient_FLOOR 32 kJ/(h·m²·K)

The assumption for the effective sky temperature comes from a recommendation made by the IDA ICE developers stated in the study performed by Joudi (2015). The ground and ground surface temperature assumption are commonly found in building simulation literature and in TRNSYS examples; additionally, the results from those assumptions were very similar to the results from a dedicated ground modelling component in TRNSYS. The infiltrations rate assumption is based on the fact that coil coated construction elements improve the air tightness, thus the traditional assumption of 0.5 air changes per hour was reduced to 0.3 air changes per hour. The density driven flow rate is a guess, no recommendations were found on this regard. The internal convective coefficient assumption for the roof and the walls follows TRNSYS recommendations, however, for the floor it was found that 11 kJ/(h·m2·K) was a too high value creating unmeaningful result for the bottom air node temperature; a constant value of 1 kJ/(h·m2·K) showed a better matching with the passive measurements. The external convective coefficient assumption for the roof and walls is half of TRNSYS recommendation; the selected value shows a better matching with the passive measurements than TRNSYS recommendation; the external convective coefficient assumption for the floor is part of TRNSYS routine for a floor in direct contact with the ground. At this point, it is worth to mention that the presented set of assumptions does not completely represent the physical building phenomena, which is totally dynamic in nature. In BES many dynamic parameters are assumed as constant, thus the quality of the results greatly depends on the assumptions made.

After the simulations for passive conditions, thirty-six different sets of key parameter value assumptions were analyzed for the small-open-volume building model under standard envelope optical properties (high interior and exterior emittance, and low TSR) and active conditions for one week (See Table 3.2). This analysis allowed to get an idea about the accuracy of building energy simulation (BES) tools and the importance of the key parameter assumptions. BES accuracy greatly depends on the key parameter assumptions input. Figure 3.2 depicts the energy demand of the small-open-volume building model for each one of the key parameter assumption set

Table 3.2. Selected set of key parameter value assumptions for the small- open-volume building active analysis.

Key parameter Value assumption

Effective sky temperature Tdry - 5 °C

Ground temperature Tdry

Figure 3.2. Dependency of BES results on key parameter assumptions for the small-open-volume building. The vertical axis indicates the energy demand during the seven-day simulation period, the horizontal axis indicates several different sets of key parameter assumptions.

The thirty-six sets of key parameter value assumption include a few extreme non-realistic conditions, in which for instance, the external convective heat transfer coefficient is set to zero and later to a very high value. The extreme

25 conditions lead to extreme low or high energy demand values, however, notice that even if the extreme energy demand values are ruled out, there is still a huge variation among the various energy demand estimations. That reflects the importance of the key parameter value assumptions and the possible accuracy range of building energy simulation tools (BES). It is worth to mention, that Figure 3.2 similarly depicts cases where Cabin B (low interior emittance, high TSR and high exterior emittance) gives the lowest total annual energy demand of all cabins, and it also depicts some other cases where Cabin B gives the highest demand. In cases of high building heat dissipation (e.g. due to misestimation of the sky temperature or the convective heat transfer coefficients) Cabin B gives the highest total annual energy demand and Cabin C (high interior and exterior emittance, and low TSR), gives a total annual energy demand lower than the demand of Cabin A (low interior emittance, low total solar reflectance (TSR) and high exterior emittance). Consequently, it could be said that the incorrect choice of key parameters as the sky temperature correlation and the convective heat transfer coefficients might generate erroneous results.

3.3.2 Large-open-volume building thermal model

As a first step, the geometry for the study model (100 m x 20 m x 8 m) was drawn in SketckUp as shown in Figure 3.3. The volume was divided up into 8 equidistant air nodes, being the first air node at the bottom.

Figure 3.3. Thermal model geometry drawn in SketchUp. Air node one is located as the bottom of the model.

After the study building geometrical definition, the thermal model was created in TRNSYS 17 Simulation Studio. The only building component in TRNSYS 17 that could be adapted to the objectives of this study is Type56. The final component layout is shown in Figure 3.4. The thermal model consists of two calculators, Equa1 and Equa2, dealing with the calculation of some solar

26 angels; a weather data processor, Type15-2, which reads tm2 files extracted from METEONORM 6.1; a Type56 component, which contains all the geometrical, thermal, and calculation mode information; one last calculator containing the values of all the TRNBuild userdefined inputs (See Figure 3.4); and two output data management components, one able to display selected system variables while the simulation is progressing called Type65d; and another one recording the output data into an Microsoft Excel file called Type46a. For the parametric runs Type65d was turned off.

Figure 3.4. TRNSYS Studio, Thermal model components (Left) and equation block (Equa3) description (Right).

After the creation of the thermal model, it is necessary to define the thermal characteristics and calculation mode for the study model, that is done with TRNBuild. Note that the building geometrical characteristics cannot be modified if the longwave radiation exchange detailed calculation mode is to be used. For that reason, the geometry mode is to be kept as 3D data. The 3D geometry can only be generated from SketchUp (See Figure 3.5).

Figure 3.5. TRNBuild, geometry mode selection window.

It is also very important to move all the zones created in SketckUp into a single zone. With this approach, the final thermal model would consist of a single thermal/radiative zone and multiple air nodes within that zone. If that is not

27 accomplished neither the air stratification nor the internal longwave radiation calculation would work out (See Figure 3.6).

Figure 3.6. TRNBuild, Zone/air node layout (Left) and TRNBuild, radiation calculation mode window (Right).

The definition of the construction material layers (conductivity, heat capacity, density), finestration, infiltration, ventilation, heating level, cooling level, internal gains, and comfort level is very intuitive and it is well explained in the Type56 user guide.

Finally, it is necessary to define the amount of air to be exchanged between air nodes (density driven flow rate or coupling air flow). That is done in the coupling air flow field of the coupling window (See Figure 3.7). It is not clear how does TRNSYS takes into account the density driven flow phenomena, however, TRNSYS is one of the very few building energy simulation tools that tries to describe it.

Figure 3.7. TRNBuild, density driven flow rate selection window.

During the early stages of the modelling process the optimization tool GenOpt was coupled. GenOpt has an inbuilt algorithm able to estimate a matrix of combinations among the independent variables present in both the Simulation

Studio (.dck file) and TRNBuild (.b17 file), and it can automatically run simulations for different independent variable combinations until an optimal point is fund. GenOpt can run optimizations under the standard longwave calculation mode, however, unfortunately, it cannot run optimization studies based on the detailed longwave calculation mode, because it cannot automatically generate the files containing the view factor matrix necessary for the detailed internal longwave analysis. In chapter 3.3.3.11, the effect of the longwave calculation mode in small and large open-volume building would be discusses and the detailed longwave calculation mode will prove to give estimation of the total annual energy demand in large-open-volume buildings in the same order of magnitude as the standard radiation calculation mode. Nonetheless, the standard calculation mode will be used for optimization proposes and its results will be compared with a simple sensitivity analysis in which the optical properties are studied independent of one another.

3.3.3 Large-open-volume building model sensitivity analysis

After the set of key parameter value assumptions was selected, a parametric study was implemented to analyze the sensitivity of the large-open-volume building annual energy used to maintain indoor conditions to the set of key parameter value assumptions and some additional variables under constant standard envelop optical properties (high interior and exterior emittance, and low TSR) and under temperature control at human level (air nodes one and two, the bottom air nodes, in the study model). The key parameter value assumptions are as follows:

Table 3.3 Selected set of key parameter value assumptions for the large- open-volume building sensitivity analysis.

Key parameter Value assumption

Effective sky temperature Tdry - 5 °C

Ground temperature Tdry

The parametric analysis results provided new insights for the optimization of the optical properties and its result are described below.

3.3.3.1 Infiltrations and ventilation

The infiltrations and the ventilation are two similar concepts that involve the exchange of air between the buildings and the ambient. Therefore, both could be studied together. A single air exchange rate including the infiltrations and the ventilation would be proposed.

One of most important advantages of steel cladding constructions is that the infiltrations are greatly reduced in comparison to other construction technologies. However, the infiltration rate is a very difficult to estimate parameter since it is not only dynamic but also stochastic in nature. The infiltrations and the ex-filtrations depend on the wind direction, building orientation, ventilation strategy (e.g. mechanical or passive), indoor to outdoor temperature difference, and the occupant window and door opening behavior (stochastic human interaction).

Figure 3.8 shows that the infiltrations rate is a very influential factor. The energy demand at all the study locations rises significantly when the infiltration rate increases. For the sensitivity analysis, a base infiltration rate of 0.3 air changes per hour was selected. Literature often show values around 0.5 and 0.7 ACH, however, as said above steel cladding constructions greatly reduce the infiltration rate.

Figure 3.8. Model sensitivity to the infiltration rate. 3.3.3.2 Density driven flow

The figure below shows the sensitivity of the model to the density driven flow rate variations. It is important to state that it is not clear how TRNSYS calculates the effect of the density driven flow. However, the small-open-volume building model showed that TRNSYS, to some extent, can describe the stratification

30 phenomena (see Figure 3.10). Additionally, no recommendation regarding the density driven flow rate assumption was found.

Figure 3.9. Model sensitivity to the density driven flow rate.

Measurements airnodes 1, 2, and 3

TRNSYS results

airnodes 1, 2, and 3

Figure 3.10. Measured and simulated indoor air stratification for the small- open-volume building model with low internal emittance and high TSR (Cabin B) for passive conditions.

Figure 3-11 and 3.12 show the indoor air stratification for the large-open-volume building model in Madrid and Stockholm according to TRNSYS 17, both with a density drive flow rate of 10 air changes per hour (ACH). The temperature of air nodes 1 and 2 is ideally controlled, the temperature of air node 6 is influence by the heat realized from the illumination.

Figure 3-11. Large-open-volume building air stratification in Madrid, for the 14th of February (up) and air stratification for the 22nd of July (down) A1 represent the first air node, which is located at the bottom of the building model.

Figure 3.12. Large-open-volume building air stratification in Stockholm, for the 14th of February (up) and air stratification for the 22nd of July (down) A1 represent the first air node, which is located at the bottom of the building.

As seen in Figure 3-11 and Figure 3.12, unfortunately, TRNSYS 17 does not calculate the stratification correctly in the winter case. In reality, if the lower part of the building is heated up to 19 °C, as the figures show, the warm air should rise if it is warmer than the air around, and the cold air should sink if it is colder than the air around according to the advective and diffusive transportation of

33 mass and energy. The winter case, should have shown all air nodes close to 19 °C. The stratification results shown, indicate that the model needed a correction, otherwise, the heating loads will be significantly underestimated.

Given that TRNSYS 17 does not calculate stratification correctly during the heating hours, a new density driven flow rate strategy was introduced to force the air nodes to converge to a temperature close to the heating temperature setting during heating periods. In principle, two different density driven flow rates were used in TRNSYS 17, a high one (1000 ACH) during the heating periods (winter) and a low one (20 ACH) during the cooling periods (summer). Figures Figure 3.13 and Figure 3.14 show the new stratification results. These new results allow for a better estimation of the heating loads.

Figure 3.13. Large-open-volume building second air stratification approach in Madrid, for the 14th of February (up) and air stratification for the 22nd of July (down) A1 represent the first air node, which is located at the bottom of the building model.

Figure 3.14. Large-open-volume building second air stratification approach in Stockholm, for the 14th of February (up) and air stratification for the 22nd of July (down) A1 represent the first air node, which is located at the bottom of the building.

3.3.3.3 Temperature setting

The comfort temperature varies from country to country, for the time of the day, for different living spaces (Inspire, 2014b, p.20), and even from person to person (ASHRAE, 2009, p.9.1). That makes it difficult to select constant temperatures settings for heating and cooling. To account for this issue several temperatures settings were studied. Figure 3.15 depicts the large-open-volume building model sensitivity to the temperature settings. It is important to notice that the total energy demand increases significantly for high temperature settings in Stockholm. The same occurs in Copenhagen, Liverpool, Amsterdam, Berlin, Vienna, and Bern; however, Roma and Madrid behave in a separate way due to their hotter climate. For the optimization study an indoor temperature setting of 20±1 °C was assumed, in other words, the temperature setting for heating is 19 °C and for cooling is 21 °C.

Figure 3.15. Model sensitivity to indoor temperature settings.

3.3.3.4 Convection heat transfer coefficients

Figure 3.16 and Figure 3.17 show the sensitivity to steady state (constant) internal and external convection coefficients respectively. All locations present similar behaviors regarding the internal convection coefficient. The higher the internal heat transfer coefficient the higher the convective heat flux to and from the indoor air, and the higher the energy demand. On the other hand, the external convection coefficient has a minor impact on well insulated building, however, on less insulated buildings, high external convective coefficients significantly impact the yearly energy demand. In the case of Madrid and Rome, it contributes to a reduction of the energy demand. The envelope insulation reduces the thermal conductive coupling between the external and the internal surfaces, thus for less insulated buildings, the external convective heat transfer coefficient is more influential than for the well-insulated ones. For the optimization study an internal convection coefficients of 11 kJ/(h·m2·K) was selected for the internal walls and the ceiling, and an internal convection coefficients of 1 kJ/(h·m2·K) of chosen for the floor. Similarly, an external convection coefficients of 32 kJ/(h·m2·K) was selected for the external walls and the roof, and an external convection coefficients of 0.001 kJ/(h·m2·K) was chosen for the slab.

Figure 3.16. Model sensitivity to internal heat transfer coefficient.

Figure 3.17. Model sensitivity to external heat transfer coefficient.

3.3.3.5 Insulation level

According to the weather classification used in the iNSPiRe project, there are seven different climate zones in Europe (See Figure 1.1). Additionally, the iNSPiRe project used literature data and simulation work to characterize typical small-open-volume building (residential and office buildings) insulation levels at each of the climate zones. Given the lack of thermal data for large-open-volume buildings. This study assumes that the U-values for large-open-volume buildings across Europe vary in the same way as the U-value for post year 2000 office buildings do (See Appendix 1Appendix ). Thus, the office building U- values presented in the iNSPiRe project are used as reference. Table 3.4 shows the reference data taken from the iNSPiRe project.

Table 3.4. Selected U-values for the study locations

Roof Wall Floor Location Insulation level U-value U-value U-value [W/(m²·K)] [W/(m²·K)] [W/(m²·K)]

Stockholm Nordic 0.16 0.25 0.23 Copenhagen North-continental 0.25 0.39 0.58 Liverpool Oceanic 0.33 0.43 0.32 Amsterdam Oceanic 0.33 0.43 0.32 Berlin Continental 0.31 0.36 0.40 Vienna Continental 0.31 0.36 0.40 Bern South-continental 0.30 0.41 0.37 Rome Mediterranean 0.65 0.77 0.58 Madrid Southern dry 0.75 0.85 0.80

Figure 3.18 shows the large-open-volume building sensitivity to the different insulation levels. For this parametric study, all the different building insulation levels used in this study (Nordic, North Continental, Oceanic, Continental, Southern Continental, Mediterranean, and South Dry) were simulated at each of the study locations. The results demonstrate that even for hot climates it is beneficial, from an energy point of view, to build well insulated buildings as the Nordic buildings. All locations present similar responses. On this particular, Joudi (2015) states that the reduction in the heat flux and increase of the insulation thickness does not have a linear relationship. There is an optimal insulation thickness regarding the material used.

Figure 3.18. Model sensitivity to insulation level.

3.3.3.6 Ground temperature and ground surface temperature

The sensitivities to ground temperature and to ground surface temperature are depicted in Figure 3.19 and Figure 3.20 respectively. It is possible to notice from the results that the slab insulation level at the locations is good enough to avoid excessive heat transference to and from the ground. Neither the ground

39 temperature nor the ground surface temperature are influential for the large- open-volume building model. Similar plots were obtained for all other locations.

Figure 3.19. Model sensitivity to ground temperature.

Figure 3.20. Model sensitivity to ground surface temperature.

The energy losses to the ground could play a significant role in the energy balance of a building. During cold seasons the heating energy could be lost if the ground floor is not properly insulated. To be able to study the influence of the ground and the ground surface temperatures, different ground models were

40 tested (See Figure 3-21). Type49 and Type1244 gave the same results both for passive and active simulations. However, Type 714 showed a much higher temperature span. For all models, the results are dependent on the input ground thermal properties. This study assumes as valid the ground thermal properties from the European project Thermomaps (2015).

Figure 3-21. Comparison among different ground models for Borlänge- Sweden.

Type1244 was selected to estimate the ground and the ground surface temperatures. Figure 3.22 and Figure 3-23 show the seven-layer ground mesh used for the analysis with Type1244.

Figure 3.22. Ground discretization top view.

Figure 3-23. Ground discretization vertical layers.

To achieve results stability Type1244 needs to be run during a minimum of five years. Figure 3.24 shows a comparizon between the heat transferred from the building to the ground during the second and fifth year for the large-open- volume building model under Borlänge weather and ground conditions. According to the results shown the ground is able to store energy and the slab to ground heat flux reduces to lower value after some years.

Figure 3.24. Type1244 heat transferred results comparison.

Finally, Figure 3.25 and Figure 3.26 show the ground temperature distribution for the first and the last discretization layer at hour 8760 of the fifth year.

Figure 3.25. Type1244, layer one (0.1 m deep) temperature distribution at hour 8760 on the 5th year in Borlänge-Sweden.

Figure 3.26. Type1244, layer seven (10 m deep) temperature distribution at hour 8760 on the 5th year in Borlänge-Sweden.

Even though, Type1244 is able to model the ground thermal behavior it was found unpractical for the propose of this study. The sensitivity analysis shows that the ground temperature is not influential. Also, Type1244 increases the computational load by a factor of five. Moreover, the sensitivity analysis for the small-open-volume building showed that there is very little difference in the results from using Type1244 or the assumption that the ground temperature is equal to the ambient temperate (Tground = Tdry). That might be a consequence of the good slab insulation level assumed in this study.

For the optimization study a ground temperature equal to the ambient dry bulb temperature was assumed.

3.3.3.7 Effective sky temperature

According to the results shown in Figure 3.27 there is a minor influence of the effective sky temperature on the total yearly energy demand. The graphs depict an impact on the cooling and heating ratios instead. Similar plots were obtained for Copenhagen, Liverpool, Amsterdam, Berlin, Vienna, Bern and Rome.

Figure 3.27. Model sensitivity to effective sky temperature.

Currently there are several correlations and assumptions in use among designers to estimate the sky temperature. Figure 3.28 shows several effective sky temperature correlations used for Borlänge weather conditions on June 21. Notice the temperature differences among the correlations of up to 15 °C. The sky temperature is still a topic of discussion among the scientific community. For that reason, it is very difficult to support the selection of a particular sky temperature correlation.

Figure 3.28. Comparison among different sky temperature correlations for Borlänge-Sweden, from the 21st to 28th of June.

During the testing of the methodology proposed used to analyze the thermal behavior of the study building (See section 3.3.1), the sky temperature data provided by Meteonorm was discarded, instead the assumption that the sky temperature is equal to the ambient temperature minus five degrees centigrade was used since it gave a closer match with the measured thermal data.

For the optimization study a sky temperature equal to the ambient dry bulb temperature minus five degrees centigrade was assumed.

3.3.3.8 Weather file

There are numerous weather files to choose from in Meteonorm 6.1. There are a few different time ranges from which the average temperatures and the solar radiation are calculated. As for the sky temperature, different reference weather data files provide different values for temperature and solar radiation level for a particular point in time. However, the sensitivity analysis shows that the different weather files gives similar results. The influence of the reference weather file on the annual energy demand estimation for the large-open-volume building model in Stockholm is shown Figure 3.29. It can be seen that more recent weather data depict a reduction in the energy demand for Stockholm, maybe due to the impact of global warming.

Figure 3.29. Model sensitivity to different Stockholm reference weather data files from Meteonorm 6.1.

T61-90 means that the ambient dry bulb temperature is a temperature average from 1961 to 1990. R81-00 means that the total horizontal radiation is a radiation average from 1981 to 2000. Climate change_2030 is the projected climate change for 2030 according to Meteonorm 6.1.

3.3.3.9 Building orientation

Figure 3.30 shows the model sensitivity to the building orientation. The results show that there is a minor influence from the building orientation. For the selected building geometry, the optimal orientation is 0° at all locations. The building orientation or building azimuth angle is measured from the south direction, thus, 0° means due south and 180° means due north.

Figure 3.30. Model sensitivity to building orientation.

3.3.3.10 Indoor air temperature control strategy

Traditionally indoor control strategies are based on the indoor dry bulb temperature. Nonetheless, that approach neglects the infrared relationship that the human body has with the surrounding surfaces. An approach based on the operative temperature (Top), commonly defined as the average between the indoor dry bulb temperature (Tdry) and the indoor mean radiative temperature (Tmr), could be used instead.

Table 3.5 shows the sensitivity to the above-mentioned control strategies for two identical small-open-volume buildings with the same envelope optical properties, insulation level, and comfort setting. The table illustrates that for a small-open-volume building, the operative temperature-based control strategy gives a slightly larger total annual energy demand both for Stockholm and Madrid. The slight difference corresponds to the fact that the buildings are very

47 well insulated and the whole volume is controlled (weak stratification), thus, all the internal surface temperatures are pretty much at the same temperature, and the indoor dry bulb temperature and the mean radiative temperature are close to one another.

Also, Table 3.5 shows the sensitivity to both control strategies for two large- open-volume buildings differing only on the insulation level. The insulation levels were adapted to the local requirements for Stockholm and Madrid, respectively. The lower insulation level in Madrid causes higher internal surface temperature differences than in Stockholm, which in addition to the fact that not all the volume is air-conditioned provokes a strong indoor air stratification at daytime during the cooling days. Consequently, the mean radiative temperature fluctuates in relation to the indoor dry bulb temperature in Madrid, thus, the operative temperature based control strategy gives a higher total energy demand than in Stockholm.

Finally, the simulation results show that the temperature control based on the operative temperature increases the energy demand for both locations. However, the comfort level for human inhabitants would be improved with that control approach.

Table 3.5.Comparizon between a Tdry based and a Top based control strategy.

Small-open-volume building Large-open-volume building Calculation Energy demand Energy demand mode [kWh/(m2·y)] [kWh/(m2·y) Stockholm Madrid Stockholm Madrid

Tdry_control 150 109 81 118

Top_control 159 117 86 170

The calculations shown in Table 3.5 were made under the detail longwave calculation mode and indoor air stratification. For the small-open-volume building, the whole indoor volume (all four air nodes) is air-conditioned. For the large-open-volume building, only at the first two air nodes (located at the bottom) are air-conditioned. In the case of dry bulb temperature control, the temperatures are measured and ideally controlled at each of air node. Contrary, in the case of operative temperature control, the operative temperature is measured at chest level (air node 2) and it is ideally controlled as well.

3.3.3.11 Stratification and internal longwave radiation

The internal longwave radiation effect can only be properly simulated in models that consider the indoor air stratification. The stratification itself has a considerable impact on the total energy demand, however, the stratification additionally promotes a temperature increment of the middle and top surfaces, which in turn, promotes the internal radiative heat exchange.

TRNSYS 17 offers the possibility to approach the indoor air stratification and to use two different calculation modes for the internal longwave radiation. The first calculation mode is the standard calculation mode, which is based on the star node approach. This method uses an artificial temperature node (Tstar) to consider the parallel energy flow from a wall surface by convection to the air node, and by radiation to other wall and window elements. Area ratios are used in these calculations to find the absorption factors between all surfaces (University of Wisconsin-Madison, 2012). The second calculation mode is the detailed calculation mode, which is based on the so-called Gebhart factors. The Gebhart-Factor is defined as the fraction of the emission from a surface that reaches another surface and is absorbed. It includes all optical paths, that is, the direct paths and paths by means of one or multiple reflections. In comparison to the standard model, there is no artificial star node, because the longwave radiative heat transfer is treated separately (University of Wisconsin- Madison, 2012)

The internal longwave radiation heat transference impacts the total annual energy demand calculation; however, it is negligible for cases in which the indoor air is most of the time mixed (not stratified) or the stratification is weak. Its effect is barely tangible in well-insulated buildings, in buildings with whole- volume active cooling and heating, and in large-open-volume buildings with the active cooling and heating in the bottom volume with long heating seasons (during the heating season the air mixes up spontaneously). In general, in the before mentioned cases, the indoor air stratification is weak; since the temperature differences for both air and surfaces at different levels are small most of the year, thus, the internal longwave radiation mechanism plays a minor role and convection becomes the predominant heat transfer mechanism. In the light of that, the well-mixed assumption is acceptable for well-insulated buildings, buildings with whole-volume active cooling and heating, and for large- open-volume buildings with active cooling and heating in the bottom volume and long heating seasons.

Contrary, for poorly insulated buildings without active cooling and heating, and for large-open-volume buildings with active cooling and heating in the bottom volume and short heating seasons, the stratification and the internal radiative heat transference become determinant factors. In general, the stratification is strong in those buildings, in other words, during daytime the top and middle internal surfaces are hotter than the bottom internal surfaces, likewise, at night the non-conditioned top and middle surfaces might be colder than the conditioned ones, depending on the climate conditions. Therefore, the net radiative heat flux is not negligible, and the well-mixed assumption is no longer valid. Table 3.6 further illustrates the above mention phenomena for a building with standard envelope optical properties (high internal emittance, low TSR, and high external emittance).

Notice in Table 3.6, that for the small-open-volume building the yearly energy demand is almost the same no matter the stratification nor the longwave radiation approach. The same happens for the large-open-volume building in Stockholm due to the long heating season and high insulation levels. As said above, it is a consequence of the generally small temperature differences for

49 both air and surfaces at different levels. On the other hand, the large-open- volume building in Madrid is sensitive to the stratification but not much to the longwave radiation approach. In Madrid, the heating season is much shorter and the insulation level much lower than in Stockholm; for that reason the indoor air is stratified for a significant part of the year, but there is a considerable heating demand as well. Finally, the longwave radiation calculation mode should not be under estimated; for the presented scenarios, the stratification is not very strong all the year around; thus, the radiation plays a minor role. Contrary for scenarios with a very strong stratification most of the year, radiation would play and important role. And the detailed model would prove to be useful.

Table 3.6. Effect of the longwave calculation mode on the estimation of the total annual energy demand for the small and large open-volume building models. Small-open-volume building Large-open-volume building Calculation Total energy demand Total energy demand mode [kWh/(m2·y)] [kWh/(m2·y)] Stockholm Madrid Stockholm Madrid Standard longwave & Well-mixed 150 110 85 153 Detailed longwave & Well-mixed 150 109 85 154 Standard longwave & Stratified 154 116 81 121 Detailed longwave & Stratified 150 109 81 118

The evolution of traditional manual building design to current BES based design and even to CFD based design is quite interesting. BES year after year are more and more flexible regarding construction elements, material databank, regarding HVAC systems, regarding visual user interaction and regarding calculation power. Nowadays, BES are trying to incorporate features conventionally available only in CFD tools to increase spatial and temporal resolution. However, in the case of TRNSYS, it is not clear how it accounts for the stratification phenomena. Despite that fact, this study relies on the multi- airnode and the relatively recent included air stratification features of TRNSYS.

Data collection

Input data for the simulation studies such as thermal properties, TSR, emittance, typical warehouse geometry, and key parameter value assumptions at the different location were estimated based on governmental reports, reports from the International Energy Organization, the iNSPiRe project reports, and previous scientific studies in the area. Meteonorm weather data files were used since it is one of the most reliable and commonly used source of reference weather data in the market.

Primary data sets were collected using simulation techniques, which are commonly considered as virtual experiments (Aste et al., 2007, p.171; Sinha et al., 2001, p.84) or numerical experiments (Ljung and Glad, 2004, p.14). Thus, it could be said that the data were experimentally like collected. The selected simulation time step was one hour and the simulation time was one year with an initial stabilization period of one month.

Framework for data analysis

The simulation result data were analyzed with the help of especially VBA (Visual Basic for Applications) coded spreadsheets that automatically captured the key data and plotted the dependent variables against the independent variable for each of the simulation scenarios.

Quality assurance

The simulation data proved to be reliable and consistent. The same results were obtained every time the simulation was repeated. Additionally, the model is completely replicable by another researcher

Limitations and assumptions

Limitations

• The effect of microclimates (topography, vegetation, local climate, urban climate-heat islands) will not be considered. The best way to estimate meteorological data is by measuring on site, unfortunately, it is expensive and non-practical

• Only flat shaped roofs would be studied. The influence of the roof profile is not part of this study

• The convection heat transfer coefficients would be assumed as constants since BES cannot account for their dynamic nature

• The thermal properties of interest such as conductivity, density, and specific heat capacity are time, temperature, and moisture fluctuation dependent, and may be position or direction dependent as well if the material is non-homogeneous or anisotropic. Such dependencies were ignored and the thermophysical properties were assumed constant.

• No stochastic phenomena, as infiltrations, would be considered

• The snow effect would not be considered. The optical properties have to be treated as constant values in Type56, there is no way to dynamically

change them. Additionally, snow is a phase change material. Its analysis is out of the scope of this study

• The precipitation cooling effect will not be estimated

• Dust gathering, dirt pick, condensation will not be considered

• The increment of the special resolution greatly increases the simulation time. The number of air nodes would be limited to eight air nodes. This, also leads to the assumption of uniform construction element temperature within an air node.

• No measurements from similar buildings are available for comparative validation of the simulation results

• The accuracy of Building Energy Simulations (BES) results greatly depend on the correctness of assumptions made.

• TRNSYS is complicated simulation that requires time to be learned and expertise to be properly used.

Assumptions

• The typical meteorological year data provided by Meteonorm would be assumed as valid.

• The models envelop does not include windows nor doors since this study focuses on the envelope optical properties, and due to the fact that window and door sizes and technologies vary a lot from country to country.

• The building internal gains are not represented in detail. Even though internal gains are influential, for this study the focus is on the envelope optical properties.

• The door/gate opening rate is an influential but difficult to estimate stochastic factor for infiltrations. The developed models may underestimate the infiltration rates.

• The ground thermal properties for the selected locations provided by the project Thermomap (2015) will be assumed as valid for this study

• Any kind of façade shading will be considered

• Longwave radiation from other buildings will not be considered.

• Ideal heating and cooling would be implemented. The ideal heating and cooling has constant performance parameters and does not have a real

physical location inside the zone to have, e.g. view factor dependency or conduction to certain wall (Joudi et al., 2013, p.564)

• This study will assume that the U-values for large-open-volume buildings across Europe vary in the same way as the U-value for office buildings do. Thus, the office building U-values presented in the iNSPiRe project would be used as a reference. It is worth to mention that the U-values of old buildings relate to those at the time of construction and are based on literature references. Those U-values do not consider the effect of renovations or upgrades (Inspire, 2014c, p.22). Therefore, this study will focus on post year 2000 U-values (See Appendix 1). The average mayor renovation interval in Europe is about 30 – 40 years (International Energy Agency 2008, p.12), which means buildings constructed after 2000 have not been renovated yet

• Under this study, it is assumed that a quasi-homogeneous group of new industrial buildings is found in Europe. Archiscapes (2014) denotes that, over time, construction materials and technologies changed both for the residential and industrial buildings. However, the structure of the warehouse held up in its core aspects: the structural pattern, wide interiors, and large windows are the same, and so are the free design rectangular plan and the pitched roof.

4 Results and Analysis

Once the variations in detailed design depending on building standards at the selected locations were examined, the thermal model for the large-open- volume buildings was created, and the key parameter value assumptions were defined; several simulations were run to analyze how the total annual energy used to maintain indoor conditions vary with the building envelope optical properties, in other words, the sensitivity of the total energy demand used to keep the thermal comfort settings to the solar radiation was studied. For that propose, the large-open-volume building model was set to dry bulb temperature control mode, indoor air stratification approach, and detailed longwave radiation calculation mode. As stated in section 3.3.1, the key parameter value assumptions used for this study were partially derived from the efforts to match the small-open-volume building simulation results with field passive measurements. Table 4.1 shows some of those key parameter value assumptions and the temperature control setting for this study. Notice that the selected density-driven flow rates force the simulation model to an almost homogeneous volume temperature during the heating season; which allows a better estimation of the heating loads.

Table 4.1. Primary set of key parameter values for the optical properties optimization analysis.

Key parameter Value assumption

Effective sky temperature Tdry - 5 °C

Ground temperature Tdry

Ground surface temperature Tdry Infiltrations and ventilation rate 0.3 Air changes per hour 1000 ACH during the heating season Density driven flow rate 20 ACH during the cooling season Internal convective heat transfer coefficient_ROOF 11 kJ/(h·m²·K) Internal convective heat transfer coefficient_WALL 11 kJ/(h·m²·K) Internal convective heat transfer coefficient_FLOOR 1 kJ/(h·m²·K) External convective heat transfer coefficient_ROOF 32 kJ/(h·m²·K) External convective heat transfer coefficient_WALL 32 kJ/(h·m²·K) External convective heat transfer coefficient_FLOOR 0.001 kJ/(h·m²·K) Dry bulb temperature setting_HEATING 19 °C Dry bulb temperature setting_COOLING 21 °C

Additionally, the initial optical properties were set as follows: 0.1 TSR, 0.9 internal emittance, 0.9 external emittance. The study confirmed that there are optical properties values that minimize the energy demand. Figure 4.1 and Figure 4.2 show the optimal optical values under the above-mentioned assumptions, varying the optical properties one at a time (independent of one another) from the initial values, and under the simulation tool limitations for Stockholm and Madrid respectively. Notice that for Stockholm the total annual energy demand shows a minor sensitivity to the external and internal emittances, thus the savings from the selection of the optimal emittance values

54 are negligible. Contrary, for Madrid the total energy demand is sensitive to the external emittance.

Figure 4.1. Large-open-volume building envelope optimal optical properties for Stockholm. The yellow arrow indicates the optimal values.

Figure 4.2. Large-open-volume building envelope optimal optical properties for Madrid. The yellow arrow indicates the optimal values.

According to the graphic results shown in Figure 4.1 and Figure 4.2 the TSR has a small influence on the total energy demand for well-insulated buildings, which is the case for Stockholm, Copenhagen, Liverpool, Amsterdam, Berlin, Vienna, Bern, and Vienna. Madrid show more sensitivity to the TSR, due to their lower insulation level and to the relatively high cooling loads. Additionally, the graphic results show that for locations with cooling demands in the same order of magnitude as the heating demand, a high TSR helps to reduce the total energy demand, as seen for Madrid. Contrary for locations with dominant heating demands a low TSR helps to reduce the total energy demand.

The external emittance optimal value behaves in a similar fashion as the TSR does. For locations with dominant heating demands a low external emittance helps to reduce the energy demand, and for locations with a cooling demand in the same order of magnitude as the heating demand or higher, a high external emittance leads to energy savings.

The optimal internal emittance for both Stockholm and Madrid tends to a value smaller than 0.9. Large-open-volume buildings tend to be high, which favors the air stratification and in turn favors the radiative heat flux from the non- airconditioned surfaces to the airconditioned surfaces and the occupants during daytime. At night, the nonconditioned surfaces might become radiative heat sinks. Internal surfaces with low emittance act like a radiative barrier, further limiting the heat flux through the envelop, in other words, low emissive surface increase the insulation level. In section 3.3.3.5 the sensitivity of the model to insulation level was study, and it was concluded that high insulation levels help to reduce the total annual energy demand for all locations. In the same section, it was also mentioned that the reduction in the heat flux and increase of the insulation thickness does not have a linear relationship. There is an optimal insulation thickness regarding the material used (Joudi, 2015).

Further analysis indicated that the optimal optical properties depend on the key parameter value assumptions. A sensitivity study performed as well under dry bulb temperature control mode, indoor air stratification approach, and detailed longwave radiation calculation mode, but under different key parameter value assumptions conducted to slightly different optimal optical properties, however, the proportionality between the heating and the cooling energy demands did not change much at the selected locations.

In order to proceed with the research, the hypothesis that “The optimal optical properties are strongly related to the ratio between the heating and the total energy consumption” would be introduced. To further explore the presented hypothesis the following optics thought experiment is proposed. Imaging two identical buildings located one hundred meters away from each other. One of the buildings is a refrigerated goods warehouse and the other is an ore melting facility. Any solar gain conducted through the building envelope is disadvantageous for the refrigerated goods warehouse, thus, it is logical to think that a high TSR, high external emissive surfaces, and low internal emissive surfaces would be optimal for the refrigerated goods warehouse. On the other hand, solar gains conducted through the building envelope would be beneficial for the ore melting facility, therefore, it is logical to think that a low TSR, low external emissive surfaces, and low internal emissive surfaces would be optimal for the food drying facility. For the refrigerated goods warehouse the cooling load is much higher than the heating load, consequently, the Qheat to Qtotal ratio tends to zero and the optimal TSR would tend to one. Contrary, for the food drying facility the heating load is much higher than the cooling load, consequently, the Qheat to Qtotal ratio tends to infinite and the optimal TSR would tend to zero.

Based on the though experiment, the standard longwave radiation and the detailed longwave radiation calculation modes were examined to compare its estimations for the Qheat to Qtotal ratios. Table 4.2 illustrates the results.

Table 4.2. Effect of the longwave calculation mode on the estimation of the Qheat to Qtotal ratio for the small and large open-volume building models.

Calculation Small-open-volume building Large-open-volume building mode Qheat/Qtotal Qheat/Qtotal Stockholm Madrid Stockholm Madrid Standard longwave & Well-mixed 0.89 0.49 0.86 0.36 Detailed longwave & Well- mixed 0.89 0.49 0.86 0.36 Standard longwave & Stratified 0.88 0.48 0.89 0.50 Detailed longwave & Stratified 0.89 0.49 0.89 0.45

As it can be seen from Table 4.2, both the standard and the detailed longwave radiation calculation modes give equivalent Qheat/Qtotal results. Therefore, the optimization tool GenOpt, which cannot run under the detailed longwave calculation mode, could be used under the standard longwave calculation mode to further study the sensitivity of the envelope optimal optical properties to the heating to total energy consumption ratio. Table 4.3 shows the GenOpt optimization results and an energy saving estimation. GenOpt creates a matrix of combinations and changes the three independent variables at once. Notice the difference with the initial results shown in Figure 4.1 and Figure 4.2.

Table 4.3 GenOpt results for the selected locations

Case study optimal optical properties Post-optimization Pre-optimization Location External Internal total energy demand total energy demand Energy savings Reflectance emittance emittance [kWh/(m2·y)] [kWh/(m2·y)] Stockholm 0.45 0.00 0.70 80 84 5% Copenhagen 0.45 0.00 0.65 82 85 4% Liverpool 0.60 0.00 0.65 62 66 7% Berlin 0.70 0.00 0.65 87 91 5% Amsterdam 0.60 0.00 0.65 66 70 6% Vienna 0.90 0.10 0.60 77 82 7% Bern 0.60 0.00 0.65 85 88 4% Rome 1.00 0.05 0.60 71 97 37% Madrid 0.95 0.05 0.60 99 120 21%

The results in Table 4.3 confirm the trend of the previous results and provide a better answer to the initial research question.

Later, GenOpt was coupled to TRNSYS 17, and the large-open-volume building model was set to dry bulb temperature control mode, indoor air stratification approach, standard longwave radiation calculation mode, and the primary set of key parameter values assumptions for the optical properties optimization analysis shown in Table 4.1 was used. For Stockholm and Madrid, the dry bulb

58 temperature settings were changed to obtained different Qheat to Qtotal ratios and the corresponding optimal optical properties values. Figure 4.3 and Table 4.4 depict the results for Stockholm and Figure 4.4 and Table 4.5 depict the results for Madrid.

Figure 4.3. Large-open-volume building GenOpt optimization results for Stockholm

Table 4.4. Large-open-volume building GenOpt optimization results data for Stockholm

Dry bulb Dry bulb Total energy Heating Cooling Total energy Tset for Tset for External Internal demand demand demand demand Q /Q TSR heating colling heat total emittance emittance post optimization [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [°C] [°C] [kWh/(m2·y)] -1 1 1 37 37 0.01 1.00 0.25 0.52 37 9 11 7 13 20 0.33 1.00 0.21 0.55 20 14 16 14 6 20 0.69 1.00 0.06 0.60 20 16 18 18 4 22 0.81 0.95 0.18 0.56 22 17 19 20 3 24 0.85 0.85 0.00 0.66 23 18 20 22 3 25 0.89 0.67 0.00 0.81 25 19 21 25 2 27 0.92 0.37 0.00 0.60 26 20 22 27 2 29 0.95 0.11 0.00 0.60 28 21 23 30 1 31 0.96 0.00 0.00 0.49 30 22 24 32 1 33 0.98 0.00 0.00 0.56 31 23 25 35 1 36 0.99 0.00 0.00 0.62 33 24 26 38 0 38 0.99 0.00 0.00 0.61 35 25 27 41 0 41 1.00 0.00 0.00 0.61 38 29 31 53 0 53 1.00 0.00 0.00 0.61 48

Figure 4.4. Large-open-volume building GenOpt optimization results for Madrid

Table 4.5. Large-open-volume building GenOpt optimization results data for Madrid

Dry bulb Dry bulb Total energy Heating Cooling Total energy Tset for Tset for External Internal demand demand demand demand Q /Q TSR heating colling heat total emittance emittance post optimization [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [°C] [°C] [kWh/(m2·y)] 6 8 0 57 58 0.00 1.00 0.21 0.55 47 14 16 5 29 34 0.14 1.00 0.19 0.56 27 16 18 7 24 31 0.23 1.00 0.08 0.63 25 19 21 12 17 29 0.43 1.00 0.02 0.60 26 20 22 15 15 29 0.50 0.94 0.04 0.68 27 21 23 17 13 30 0.56 0.81 0.00 0.73 28 22 24 19 11 30 0.63 0.68 0.00 0.79 29 23 25 22 10 31 0.69 0.63 0.00 0.76 31 24 26 24 8 33 0.75 0.53 0.00 0.66 32 25 27 27 7 34 0.79 0.43 0.00 0.62 33 26 28 30 6 36 0.84 0.32 0.00 0.53 35 27 29 33 5 38 0.87 0.23 0.00 0.53 37 28 30 36 4 40 0.90 0.13 0.00 0.57 38 29 31 40 3 43 0.93 0.03 0.00 0.51 40 30 32 43 2 46 0.95 0.00 0.00 0.52 42

The results shown in Figure 4.3 and Figure 4.4 are in line with the hypothesis that “The optimal optical properties are strongly related to the ratio between the heating and the total energy consumption”. Notice that the TSR curve decay rate for Stockholm differs from the decay rate in Madrid. It is related to the insulation level at the two locations. High insulation levels reduce the thermal coupling between the envelope external and internal surfaces; thus, hindering the benefits of the optical selectivity. Such a graph could help to rapidly estimate the optimal optical properties if the Qheat and Qcool are known and if the graph is adequate for the building size, shape, insulation level and local weather.

Given the tiny energy saving for the large-open-volume building model at the most of selected locations, it was decided to study the advantages of selective building envelope surfaces in all the world capitals. The biggest limitation was

60 the lack of building thermal characteristics at the locations. However, to some extent the influence of the envelop optical properties could be studied for two reference building differing only on the envelop optical properties. The total energy demand calculation from such study would not be representative for all locations. Nonetheless, the relative energy savings between the reference buildings would be indicative of the potential savings at each location.

The insulation level of the three-studied small-open-volume buildings; named Cabin A, Cabin B, and Cabin C, is comparable with the post year 2000 Nordic insulation level defined in the iNSPiRe project (See Appendix 1). The optical properties differ among them. Cabin A has low interior emittance, low total solar reflectance (TSR), and high exterior emittance. Cabin B has low interior emittance, high TSR, and high exterior emittance. Cabin C has standard optical properties, high interior and exterior emittance, and low TSR. The three small- open-volume buildings were simulated for 243 locations and the results from Cabins A and B were compared with those of Cabin C. The results show good energy saving potential for most of the locations (See Appendix 2). The Nordic insulation levels are one of the highest in the world. A similar study including local insulation level would lead to even bigger energy saving from the smart selection of the envelop optical properties. On this particular, Joudi (2015, p. 66) states that the influence of the optical properties on the total heat flux is considerably higher with thinner insulation thickness for Amsterdam climate. The same study for the large-open-volume building would lead to similar results.

This technology has enormous energy saving potential for many locations. It could also be used to help to reduce the thermal insulation level. In other words, a reduction in the insulation level could be compensated with an optimized optical selective envelope, thus, possibly reducing the construction time and most probably the construction cost as well.

Finally, it is worth to mention that building simulation is a very complex activity implicating a significant effort, which could be worthy, or not. The complexity of this study requires CFD tools for a better representation of the indoor density driven mass flows and the convective heat transfer mechanism. Additionally, field measurements for all types of buildings along with crystal clear boundary conditions are needed to guide modelers in their endeavors. Unfortunately, buildings energy simulations greatly depend on too many assumptions and on the modeler expertise. Therefore, the presented results might not completely account for studied phenomena. Nonetheless, the presented energy saving trends from the envelope optimal optical properties selection are irrefutable.

5 Discussion and conclusions

It was possible to map the envelope external optimal optical properties minimizing the energy use to the selected geographical locations and building type. It was a complex task, as the optimal optical properties of the envelope are strongly related to the internal heating and cooling loads of the building. BES alone might not always provide accurate load estimations, since there are many influential assumptions involved in the load estimation. Moreover, phenomena as infiltrations partially depend on occupant interaction (e.g random door and window openings), which gives them a statistical component. And finally, the cultural thermal preferences, different temperature settings also constitute a major statistical obstacle. At the moment, there is no building simulation tool able to account for all these parameters. Thus, CFD and experimentation are also necessary in order to further explore this topic in detail.

The regional variations in building design, in particular, the insulation level variations are critical for the estimation of the optimal optical properties. The general lack of building thermal information, and the many different ways in which the information is presented among the many different standards constitutes a mayor hinder for this study. However, for European locations there are insulation data recollected by the Inspire project group, which allowed to compare the regional and historical variations of the insulation levels.

The transient thermal behavior of large-open-volume buildings can be approached with BES. The conception of a method considering the key variables for the estimation of the building thermal performance, under simplistic assumptions, is the most important outcome of this study. The solution of a complex problem does not have to be completely accurate to be acceptable. A trend, an approximation, might be a good cost-effective approach. The lack of accurate input information and the computational limitations undoubtedly affect the accuracy of the method, however, if given all the necessary data to avoid the critical assumptions, the uncertainty of the results would be significantly reduced. The results of this study indicate that the optimal optical properties are defined by the ratio between the heating and cooling loads. BES cannot calculate in great detail the total annual energy demand, however the ratio between the two could be estimated.

At the selected locations, the total annual energy use to maintain indoor conditions could be reduced with the smart choice of building envelope surface optical properties depending on the location. However, for hot climates and less insulated buildings the energy savings are much bigger. The benefits of the building envelop optical selectivity are reduced as the insulation level is increased.

This technology has proven enormous potential for energy reduction at some locations in the world. As stated by Joudi (2015, p 68) there is a strong interaction of building surface optical properties and internal load of the building. Building envelopes with selective surfaces could help to improve the way we

62 live and to meet up the challenges impose. by the already being felt climate changes.

Further studies

Validate the presented model versus field measurements.

Couple BES and CFD to compare results with those of BES.

Examine the optimal optical properties and the influence of the conditioning equipment efficiency.

Study the optical influence of snow and dust accumulations.

Investigate the relationship between building size (Surface area to Volume ratio) and the optimal optical properties.

For future studies the following set of requirements is recommended. • BES tool capable of • Determination of cooling demand. • Determination of heating demand. • Possibility to change insulation values. • Possibility to change building envelope external optical properties. • Possibility to change location (climate data). • Possibility to a stratified air volume assumption. • Possibility to perform multi-zone analysis. • Excellent parametrization features (TRNSYS is limited on this regard) • Get familiar with the simulation tool computationally, mathematically and statistically • Get familiar with the physics behind the simulation tool (heat transferences and fluid dynamics) • CFD tool for the transient estimation of the convective heat transfer coefficients. The CFD tool should couple to the BES tool • Optimization tool able to estimate all possible combinations among the independent variable, and to run automatic simulations for complex models • Data management tool (excel 2016) for data analysis and dynamic graphic generations • Intermediate VBA coding skills • PC CORE i3 as minimum

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Appendix 1

Regional and historical U-value variations in the studied locations

Nordic climate Northern continental climate Continental climate Oceanic climate

Southern continental climate Mediterranean climate Southern dry climate

Figure A 1. Regional and historical U-value variations in the studied locations (Inspire, 2014c).

Appendix 2

Small-open-volume building projected energy savings around the world from the smart choice of envelope optical properties

Table A 1 shows the small-open-volume building relative total energy savings from using improved optical properties compared to standard optical properties. Cabin A has low interior emittance, low total solar reflectance (TSR) and high exterior emittance. Cabin B has low interior emittance, high TSR and high exterior emittance. Cabin C has standard optical properties, high interior and exterior emittance, and low TSR. The table illustrates that Cabin B saves between 5 to 33% of the yearly total energy consumption for 182 of the 243 world capital cities, where approximately one third of the world population lives. Unfortunately, for most of the European locations the energy savings are not significant, except for southern European locations.

Table A 1. Small-open-volume building energy around the world from the smart choice of the optical properties. Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 1 Abkhazia Sukhumi 100 94 99 -1 5 2 Afghanistan Kabul 115 106 113 -2 7 Episkopi 3 Akrotiri and Dhekelia Cantonment 101 82 100 -2 18 4 Albania Tirana 116 108 114 -1 5 5 Algeria Algiers 208 209 208 0 -1 6 American Samoa Pago Pago 141 117 141 0 17 7 Andorra Andorra la Vella 155 166 154 -1 -8 8 Angola Luanda 123 101 123 0 18 9 Anguilla The Valley 144 121 145 0 17 10 Antigua and Barbuda St. John's 140 118 141 0 16 11 Argentina Buenos Aires 79 64 77 -2 17 12 Armenia Yerevan 129 125 128 -1 3 13 Aruba Oranjestad 159 134 160 0 16

Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 14 Ascension Island Georgetown 125 100 125 0 20 15 Australia Canberra 100 91 97 -3 7 16 Austria Vienna 121 116 120 -1 3 17 Azerbaijan Baku 123 116 122 0 5 18 Bahamas Nassau 116 92 116 0 21 19 Bahrain Manama 154 131 153 0 15 20 Bangladesh Dhaka 124 102 123 0 18 21 Barbados Bridgetown 148 125 148 0 16 22 Belarus Minsk 160 159 159 0 0 23 Belgium Brussels 110 109 109 -1 0 24 Belize Belmopan 137 115 137 0 16 25 Benin Porto-Novo 134 115 134 0 14 26 Bermuda Hamilton 84 65 83 -1 22 27 Bhutan Thimphu 132 138 130 -1 -6 28 Bolivia La Paz 108 109 105 -3 -4 29 Bosnia and Herzegovina Sarajevo 133 133 132 -1 -1 30 Botswana Gaborone 103 80 101 -2 21 31 Brazil Brasília 82 61 81 -1 25 32 British Virgin Islands Road Town 126 103 126 0 18 Bandar Seri 33 Brunei Begawan 138 118 138 0 15

Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 34 Bulgaria Sofia 131 129 130 -1 1 35 Burkina Faso Ouagadougou 172 147 172 0 15 36 Burundi Bujumbura 92 72 92 0 22 37 Cabo Verde Praia 102 78 102 0 24 38 Cambodia Phnom Penh 163 142 163 0 13 39 Cameroon Yaoundé 84 63 84 0 25 40 Canada Ottawa 165 166 164 0 -1 41 Cayman Islands George Town 154 132 155 0 15 42 Central African Republic Bangui 116 96 116 0 17 43 Chad N'Djamena 173 148 173 0 15 44 Chechnya Grozny 130 126 129 0 3 45 Chile Santiago 95 84 92 -3 9 46 China Beijing 133 129 132 -1 2 47 Christmas Island Flying Fish Cove 119 96 119 0 20 48 Cocos (Keeling) Islands West Island 144 120 144 0 17 49 Colombia Bogotá 68 64 65 -4 2 50 Comoros Moroni 112 91 112 0 19 51 Cook Islands Avarua 106 84 106 0 21 52 Costa Rica San José 74 53 73 -1 28 53 Côte d'Ivoire Yamoussoukro 130 110 130 0 16

Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 54 Croatia Zagreb 119 114 119 -1 4 55 Cuba Havana 110 88 110 0 20 56 Curaçao Willemstad 167 143 167 0 15 57 Cyprus Nicosia 107 87 106 -2 17 58 Czech Republic Prague 136 137 136 0 -1 Democratic Republic of the 59 Congo Kinshasa 108 89 107 0 17 60 Denmark Copenhagen 133 134 132 0 -1 61 Djibouti Djibouti 91 69 89 -2 22 62 Dominica Roseau 91 67 90 0 26 63 Dominican Republic Santo Domingo 119 100 119 0 16 64 East Timor Dili 108 85 108 0 21 65 Easter Island Hanga Roa 66 47 66 -1 28 66 Ecuador Quito 62 56 60 -5 6 67 Egypt Cairo 111 90 110 -1 18 68 El Salvador San Salvador 87 65 86 -1 25 69 Equatorial Guinea Malabo 118 100 119 0 16 70 Eritrea Asmara 83 59 81 -2 27 71 Estonia Tallinn 165 166 164 0 -1 72 Ethiopia Addis Ababa 67 51 63 -5 20 73 Falkland Islands Stanley 135 142 134 0 -6

Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 74 Faroe Islands Tórshavn 142 150 142 0 -6 75 Federated States of Micronesia Palikir 137 116 137 0 16 76 Fiji Suva 100 80 99 0 19 77 Finland Helsinki 169 170 169 0 -1 78 France Paris 103 101 102 -1 2 79 French Guiana Cayenne 122 102 122 0 17 80 French Polynesia Papeete 131 106 132 0 19 81 Gabon Libreville 113 94 113 0 17 82 Gambia Banjul 179 153 179 0 14 83 Georgia Tbilisi 124 121 123 -1 2 84 Germany Berlin 134 133 133 -1 0 85 Ghana Accra 137 117 138 0 15 86 Gibraltar Gibraltar 72 54 70 -3 24 87 Greece Athens 96 83 94 -1 13 88 Grenada St. George's 137 114 137 0 16 89 Guadeloupe Basse-Terre 115 93 115 0 19 90 Guam Hagåtña 142 121 142 0 15 91 Guatemala Guatemala City 63 44 61 -4 29 92 Guernsey St. Peter Port 86 85 85 -1 0 93 Guinea Conakry 138 116 138 0 16

Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 94 Guinea-Bissau Bissau 143 120 143 0 16 95 Guyana Georgetown 124 103 124 0 17 96 Haiti Port-au-Prince 140 117 141 0 17 97 Honduras Tegucigalpa 79 59 78 -1 24 98 Hong Kong Hong Kong 98 83 97 0 15 99 Hungary Budapest 130 126 129 -1 3 100 Iceland Reykjavík 154 163 154 0 -5 101 India New Delhi 145 123 144 -1 15 102 Indonesia Jakarta 141 121 141 0 15 103 Iran Tehran 131 118 130 -1 9 104 Iraq Baghdad 123 110 122 -1 10 105 Ireland Dublin 101 104 100 -1 -4 106 Isle of Man Douglas 107 110 106 -1 -4 107 Israel Jerusalem 111 93 109 -2 14 108 Italy Rome 97 84 95 -2 12 109 Jamaica Kingston 146 125 146 0 14 110 Japan Tokyo 92 86 91 -1 5 111 Jersey St. Helier 89 88 88 -1 0 112 Jordan Amman 108 91 107 -2 15 113 Kazakhstan Astana 207 208 207 0 -1

Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 114 Kenya Nairobi 60 43 57 -4 25 115 Kiribati Tarawa Atoll 150 125 150 0 16 116 Kosovo Pristina 126 122 124 -1 2 117 Kuwait Kuwait City 165 147 165 0 11 118 Kyrgyzstan Bishkek 136 132 136 -1 3 119 Laos Vientiane 140 118 140 0 16 120 Latvia Riga 156 156 155 0 0 121 Lebanon Beirut 91 72 90 -1 20 122 Lesotho Maseru 106 87 102 -3 15 123 Liberia Monrovia 118 97 118 0 18 124 Libya Tripoli 97 79 95 -1 17 125 Liechtenstein Vaduz 136 140 135 -1 -3 126 Lithuania Vilnius 159 160 159 0 -1 127 Luxembourg Luxembourg 131 131 130 -1 -1 128 Madagascar Antananarivo 71 51 68 -4 25 129 Malawi Lilongwe 92 70 90 -1 23 130 Malaysia Kuala Lumpur 136 118 137 0 14 131 Maldives Malé 149 125 150 0 16 132 Mali Bamako 155 131 155 0 15 133 Malta Valletta 89 71 88 -2 19

Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 134 Marshall Islands Majuro 138 117 138 0 15 135 Martinique Fort-de-France 116 94 116 0 20 136 Mauritania Nouakchott 128 105 128 0 18 137 Mauritius Port Louis 105 82 105 0 22 138 Mayotte Mamoudzou 117 96 117 0 18 139 Mexico Mexico City 65 52 62 -5 17 140 Moldova Chișinău 138 134 138 -1 2 141 Monaco Monaco 88 77 86 -2 10 142 Mongolia Ulaanbaatar 240 247 240 0 -3 143 Montenegro Podgorica 110 99 108 -1 9 144 Montserrat Plymouth 114 91 114 0 20 145 Morocco Rabat 74 57 72 -3 21 146 Mozambique Maputo 103 81 103 0 21 147 Myanmar Naypyidaw 124 102 124 0 18 148 Nagorno-Karabakh Republic Stepanakert 126 121 125 -1 3 149 Namibia Windhoek 113 88 111 -2 21 150 Nauru Yaren 155 132 155 0 15 151 Nepal Kathmandu 103 83 101 -2 18 152 Netherlands Amsterdam 105 105 104 -1 -1 153 New Zealand Wellington 84 85 85 0 0

Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 154 Nicaragua Managua 144 122 144 0 15 155 Niger Niamey 170 148 171 0 13 156 Nigeria Abuja 126 103 126 0 19 157 Niue Alofi 126 102 127 0 19 158 Norfolk Island Kingston 64 42 62 -3 32 159 North Korea Pyongyang 143 144 143 0 -1 160 Norway Oslo 152 154 151 0 -2 161 Oman Muscat 169 145 169 0 15 162 Pakistan Islamabad 127 108 126 -1 14 163 Panama Panama City 136 116 136 0 15 164 Papua New Guinea Port Moresby 142 118 142 0 17 165 Paraguay Asunción 103 85 102 -1 17 166 Peru Lima 65 44 63 -3 31 167 Philippines Manila 147 128 147 0 13 168 Pitcairn Adamstown 94 72 94 0 24 169 Poland Warsaw 144 143 143 0 0 170 Portugal Lisbon 73 59 71 -3 18 171 Puerto Rico San Juan 134 112 135 0 17 172 Qatar Doha 155 135 155 0 13 173 Republic of Macedonia Skopje 139 120 139 0 14

Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 174 Republic of the Congo Brazzaville 109 90 109 0 17 175 Réunion Saint-Denis 105 82 105 0 22 176 Romania Bucharest 134 129 133 -1 3 177 Russia Moscow 182 183 182 0 -1 178 Rwanda Kigali 66 48 65 -3 26 179 Saint Barthélemy Gustavia 147 124 148 0 16 180 Saint Helena Jamestown 54 35 52 -5 33 181 Saint Kitts and Nevis Basseterre 144 121 144 0 16 182 Saint Lucia Castries 148 125 148 0 16 183 Saint Martin Marigot 139 115 139 0 17 184 Saint Pierre and Miquelon St. Pierre 148 155 147 0 -5 Saint Vincent and the 185 Grenadines Kingstown 142 119 142 0 16 186 Samoa Apia 158 134 159 0 15 187 San Marino San Marino 101 94 100 -1 6 188 São Tomé and Príncipe São Tomé 77 59 77 0 24 189 Saudi Arabia Riyadh 169 145 168 0 14 190 Senegal Dakar 119 95 119 0 21 191 Serbia Belgrade 119 114 118 -1 4 192 Seychelles Victoria 133 114 133 0 15 193 Sierra Leone Freetown 140 119 140 0 15

Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 194 Singapore Singapore 123 104 123 0 15 195 Sint Maarten Philipsburg 139 116 139 0 17 196 Slovakia Bratislava 130 126 129 -1 2 197 Slovenia Ljubljana 124 121 123 -1 2 198 Solomon Islands Honiara 142 118 142 0 17 199 Somalia Mogadishu 145 125 145 0 14 200 Somaliland Hargeisa 133 107 133 0 19 201 South Africa Pretoria 87 67 84 -3 21 South Georgia and the South 202 Sandwich Islands King Edward Point 249 267 250 0 -7 203 South Korea Seoul 118 116 118 0 1 204 South Ossetia Tskhinvali 128 125 127 -1 1 205 South Sudan Juba 96 74 95 -1 22 206 Spain Madrid 111 100 109 -1 8 Sri Jayawardenepura 207 Sri Lanka Kotte 144 122 144 0 16 208 Sudan Khartoum 198 172 199 0 14 209 Suriname Paramaribo 125 104 125 0 17 210 Swaziland Mbabane 84 65 82 -3 20 211 Sweden Stockholm 150 151 150 0 0

Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 212 Switzerland Bern 126 125 125 -1 0 213 Syria Damascus 122 105 120 -2 12 214 Taiwan Taipei 79 67 79 -1 15 215 Tajikistan Dushanbe 129 119 128 -1 7 216 Tanzania Dodoma 69 47 67 -2 30 217 Thailand Bangkok 165 144 165 0 13 218 Togo Lomé 135 115 135 0 15 219 Tonga Nukuʻalofa 119 95 119 0 20 220 Transnistria Tiraspol 139 135 139 0 3 221 Trinidad and Tobago Port of Spain 122 102 122 0 17 Edinburgh of the 222 Tristan da Cunha Seven Seas 74 73 72 -3 -2 223 Tunisia Tunis 101 84 100 -2 16 224 Turkey Ankara 128 124 127 -1 2 225 Turkmenistan Ashgabat 135 124 134 -1 7 226 Turks and Caicos Islands Cockburn Town 146 123 146 0 16 227 Tuvalu Funafuti 162 139 162 0 14 228 Uganda Kampala 71 52 69 -2 25 229 Ukraine Kiev 149 148 149 0 1 230 United Arab Emirates Abu Dhabi 165 141 165 0 14 231 United Kingdom London, 94 93 93 -1 0

Relative Relative Energy Energy Energy savings savings demand demand demand Item Country Capital City CABIN CABIN CABIN A CABIN B CABIN C A B [kWh/(m2·y)] [kWh/(m2·y)] [kWh/(m2·y)] [%] [%] 232 United States Washington 116 108 114 -1 5 233 United States Virgin Islands Charlotte Amalie 141 118 142 0 16 234 Uruguay Montevideo 77 66 75 -2 13 235 Uzbekistan Tashkent 136 125 135 -1 7 236 Wallis and Futuna Mata-Utu 144 121 145 0 16 237 Vanuatu Port Vila 115 91 115 0 21 238 Vatican City Vatican City 101 89 100 -1 11 239 Venezuela Caracas 85 66 85 0 22 240 Vietnam Hanoi 97 83 97 0 14 241 Yemen Sana'a 100 76 98 -2 23 242 Zambia Lusaka 85 63 83 -2 24 243 Zimbabwe Harare 76 56 73 -3 24

Scale 33% Energy reduction

Figure A 2. Possible total annual energy saving for the both sides reflective cabin (Cabin B) compared to a standard cabin.

Scale

5% Energy reduction

Figure A 3. Possible total annual energy saving for the internal reflective cabin (Cabin A) compared to a standard cabin.

Scale

33% Energy reduction

1.36 billion people

Figure A 4. Possible total annual energy saving for the both sides reflective cabin (Cabin B) compared to a standard cabin versus country population.