Energy Performance of a University Campus in Norway

ENERGY PERFORMANCE OF A UNIVERSIT Y CAMPUS IN NORWAY

SUSTAINABLE STRATEGIES AND DESIGN SOLUTIONS TO REDUCE ENERGY CONSUMPTIONS

Martina Bianchi

Master Thesis in Building Università degli Studi di Genova Engineering and Architecture NTNU, Trondheim - ZEN Reaseach Centre 2018/2019

“Intelligence is the ability to adapt to change.” Stephen William Hawking

ABSTRACT

Today more then ever cities have a fundamental role not only from the design point of view but also from the social and economic one. In a century in which “urbanization” has a leading part, it is becoming more and more crucial to start toward a sustainable approach. Cities have to guarantee not only the quality of life for the inhabitants but also a low environmental impact which does not affect the needs of the future generations. For this purpose, lot of cities in the world are reorganizing and rethinking them- selves with the aim of becoming more smart and adapting to changes that could not be reversible. In an historical period in which buildings sector produces the main part of the global emissions and uses about the 40% of the energy source, the attention to the energy behaviour of the construction has assumed an essential importance. For existing buildings the energy simulation has two different advantages: to evaluate the current energy status and their improvement as a result of eventual inter- ventions. Energy simulation has increasingly taken on a dynamic characteristic and today is a valid tool to implement the existing built. Recently developed tools give the opportunity to estimate the energy behaviour of entire neighbourhoods and cities, giving the chance to evaluate the situation from a global and completely new point of view. The totalitarian approach and not focused on the single building, could be revolutionary and decisive for many cities that are not able to guarantee and pursue the goals regarding the sustainability. TABLE OF CONTENTS

Abbreviations and glossary 1 List of figures 2 List of tables 9 Introduction 10

Chapter 1 – Focus on sustainability 12 • Concept of Sustainability and background 14 • Sustainability and buildings 17

Chapter 2 – ZEN Research Centre 22 • The Research Centre on Zero Emission Neighbourhoods 24 in Smart Cities – FME, ZEN Definition of ZEN Research Centre 24 Goals of ZEN Research Centre 24 Numbers of ZEN Research Centre 24 Partners of ZEN Research Centre 25 Pilot project of ZEN Research Centre 25 Organisation of ZEN Research Centre 26 Background of ZEN Research Centre 28 ZEB and nZEB 29 From ZEB to ZEN 29 Zero Emission Neighbourhoods: definition and approach 31 • Norway 33 Trondheim 33 Climate 34 NTNU - Norwegian University of Science and Technology 35 Strategic research areas 36 Campuses in Trondheim 36 Students at NTNU 37 Students’ life 37 Gløshaugen campus 39 location and accessibility 39 Facilities 40 Boundaries and buildings’ use 41 Elevation of the terrain 42 Historical background 42 Gløshaugen campus’ renovation project 43 Chapter 3 – Background and definition 44 • Energy requirements for buildings 48 Past energy requirements 48 Current energy requirements 48 Future energy requirements 49 • LCA, LCEA, LCCA 50 Embodied, operational and demolition energy/emissions 51 • Low energy buildings 54 Passive techniques 54 Low embodied energy materials 54 The importance of systems and renewable energy 56

Chapter 4 – Energy modelling at urban scale 58 • Systems boundaries 60 • Assessment criteria and key performance indicators 61 • Categories 62 • Energy modelling and building simulation 68 Reference buildings 70

Chapter 5 – Tools for energy modelling at urban scale 72 • IESVE 74 • CityBES 75 • UMI 75 • CitySIM 77 • CEA 78 • Chapter 6 – Energy modelling with the software UMI 80 • Context analysis 82 Mapping 83 Blocking 84 Numbering 86 Grouping 87 • Input data collection 90 Climate data 90 Geometry data 90 Definition of reference buildings 91 Buildings’ properties 93 Energy consumptions 102 LCA data 103 • Model creation 104 3D model 104 Template creation 108 Template assignment 113 • Simulation 124 Energy 124 GHG emissions 126 • Output data examination 128 Output data exporting and reading 128 Output data validation 142

Chapter 7 – Solution scenarios proposal 146 • Critical buildings selection 150 • Solution approach 154 • Scenario A 155 Condensation check 157 Regulation limit verification 162 Total operational energy 163 Heating energy 166 Cooling energy 167 Embodied energy 179 Embodied carbon 180 • Scenario B 181 Condensation check 183 Regulation limit verification 186 Total operational energy 191 Heating energy 296 Cooling energy 205 Embodied energy 213 Embodied carbon 214 • Scenario A+B 215 Total operational energy 215 Heating energy 216 Cooling energy 221 Embodied energy 223 Embodied carbon 224 • Discussion of the results 225

Final considerations 236 Bibliography 237 Acknowledgment 240 NOMENCLATURE AND ABBREVIATION

BEM Building energy modelling

GHG Green house gases

HVAC Heating, ventilation, and air conditioning

KPIs Key performance indicators

LCA, Life cycle assessment

LCCA Life cycle carbon assessment

LCEA Life cycle energy assessment

MMTCDE Million metric tonnes of carbon dioxide equivalents

PV Photovoltaic Panels

R Thermal resistance

RBs Reference buildings

TEK17 Norwegian regulations on technical requirements for construction works

UBEM Urban building energy modelling

UMI Urban modelling interface

U-value Thermal transmittance

WWR Window-to-wall ratio

ZEB Zero emission/energy buildings

ZEN Zero emission neighbourhood

λ Thermal conductivity

1 LIST OF FIGURES

Figure 1: the most important phases in the history of the sustainable development are presented in the graph [1]. Figure 2: the image shows the seventeen different goals for a sustainable development according to the United Nations. Figure 3: the world population will reach ten billions in the next thirty years [2]. Figure 4: the demand of floor area will remarkably increase in the next thirty years [3]. Figure 5: goals which a smart cities have to reach according to the European Unions [6]. Figure 6: the ZEN Centre’s partners, both the private sector, public sector and research and education one. Figure 7: map of the ZEN pilot projects. Figure 8: the ZEN Centre’s organizational structure. Figure 9: Work packages in the ZEN Research Centre. Figure 10: important definitions related to ZEB. Figure 11: different phases ot a building’s life that are included in the definitions levels. Figure 12: percentage of GHG emissions produced by sector; buildings are responsible of the 39% of the total global emissions [13]. Figure 13: buildings considered as a whole neighbourhood can reach the balance that might not be attain from a single one. Figure 14: Peel M C, Finlayson B L and McMahon T A 2007 Hydrol. Earth Syst. Sci. Discuss. 4 439–73 Figure 15: location of the eleven campuses of NTNU in Trondheim. Figure 16: numbers related to students at NTNU. Figure 17: facilities for students on or near the campuses on Trondheim. Figure 18: travel time by transport type to reach the city centre from Gløshaugen Campus. Figure 19: facilities for students in Gløshaugen cam Figure 20: map of the different type of use of the buildings in the Campus. Figure 21: map of the elevation of the terrain in Gløshaugen campus Figure 22: the scheme show which percentage of the emissions related to the buildings is due to the operations part and which is correlated to the embodied carbon [19]. Figure 23: the scheme shows the factors which influence the energy consumption in a building; they are both technical and human. Figure 24: different phases of the LCA workflow are presented. LCE and LCCA represent two steps of the entire process. [23] Figure 25: the scheme shows the different energy and emissions provision that have to be considered during a Life Cycle Energy and a Life Cycle Carbon Emis- sions Assessment.

2 Figure 26: during the life-span of a building, three different kind of embodied emissions have to be considered: the initial ones, the operational emissions (comprehended the emissions due to renovation) and the emissions resulting from the demolition of the building at the end of its life. [25] Figure 27: the graph underlines the embodied energy of some of the main building materials during their life-span; concrete, plastic and steel have a high level of embodied energy, contrariwise natural material like timber, stone and copper have a lower impact. [26] Figure 28: the best combination regarding the systems on a building, is the result of the combination between high efficiency plants and the use of renewable source for the energy production. Figure 29: global population growth and cars production in the future [30]. Figure 30: global population growth and electricity demand in the future [31]. Figure 31: explication of the innovation category through its main pillars [32]. Figure 32: assessment criteria and KPIs for the ZEN Research Centre. Figure 33: the four standard different phases that have to be developed in order to do an energy simulation [33]. Figure 34: IESVE software’s available modules [35]. Figure 35: CityBES is made by three part: data, software and use case; all of them interact in order to visualize the final performances [36].

Figure 36: annual global buildings sector CO2 emission sectors: embodied carbon is [21]. Figure 37: CitySIM has a three-based structure made of different modules: dis- trct, buildings and thermal zones. Figure 38: in the scheme, the workflow of CEA is showed [38]. Figure 39: in order to decide what tool was more suitable for the developing of the work about Gløshaugen Campus, a table with the pros and cons for each software have been done. Figure 40: phases that will be faced in order to develop the work with UMI. Figure 41: identification of the campus area that also correspond to the project area. Figure 42: the first scheme show how buildings with different geometry have to been split in simpler blocks; similarly, buildings in which there are different thermal zones, have been divided. Figure 43: the map shows how the different building were divided in order to create simpler blocks. Figure 44: numbers assignment to all the blocks of the Campus. Figure 45: categories assignment to all the blocks of the Campus. Figure 46: percentage of buildings per group: most of the buildings of the campus belong to Group B (1951-1960) and Group C (1961-1980). Figure 47: reference buildings. Figure 48: each component for each reference building (i.e. for each category) have been named. Each component is preceded by the letter of the correspondent category in order to create ordered lists which can be easily input and manage in the template editor of the tool UMI.

3 Figure 49: values of surface resistance given by the European Regulation ISO 6946. This values have to be used for plane surfaces in the absence of specific information on the boundary conditions. The values under “horizontal” apply to heat flow directions ± 30 ° from the horizontal plane [24]. Figure 50: the first image shows the plans of the Campus that has been created using OpenStreet Map; then, highs were added in order to create solids using Trondheim Municipality 3D model as a source. At last, windows were added as a percentage for each surface. Figure 51: UMI’s template editor workflow is showed; each section is useful to create the building template and has to be completed carefully in all its part. A incorrect template creation could bring to simulation problems. Figure 52: energy simulation can evaluate the total operational energy or sepa- rate portion of lighting, equipment, heating, cooling, domestic hot water. Also, it is possible to evaluate if there is an overheating. Figure 53: energy can be evaluated both in kWh and as a normalized value in kWh/m2. For the comparison of energy consumption between the buildings of the campus, the normalized value is the one that it will be considered. Figure 54: emissions evaluated during the lifecycle of the buildings can be showed both in kWh (embodied energy) or in kgCO2 (embodied emissions). It is possible to consider the whole building or just the facade and glazing. Figure 55: as for the operational energy, the emissions can be evaluated as a 2 normalized value [kgCO2/m ]. Figure 56: the graph shows the annual total energy consumption per each building of the Campus, expressed in kWh/m2. The average value of energy consumption is 138 kWh/m2. Figure 57: the graph shows the average monthly total energy consumption per each building of the Campus, expressed in kWh/m2. Figure 58: the graph shows the annual heating consumption per each building of the Campus, expressed in kWh/m2. The average value of energy consumption is 71 kWh/m2. Figure 59: the graph shows the average monthly heating consumption per each building of the Campus, expressed in kWh/m2. Figure 60: the graph shows the annual cooling consumption per each building of the Campus, expressed in kWh/m2. The average value of energy consumption is 23 kWh/m2. Figure 61: the graph shows the average monthly cooling consumption per each building of the Campus, expressed in kWh/m2. Figure 62: the graph shows the annual lighting consumption per each building of the Campus, expressed in kWh/m2. The average value of energy consumption is 39 kWh/m2. Figure 63: the graph shows the average monthly lighting consumption per each building of the Campus, expressed in kWh/m2. Figure 64: the graph shows the annual DHW consumption per each building of the Campus, expressed in kWh/m2. The average value of energy consumption is 40 kWh/m2.

4 Figure 65: the graph shows the average monthly DHW consumption per each building of the Campus, expressed in kWh/m2. Figure 66: the graph beside shows the percentage of energy consumption per type in the Campus; Figure 67: embodied energy of the buildings in their whole life span of fifty years [ kWh/m2]. Figure 68: embodied carbon of the buildings in their whole life span of fifty years

[kgCO2/m2]. Figure 69: the graph shows the comparison between the simulated values for the total operational energy and the statistics ones, expressed in [kWh/m2]. Figure 70: the graph shows the error in percentage between the simulated value for total operational energy and the statistic one. Figure 71: the graph shows the comparison between the simulated values for the heating energy and the Master’s Thesis ones, expressed in [ kWh/m2]. Figure 72: the graph shows the error in percentage between the simulated value for heating energy and the Master’s Thesis one. Figure 73: building with an annual energy consumption which is higher that the average in Norway of 175 kWh/m2, are identified. Figure 74: the graph shows the total energy usage of the ten critical buildings which have been identified in the campus. All of them have an energy consumption that is higher then the limit set at 175 kWh/m2. Figure 75: the graph shows the total energy usage of the ten critical buildings per month [ kWh/m2]. Figure 76: the graph shows the quantity of kWh/m2 which exceed the established limit. Figure 77: the graph shows the percentage number of pass rate of the limit value; some buildings exceed the limit more than 15%. Figure 78: 30% of the critical buildings belong to Group A, the 70% to Group B. No buildings of the other categories are among the ten worst of the Campus. Figure 79: the 66% (4 out of 6) of Group A buildings are critical, i.e. have an energy consumption higher than 175 kWh/m2; regarding Group B, more than the 28% (7 out of 28)of the buildings are in the worst ones selection. Figure 80: a simple scheme with the new stratigraphy for the façade of Group A buildings is displayed; dimension are given in centimetres. Figure 81: the results of Glaser method are showed per each month; the façade present interstitial condensation. Figure 82: adding the vapour barrier is possible to solve the problem of interstitial condensation in the façade. Figure 83: the graph shows the total energy consumption of the Group A buildings before and after the insulation adding. Figure 84: the graph shows the total saved energy by the critical Group A buildings after the intervention. Figure 85: the graph shows the percentage annual improvement of the energy consumption after the insulation adding. Figure 86: graphs show the comparison between the monthly total energy con

5 sumption of the buildings between the status quo and the solution Scenario A. Figure 87: the graph shows the heating consumption of the Group A buildings before and after the insulation adding. Figure 88: the graph shows the total saved heating energy by the critical Group A buildings after the intervention. Figure 89: the graph shows the percentage annual improvement of the heating consumption after the insulation adding. Figure 90: graphs show the comparison between the monthly heating consumption of the buildings between the status quo and the Scenario A. Figure 91: the graph shows the annual cooling consumption of the Group A buildings before and after the insulation adding. Figure 92: the graph shows the total saved cooling energy by the critical Group A buildings after the intervention. Figure 93: as a consequence of the adding of the insulation, the annual cooling demand increased. Figure 94: graphs show the comparison between the monthly cooling consumption of the buildings between the status quo and the Scenario A. Figure 95: the graph shows the kWh/m2 of different component of the total energy before and after the renovation. Figure 96: the graph shows the annual production of embodied energy of the buildings before and after the solution execution. Figure 97: the graph shows the annual production of embodied carbon of the buildings before and after the solution execution. Figure 98: stratigraphy of the façade is showed; measures are given in centimetres. Figure 99: stratigraphy of the roof is showed; measures are given in centimetres. Figure 100: the results of Glaser method are showed per each month; the façade present interstitial condensation. Figure 101: the results of Glaser method are showed per each month; the façade present interstitial condensation. Figure 102: the graph shows the total energy consumption of the critical Group B buildings before and after the renovation. Figure 103: saved energy due to the renovation of the Group B critical buildings is showed. Figure 104: critical buildings of Group B have all a remarkable yearly energy saving after the renovation. Figure 105: the graph shows the percentage improvement regarding the saved energy before and after the renovation during one year. Figure 106: the graph shows the percentage improvement resulting just from the renovation of the envelope; the values are not so remarkable compared to the solution which involved the systems too. Figure 107: the graph shows the percentage improvement regarding the saved energy before and after the renovation during one year. Figure 108: the graph shows the heating consumption of the Group B buildings before and after the insulation adding during a typical year.

6 Figure 109: the graph shows the total saved heating energy by the critical Group B buildings during a year after the renovation. Figure 110: the graph shows the yearly improvement of saved energy which result after the renovation of critical Group B buildings. Figure 111: graphs show the comparison between the monthly heating consumption of the buildings between the status quo and the Scenario B. Figure 112: the graph shows the compared yearly energy consumption of the buildings before and after the intervention. Figure 113: the graph shows the total saved cooling energy by the critical Group B buildings after the intervention during a typical year. Figure 114: the improvement of the buildings after the renovation during a typical year is showed in the graph. Figure 115: the graphs shows the monthly comparison of cooling energy demand of the buildings before and after the renovation. Figure 116: the graph shows the kWh/m2 of different component of the total energy before and after the renovation. Figure 117: the graph shows the annual production of embodied energy of the buildings before and after the solution execution. Figure 118: the graph shows the annual production of embodied carbon of the buildings before and after the solution execution. Figure 119: the graph shows the reduction of total energy consumption during a year of the whole Campus. Figure 120: after the renovation, the Campus energy performances improved of the 8% compared to the current situation. Figure 121: the monthly average energy consumption of the campus decreased during a typical year. Figure 122: the graph shows the reduction of heating energy consumption during a year of the whole Campus. Figure 123: after the renovation, the Campus energy performances regarding the heating improved of the 13% compared to the current situation. Figure 124: the monthly average heating energy consumption of the campus decreased during a typical year. Figure 125: the graph shows the reduction of cooling energy consumption during a year of the whole Campus. Figure 126: after the renovation, the Campus energy performances regarding the cooling improved of the 4% compared to the current situation. Figure 127: the graph shows the average embodied energy produced in one typical year by each buildings of the Campus. Figure 128: the graph shows the total embodied energy produced in one typical year by all the buildings of the Campus. Figure 129: the graph shows the average embodied carbon produced in one year by each buildings of the Campus after the renovation. Figure 130: the graph shows the total embodied carbon produced in one year by all the buildings of the Campus after the renovation. Figure 131: the U.value of the façade decreased from 0.93 to 0.22 [W/m2K] after

7 the renovation. Figure 132: the schemes show the energy saved after the renovation from all the critical Group A buildings. Figure 133: both the U-value of the façade and the roof decreased significantly after the renovation. Figure 134: comparison of the results considering or not the systems in the renovation. Figure 135: the scheme shows the sum of energy consumption of all the critical buildings during a typical year, before and after the renovation Scenario A+B. Figure 136: comparison of the results regarding the heating and cooling demand are showed in the scheme above. Figure 137: renovating all the critical buildings in the Campus it is possible to save about 808 kWh/m2 every year. Figure 138: the saved kWh/m2 correspond to the yearly energy consumption of about eight Group E building. Figure 139: the scheme shows the Norwegian energy label and their limit value regarding the energy consumption in kWh/m2/yr. Figure 140: all the buildings, both belonging to Group A or B, improved their current energy label.

Figure 141: the graph shows the embodied CO2 produced by the Campus in a life- span of fifty years before and after the renovation. Figure 142: in the left, the average embodied carbon produced by the Campus before and after the renovation is showed (per square metres); on the right, total 2 kgCO2/m produced by the Campus in fifty years before and after the renovation. Figure 143: average and total embodied energy produced by the Campus before and after the renovations.

8 LIST OF TABLES

Table 1: minimum U-values for buildings’ components introduced in the TEK17. Table 2: summary tables of the five different categories of buildings which have been created for the developing of the work. Table 3: the tables show the different materials per each component of each category. Properties have been extrapolated from official document of the University. Table 4: values of conductivity [W/mK] and thermal resistance [m2K/W] of each material. Table 5: values of density [kg/m3], solar absorptance [-] and specific heat [J/kgK] of each material. Table 6: values of thermal emittance [-] and visible absorptance [-] of each material. Table 7: values of calculated U-values compared to the given U-values for each component. Table 8: errors in percentage between calculated U-values and given U-values; the error never exceeds the 15%. Table 9: information collected about the energy usage in the Campus are listed. Table 10: information collected about the embodied properties of the materials are listed. Table 11: windows percentage per each surface of the buildings. Table 12: values of energy consumption per type are listed, expressed in total kWh/m2 per year. Table 13: values of the simulated total energy consumption and the statistic one are reported in the table. Table 14: the table shows the percentage error between the simulated value of total operational energy and the statistic one. Table 15: values of the simulated heating consumption and the statistic one are reported in the table. Table 16: The table shows the percentage error between the simulated value of heating energy and the one from the previous Master’s Thesis work. Table 17: the stratigraphy for the façade of Group A is showed; internal insulation has been added to the wall. Table 18: the U-value has been calculated with the new stratigraphy and it respect the regulation limit. Table 19: the new stratigraphy for critical Group B buildings are showed; renovation regard the roof and the façade. Table 20: properties of the chosen windows are listed. Table 21: transmittance U-vales of new stratigraphy respect the regulation TEK17 limits.

9 INTRODUCTION

The work was carried out as part of an internship abroad, in particular at the ZEN - Zero Emission Neighbourhood Research Centre in Smart Cities in Trondheim, Nor- way. The experience, which lasted five months, was aimed at approaching the theme of sustainability from a different and wider point of view. In fact, the key concept of the Research Centre considers the step from the focusing on the single building to an entire neighbourhood analysis; the aim is to reach sustainability targets for the whole district and compensate for those buildings with a weaker energy behaviour.

The topic addressed is about the energy performances of buildings; initially it will be necessary to deep some theoretical aspect of the subject in order to approach the work in a correct way.

The work which has been assigned is the energy evaluation of the University Cam- pus of Gløshaugen in Trondheim, in anticipation of its planned renewal in the next ten years. As it is composed of buildings of different types and uses, it can be considered as an urban district because of its variety.

For this purpose, it was requested to identify one software that could perform a large-scale energy simulation and also the emissions related to the buildings. The relationship between the energy usage and the emissions related to buildings have to pointed out in order to understand if it is possible to give solution for the reduction of both of them. This will require an analysis of the programmes allowing this type of evaluation and the justified choice of one of them.

Next, it will be necessary to perform an energy and emissions simulation of the entire Campus with the software chosen and find a methodology to make the process as efficient as possible. The results obtained should be validated in such a way that the energy model itself can be validated. In conclusion, design solutions aimed at improving the current state of the Campus will be provide; these will have to be justified and explained so that it is clear their feasibility. It will also have to be shown that the solutions proposed are actually the solution to the problems we are facing today.

The aim is to find a software which it is possible to carry on the work with and obtain results that have to be as correct and true as possible. In addition, design solutions that can improve decisively the energy performance of the Campus compared to the current situation will be found and given. The work have to underline the strictly relationship between energy and emissions and understand if it is possible to improve both the energy consumption and the emissions of the buildings after a renovation process.

chapter 1 FOCUS ON SUSTAINABILITY Nowadays, the term “sustainability” has become part of our everyday life. This chapter will deep the most important step which brought to the current concept and definition of the word. Moreover, the connection between the sustainable development and the building sector will be analysed.

13 FOCUS ON SUSTAINABILITY

CONCEPTS AND BACKGROUND

Nowadays, the world “sustainability” has become commonly used and concepts related to it more and more crucial; now in the 21st century, with a global population which is expected to reach the ten billion in the next thirty years, a sustainable development is crucial and it will be more with the passing of the years. The concept of sustainability so claimed in these last years has to pass through different and crucial steps in the last decades in order to reach the importance that it has today [1].

- publication of “The limit of growth” 12 Foundation of the - first definition of “sustainable development is develop- Club of Rome ment that meets the needs of the present without compromising the ability of future generations to meet their own needs”.

12 - foundation of “UNEP” 1 World climate (United Nations Enviroment Programme) Conference

1 Conference of the World - writing of the document “Our Common Future” about Commission of Environ- the concept of “sustainable development” ment and Development

1 Foundation of the IPCC - deeping on the concepts related to the climate changes Intergovernmental Panel on Climate Change

12 Earth Summit in Rio de - writing of several documents including the “Rio aneiro Declaration” and the “Agenda 21”

15 COP-1 Conference of - first climate change world conference Parts in Berlin

1. Source: website1 of the United Nations (ONU). - “Kyoto Protocol” first important international agreement COP-3 Conference of regarding climate change mitigation actions Parts in Kyoto 14

24 COP-10 Conference of - decisions about “Kyoto Protocol” execution Parts in Buenos Aires

25 Kyoto Protocol - Kyoto Protocol became true and active eecution

21 - 10-years strategy for advancement of European Union Europe 2020 economy in a smart and sustainable way strategies

21 - definition of the eight millennium goals: eradicate ONU summit Millennium extreme poverty and hunger, achieve universal primary Development Goals education, promote gender equality, reduce child MDGs mortality, improve maternal health, combat diseases, ensure enviromental sustainability, global partnership.

212 ONU Conference - renew the commitments of Rio in 1992 regarding the Rio 20 sustainable development

215 - new “Agenda 2030” and definition of the new seventeen United Nations summit in goals of a sustainable development Paris - publication of “The limit of growth” 12 Foundation of the - first definition of “sustainable development is develop- Club of Rome ment that meets the needs of the present without compromising the ability of future generations to meet their own needs”.

12 - foundation of “UNEP” 1 World climate (United Nations Enviroment Programme) Conference

1 Conference of the World - writing of the document “Our Common Future” about Commission of Environ- the concept of “sustainable development” ment and Development

1 Foundation of the IPCC - deeping on the concepts related to the climate changes Intergovernmental Panel on Climate Change

12 Earth Summit in Rio de - writing of several documents including the “Rio aneiro Declaration” and the “Agenda 21”

15 COP-1 Conference of - first climate change world conference Parts in Berlin

1 - “Kyoto Protocol” first important international agreement COP-3 Conference of regarding climate change mitigation actions Parts in Kyoto

24 COP-10 Conference of - decisions about “Kyoto Protocol” execution Parts in Buenos Aires

25 Kyoto Protocol - Kyoto Protocol became true and active eecution

21 - 10-years strategy for advancement of European Union Europe 2020 economy in a smart and sustainable way strategies

212 ONU Conference - renew the commitments of Rio in 1992 regarding the Rio 20 sustainable development

215 - new “Agenda 2030” and definition of the new seventeen United Nations summit in goals of a sustainable development Paris

Figure 1: the most important phases in the history of the sustainable development are presented in the graph [1].

The last United Nations summit in Paris was crucial for the future development of sustainable strategies; seventeen new different goals we listed in order to reach the aim. A common denominator for most of these goals is that technology will play an essential role in solving the problems that must be addressed. The Sustainable Development Goals are the blueprint to achieve a better and more sustainable future for all. They address the global challenges we face, including those related to poverty, inequality, climate, environmental degradation, prosperity,

1. Source: website of the United Nations (ONU).

15 ICONS 48 and peace and justice. The Goals interconnect and in order to leave no one behind, ICONit is importantS that we achieve each Goal and target by 2030. The seventeen goals are showed in the following Figure 2:

17 ICONS: COLOUR VERSION

Figure 2: the image shows the seventeen differ- When an iconent is on goals a square, forthat s aqu sustainableare must be proport idevelopmentonal 1 x 1. accord- The white icoingn sho tould bthee con Unitedtained by it sNations. deﬁned colour, or black background.

Do not alter the colours of the SDG icons.

Eective 1 JanAccordinguary 2018, the Unite dto Nat iothens is la unAgendaching a revised 2030,design of Ic onthese 10, as seen ogoalsn this page should be adopt by all the signatory states within the next ten years. If not respected, it will not be guaranteed that we will be able to come back from a point of no return.

16 SUSTAINABILITY AND BUILDINGS

Now in the 21st century, nearly the 55% of the global population lives in the cities; urbanization is the key word that will characterized the next years. With a population that will reach the ten billions people in the 2050, the focus of the sustainability topics will be more and more important.

Future population growth

10000 5000 2500 1000 500 250 100 50 40 20 10 1950 world1960 1970 1980Europe1990 2000 2010Latin America2020 2030 2040Oceania2050 Asia Africa North America

Figure 3: the world population will reach ten billions in the next thirty years [2].

As a consequence of the increasing population, the demand of housing will grow r and shaping spaces and cities will be crucial. Existing cities have to be rethought and new ones will have to be approach in a different and smarter way. The required global floor area will triple compared to the current demand, hence new buildings will be build to make up to the great need.

2. Source: book “Why are cultures warlike or peaceful? Introducing regality theory” by Agner Fog. 17 5.5 5 4.5 4 3.5 2 m

3 n o

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1.5 8 1 0 1 2 0.5 0 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 Global floor area Figure 4: the demand of floor area will remarkably increase in the next thirty years [3].

ICONS 48

ICONThe overallS sustainability goal is thus to design sustainable, high density neighbourhoods that combine building resource–efficiency with quality indoor and outdoor spaces, which in turn support community building and favour human powered 17 ICONS: CmodesOLOUR VERS IofON transportation.

Among the seventeen goals drafted by the United Nations, four of them are strictly related to the buildings sector.

Energy is central to nearly every major challenge and opportunity the world faces today. Focusing on universal access to energy, increased energy efficiency and the increased use of renewable energy through new economic and job opportunities is crucial to creating more sustainable and inclusive communities and resilience to environmental issues like climate change [1].

Nowadays buildings are responsible of nearly the 40% of total primary energy consumption; this last has dramatically increased in the past decades because of the population growth. Since more people spend time indoor, more energy is needed to ensure the thermal environment quality. A proper design, construction and opera- When an icontion is on a sphasequare, that s qofuare the must b ebuildings proportional 1 x 1 .can give a significant energy saving; building energy ef- The white icon should be contained by its deﬁned colour, or black background.ficiency can provide key solutions to energy shortages, carbon emissions and their

Do not alter tserioushe colours of th threate SDG icons. to our living environment [4].

Eective 1 January 2018, the United Nations is launching a revised design of Icon 10, as seen on this page 3. Source: website “Architecture 2030”, article “Why the buildings sector?”. 4. Source: paper “Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade”, X. Cao, X. Dai, J Liu.

18 ICONS ICONS 48

17 ICONS: COLOUR VERSION

Investments in infrastructure – transport, irrigation, energy and information and communication technology – are crucial to achieving sustainable development and empowering communities in many countries. It has long been recognized that growth in productivity

and incomes,ICONS and48 improvements in health and education outcomes ICONS require investment in infrastructure [1]. Infrastructures have to became more and more resilient in order to me adaptable to the fast growing of the cities; in particular roads, railways and the other way of 17 ICONS: COLOUR VERSION transport have to be think in a smarter way to make cities more efficient and people easier to move. When an icon is on a square, that square must be proportional 1 x 1. New strategy to make infrastructure flexible to the existing cities’ patterns have to The white icon should be contained by its deﬁned colour, or black background. be thought.

Do not alter the colours of the SDG icons.

Eective 1 January 2018, the United Nations is launching a revised design of Icon 10, as seen on this page Smart cities are hubs for ideas, commerce, culture, science, productivity, social development and much more. At their best, cities have enabled people to advance socially and economically. With the number of people living within cities projected to rise to 5 billion people by 2030, it’s important that efficient urban planning and management practices are in place to deal with the challenges brought by urbanization.

Future cities have to become smart: a smart city is a designation given to a city that incorporates information and communication technologies (ICT) to enhance the quality and performance of urban services such as energy, transportation and utilities in order to reduce resource consumption, wastage and overall costs. The When an icon is on a square, that square must be proportional 1 x 1. overarching aim of a smart city is to enhance the quality of living for its citizens The white icon should be contained by its deﬁned colour, or black background. through smart technology [5].

Do not alter the colours of the SDG icons.

Eective 1 January 2018, the United Nations is launching a revised design of Icon 10, as seen on this page - sustainable urban mobility - sustainable districts and built environment - integrated infrastructures and processes in energy, information and communication technologies and transport - citizen focus - policy and regulation Goals of a smart city - integrated planning and management - knowledge sharing - baselines, performance indicators and metrics - open data governance - standards - business models, procurement and funding

Figure 5: goals which a smart cities have to reach according to the European Unions [6]. PALETTE COLORI 5. Source: web site “Technopedia”. 6. Source: web site of the European Commission, definition of “smart cities”.

Lore m ipsu ICONS ICONS 48

17 ICONS: COLOUR VERSION

Sustainable consumption and production is about promoting resource and energy efficiency, sustainable infrastructure, and provid- ing access to basic services, green and decent jobs and a better quality of life for all. Since sustainable consumption and production aims at “doing more and better with less,” net welfare gains from economic activities can increase by reducing resource use, degradation and pollution along the whole life cycle, while increasing quality of life.

Life-cycle assessment is becoming a common strategy related to the buildings sector; it is useful to track and calculate the emissions related to all the compo- When an icon is on a square, that square must be proportional 1 x 1. nents and materials of a construction. The white icon should be contained by its deﬁned colour, or black background. It can also be used to reduce the impact of the emissions; nowadays buildings are Do not alter the colours of the SDG icons. responsible for almost the 36% of European greenhouse gas emission [3].

Eective 1 January 2018, the United Nations is launching a revised design of Icon 10, as seen on this page For this reason, is strongly important understand how to mitigate this percentage.

In conclusion, buildings have a crucial role in nowadays society and they also play and important role regarding the sustainable development. Recent statics have showed that buildings are responsible of the main world GHG emissions and also of the higher percentage of energy demand. Without any doubt it is important to find new strategies and approaches for the buildings sector which will be more and more challenging in the next years.

This chapter has been developed in collaboration with Sara Corio., Master Thesis student in Buildings Engineerign and Architecture at University of Genoa, Italy.

20 chapter 2 THE ZEN RESEARCH CENTRE The work has been developed in the ZEN (Zero Emissions Neigh- bourhoods) in Smart Cities in Trondheim, Norway. This chapter is intended to provide information regarding ZEN, its background and future goals. Moreover, an overview of Norway and of the Norwegian Universi- ty of Science and Technology will be showed.

23 RESEARCH CENTRE ON ZERO EMISSION NEIGHBOURHOODS IN SMART CITIES – FME ZEN

DEFINITION OF ZEN RESEARCH CENTRE

The ZEN Research Centre conduct research on zero emission neighbourhoods (ZEN) in smart cities, with the goal of developing solutions for future buildings and neighbourhoods with no greenhouse gas emissions and thereby contributing to a low carbon society.

The ZEN Research Centre is a research centre for environmentally friendly energy that was established in 2017 by the Research Council of Norway. It is hosted by the Norwegian University of Science and Technology and organized as a joint NTNU/ SINTEF unit [7].

GOALS OF ZEN RESEARCH CENTRE

Increased innovation and value creation in the participating public institutions and private businesses as well as in the Norwegian society in general

To contribute to the reduction of greenhouse gas emissions national- ly and internationally as well as to a more effective use of energy and a higher production of renewable energy

NUMBERS OF ZEN RESEARCH CENTRE

2017-2024

ca. 380 MNOK of which are: 176 MNOK from the Research Council of Norway, 104 MNOK from the user partners, 100 MNOK from the research partners (NTNU and SINTEF)

staff at the end of 2017: 18 Key researchers, 7 PhD candidates, 2 postdocs, 37 associated researchers, 2 administrative staff

7. “Organization of the ZEN Research Centre”, Annual Report 2018 and

24 PARTNERS OF ZEN RESEARCH CENTRE

The partners in the ZEN Research Centre hold central roles within the design and development of neighbourhoods and the energy system.Oslo, This Bergen, include Trondheim, representa Bærum - Bodø, Elverum, Steinkjer tives from municipal and regional governments, property owners, developers, con- Trøndelag fylkeskommune sultants and architects, ICT companies, contractors,11 public energyStatsbygg companies, manufac- tures of materials and products, and governmentalpartners organisationsNVE – Norges vassdrag (Figure og 6). energidirektorat

DiBK – Direktoratet for byggkvalitet Oslo, Bergen, Trondheim, Bærum ByBo, Elverum Vekst Bodø, Elverum, Steinkjer TOBB Trøndelag fylkeskommune Snøhetta, ÅF Engineering, Asplan Viak 11 public Statsbygg Multiconsult, SWECO, Civitas partners NVE – Norges vassdrag og FutureBuilt energidirektorat Energi Norge, Norsk Fjernvarme NTE – Nord-Trøndelag Energiverk DiBK – Direktoratet for byggkvalitet 21 industry partners Statkraft ByBo, Elverum Vekst Hunton TOBB Figure 6: the ZEN Centre’s partners, both the Moelven Snøhetta, ÅF Engineering, Asplan Viak private sector, public sector and research and Norcem education one.Multiconsult, SWECO, Civitas Smart Grid Services Cluster FutureBuilt Skanska Energi Norge, Norsk Fjernvarme GK, Caverion 21 industry NTE – Nord-Trøndelag Energiverk NTNU partners Statkraft 2 research SINTEF Hunton partners Moelven Norcem PILOT PROJECTSSmart Grid Services Cluster OF ZEN RESEARCH CENTRE Skanska Bodø: Airport areaGK, Caverion The ZEN pilot projects include both new Trondheim:2 research NTNUNTNU Campus & Sluppen areas and well-established areas that Bærum:partners Oksenøya,SINTEF Fornebu Bergen: Zero Village Bergen are to be upgraded and developed fur- Steinkjer: Residental area ther, in Norway (Figure 7). Evenstad: Campus The ZEN pilot projects serve as innova- Elverum: Ydalir tion hubs where the ZEN researchers, Oslo: Furuset together with building professionals, Figure 7: map of the property developers, municipalities, ZEN pilot projects. energy companies, building owners, and users, test new solutions for the construction, operation and use of neighbourhoods in order to reduce the greenhouse gas emissions to zero on a neighbourhood scale.

8. “Organization of the ZEN Research Centre”, Annual Report 2018

25 ORGANISATION OF ZEN REASEARCH CENTRE

The ZEN Research Centre has a General Assembly and an Executive Board (EB). The General Assembly (GA) includes a representative from each of the partners, gives guidance to the Executive Board in their decision-making on major project management issues and approval of the semi-annual implementation plans. The Executive Board (EB) is responsible for the quality and progress of the research activities toward the Council of Norway and for the allocation of funds to the various activities.

The Centre has also a Scientific Committee (SC) with representatives from leading international institutes and universities to ensure international relevance and quality of the work performed.

The ZEN Research Centre is highly multi-disciplinary and has organized the research activities in 6 work packages (Figure 8).

General Assembly Eecutive Board Centre Managment all partners 8 representatives: Team 6 user partner centre director, representatives, centre industry, NTNU, SINTEF communications, advisor & coordina- tor, work package leaders

Scientific Committee

WP1 WP2 WP3 WP4 WP5 WP6 analytical policy responsive energy local energy pilot projects framework measures, and energy flexible system and living for design innovation efficient neigh- optimisation labs and planning and business buildings bourhoods in a larger of ZEN models system

Figure 8: the ZEN Centre’s organizational structure.

9. “Organization of the ZEN Research Centre”, Annual Report 2018 WP1 analytical framework for design and planning of ZEN 26 WP2 policy measures, innovation and business models WP6 pilot projects and living labs WP3 WP4 WP5 responsive and energy local energy system energy efficient flexible neigh- optimisation in a buildings bourhoods larger system General Assembly Eecutive Board Centre Managment all partners 8 representatives: Team 6 user partner centre director, representatives, centre industry, NTNU, SINTEF communications, advisor & coordina- tor, work package leaders

Scientific Committee

WP1 analytical framework for design and planning of ZEN

WP2 policy measures, innovation and business models WP6 pilot projects and living labs WP3 WP4 WP5 responsive and energy local energy system energy efficient flexible neigh- optimisation in a buildings bourhoods larger system

Figure 9: Work packages in the ZEN Research Centre.

WP1 - The goals of WP1 are to develop neighbourhood design and planning instruments while integrating science-based knowledge on greenhouse gas emissions. The aim is to improve: benchmarking for ZEN based on customised indicators and quantitative and qualitative data and a life cycle analysis methodology for the use of energy and emissions at a neighbourhood scale.

WP2 - The research in WP2’s aim is to create business models, roles and services that address the lack of flexibility towards market and catalyse the development of innovations for a broader public use. It evaluates possible transition pathways towards ZEN consisting of integrated studies of policy measures, different forms of public-private collaboration, different financial and business models and instruments as well as improved innovation processes.

WP3 - the aim is to create cost effective, responsive, resource and energy efficient buildings by developing low carbon technologies and construction systems based on lifecycle design strategies.

WP4 - it develops knowledge, technologies and solutions for the design and operation of energy flexible neighbourhoods.

WP5 - The researchers in WP5 develop a decision-support tool for optimizing local energy systems and their interaction with larger system. They apply methodologies that identify the socio-economic optimal operation and expansion of energy systems within demarked areas.

WP6 - the ZEN pilot projects serve as innovation hubs for the Centre’s research: they create and manage a series of neighbourhoods-scale living labs, that acts as testing ground for the solutions developed in the ZEN Research Centre. They are geographically limited – primarily urban – areas in Norway in which the Centre’s researchers, together with their partners, test new solutions for the construction, operation and use of neighbourhoods in order to reduce the greenhouse gas emissions on a neighbourhood scale to zero.

27 BACKGROUND OF ZEN RESEARCH CENTRE

The ZEN Research centre was founded in the 2017 and it replaced the previous centre, ZEB (Zero Emission Buildings). This last was focusing on eliminating the GHG emissions caused by buildings using research, innovation and implementation within the field of energy efficient zero-emission buildings.

ZEB AND N ZEB

In order to increase the high energy efficient buildings, the European Union released the EPBD (Energy performance of buildings directive) 2010/31/UE; in this legislative instruments a definition of nZEB is given as “a building with almost zero energy demand whose demand is covered significantly by renewable sources” [10]. Briefly, the Directive only defines the big picture giving considerable latitude to Member States to refine it. Therefore, the nZEB concept is very flexible with no single, harmonised nZEB definition throughout the EU. Member States are responsible to define in their national plans what constitutes an nZEB while considering the feasibility of implementing such a concept in their national contexts. These definitions were expected to be included in the MS National Energy Efficiency Action Plans which are expected to be reviewed by the Commission by the end of 2015 [11]. During the years, each country has implemented the directive and gave different and more precise definition of both nZEB and ZEB since Article 9 of the EPBD requires Member States (MS) not only to set a national nZEB definition, but also to actively promote higher market uptake of such buildings.

Norway, although not part of the UE, has adhered to the directive and a definition of ZEB has been proposed by the ZEB Research Centre: “a zero emission building produces enough renewable energy to compensate for the building’s greenhouse gas emissions over its life span” [12]. The definition takes in account the emissions, and not only the energy demand of the buildings. The ZEB research centre has defined different levels of zero emission buildings depending on how many phases of a building’s lifespan that are counted in. The most important definitions are five and they are showed in the following scheme in Figure 3:

10. web site of the European Commission, “Definition of n-ZEB buildings”. 11. Factsheet “nZEB definitions across Europe” by EPBD (Energy performance of buildings directive).1 12. ZEB Research Centre web site: ZEB definitions

28 7/12/2019 ZEB Definitions

(/) (http://www.forskningsradet.no/servlet/Satellite?c=Page&cid=1222932140849&p=1222932140849&pagename=energisenter%2FHovedsidemal) ZEB – O ZEB – O E ZEB – OM ZEB – COM ZEB – COMPLETE Search... (/index.php/no/om-zeb/zeb-definisjoner) (https://www.zeb.no/index.php/en/about-zeb/zeb-definitions) the buildings the buildings the buildings the buildings the buildings renewable renewable renewable renewable renewable ZEB Definitionsenergy (/index.php/en/about-zeb/zeb-definitions) energy energy energy energy production production production production production compensate for compensate for compensate for compensate for compensate for ZEB – Zerogreenhouse Emission Buildings gas GHG emissions greenhouse gas greenhouse gas greenhouse gas A zero emission building produces enough renewable energy to compensate for the building's greenhouse gas emissions over its life span. The ZEB research centre has defined differentemissions levels of zero emission from buildings dependingfrom on how operation many phases of a ofbuilding's emissions lifespan that are counted from in. The 5 mostemissions important definitions, in fromrising emissions from ambition level, are:operation of the the building operation and construction, the entire

ZEB – O minus the energy production of its operation and lifespan of the The building's renewable energy production compensate usefor greenhouse for gas emissionsequip from- operationbuilding of the building. production of building. Building ment. materials. building materials – ZEB – O ÷ EQ The building's renewable energy production compensate for greenhouse gas emissions from operation of the building minus the energy usematerials. for equipment (plug loads). construction – operation and ZEB – OM demoli- The building's renewable energy production compensate for greenhouse gas emissions from operation and production of its building materials. tion/recycling. ZEB – COM The building's renewableFigure energy 10: production important compensate definitions for greenhouse gas emissions related from construction,to ZEB. operation and production of building materials.

ZEB – COMPLETE The building's renewable energy production compensate for greenhouse gas emissions from the entire lifespan of the building. Building materials – construction – operation and demolition/recycling. Figure 11: different phases ot a 2 - materials construction use end of life building’s life that are included in the definitions levels. Generate re - Generate ener newable CO Payback gy. 2 Energy use CO Emissions

(/cache/4/b456c5d4dacf2e6775afe1cfbf799dc8.PNG) The illustration shows the different phases of a building’s life that are included in https://www.zeb.no/index.php/en/about-zeb/zeb-definitionsthe various ZEB definition levels. The renewable energy production1/3 (green circle) compensates for all greenhouse gas emissions throughout the life span of the building in the example.

29 FROM ZEB TO ZEN

2009-2017 2017-2024

290 MNOK ca. 380 MNOK

Sandvika: Powerhouse Kjorbo Bodø: Airport area Arendal: Skarpnes Trondheim: NTNU Campus & Sluppen Bergen: Zero Village Bergen, Visund Bærum: Oksenøya, Fornebu Steinkjer: Residental area Bergen: Zero Village Bergen Evenstad: Campus Steinkjer: Residental area Trondheim: powerhouse Brattorkaia, Evenstad: Campus ZEB Living Lab, Heimdal VGS Elverum: Ydalir Oslo: Furuset

to develop competitive products to increase innovation in the and solutions for existing and participating public institutions, new buildings that will lead to private businesses and in the market penetration of buildings Norwegian society in general with zero greenhouse gas emis- To contribute to the reduction of sions related to their production, greenhouse gas emissions, to operation, and demolition. a more effective use of energy and a higher production of renewable energy.

WP1 - Advanced materials WP1 - Analytical framework for technologies design and planning of ZEN WP2 - Climate-adapted lowen- WP2 - Policy measures, innova- ergy envelope technologies tion and business models WP3 - Energy supply systems WP3 - Responsive and energy ef- and services ficient buildings WP4 - Use, operation, and im- WP4 - Energy flexible neighbour- plementation hoods WP5 - Concepts, strategies and WP5 - Local energy system opti- pilot buildings misation in a larger system WP6 -pilot projects and living lab

30 ZERO EMISSION NEIGHBOURHOODS: DEFINITION AND APPROACH

As explained in the previous chapter, the increasing of the buildings sector will bring to a higher demand of materials and energy. With the more and more significant growth of the cities, emissions related to the construction will assume a crucial role.

Figure 12: percentage of GHG emissions pro- other duced by sector; buildings are responsible of the 39% of the total global emissions [13].

buildings industry 3 3

transportation 22

As showed in the graph above, buildings sector is responsible of the 39% of total global emissions; this value is going to increase dramatically in the next thirty years. Since buildings hold the higher percentage of emissions, it will not be possible to reach the climate goals without eliminating or at least decreasing the emissions by 2050.

Since this topic is becoming important and it will be much more in the next years, the ZEB Research Centre started focusing on it and the ZEN Research Centre replaced the first one in the 2017. The focus on the decreasing of the emissions related to the buildings is one of the main new topic of ZEN; to approach it, a change of perspective was necessary. The critical issues related to ZEN were related to the difficulties of considering the buildings one by one, without having a global view. Reach the goals regarding the energy and the emissions would have been easier with a change of perspective to a wider point of view. Considering not a single building but a whole neighbourhood, it will be easier to meet the ambition regarding both the energy and the emissions; buildings with a

13. Source: website “Architecture 2030” - article “New buildings: embodied carbon”. 31 good behaviour in terms of energy/emissions could supply to the ones which would have never attain the goals if considered alone. Following a Norwegian saying, at ZEN people think that “the whole is greater than the sum of its parts” and they are trying to apply it to buildings as well. Different buildings categories have different use patterns; the differences between buildings types with their individual use patterns might influence optimization on a neighbourhood scale.

Every building might not need to be a “zero emission”, it is possible to use loads distribution over time and have a mosaic of buildings which individually may not have a zero-emissions balance, but which reach it as a group. This allows for a larger degree of freedom compared to individual building design; instead of making one hundred zero emissions buildings, it is possible to build a zero emissions neighbourhood [14].

Figure 13: buildings considered as a whole neighbourhood can reach the balance that might not be - - 5 - 6 + 1 + attain from a single one.

sum

PALETTE COLORI

Lore m ipsu

14. Source: ZEN annual report, article “How do responsive buildings contribute to Zero Emis- sions Neighbourhood?”, by S. Grynning.

32 NORWAY Norway is a rich and developed country with a high standard of living, located in the high north of the Scandinavian Peninsula. It is known for its mountains, fjord coastline and a long history as a seafaring power. The geographical locations of Norway’s mainland extend over more than 13 latitudes, from Lindesnes at the latitude 57°N to Nordkapp at the 71°N latitude with an area of 324 000 square kilometers. Considering the high latitudes and the coastline facing the Arctic Ocean, Atlantic Ocean and the North Sea, Norway has relatively warm climate compared to other regions of the world at the same latitude. This is possible because of the Norwegian current, the north-east extension of the Gulf Stream, and the southern air currents form the Atlantic Ocean. As a result, a subarctic climate dominates most of Norway, with a temperate climate along the coast and an Arctic one in the mountainous regions in the inland. The figure shows the Köppen climate types in Norway.

ET / tundra

dfc / subarctic

dfb / warm summer and humid continental

cfb / oceanic cfc / subpolar oceanic

TRONDHEIM

Trondheim is a city and municipality in Trøndelag county, Norway. It is Norway’s 3rd most populous city with approximately 190.000 inhabitants and is the fourth largest urban area. It is located at a 130 km long and 10–20 km wide fjord in Mid-Norway. The city is dominated by the Norwegian University of Science and Technology (NTNU), the Foundation for Scientific and Industrial Reasearch (SINTEF) and other technology-oriented institutions. Trondheim is connected to the world through an international airport, a railway and bus stations, a harbour that is used by cruise ships in summer and by coastal ships during all the year.

33 CLIMATE

The weather in Norway is dominated by the Westerlies and polar winds in the north, with alternating low- and high-pressure activity, resulting that the western coast experiences more rainfall and wind than further north. The location’s climate is classified as subarctic (Dfc) by Köppen and Geiger’s classi- fication system, which is widely used for classifying the world’s climate. It has been designed by W.P. Köppen and is based on annual and monthly mean values of temperature and precipitation. The climate around the globe can be categorised in five major climate types; in Figure 14 it is possible to see which are the subarctic areas. The subarctic climate (also called subpolar climate) is characterised by long, usually very cold winters, and short, cool to mild summers. It is found on large landmass- es, away from the moderating effects of an ocean, generally at latitudes from 50° to 70°N poleward of the humid continental climates. The mean annual temperature of Trondheim’s official weather station in Voll (68860) is 4.8 °C. Average annual precipitation is 855 mm. The prevailing wind direction in Trondheim is south west (225° from the north).

15. [14] Peel M C, Finlayson B L and McMahon T A 2007 Hydrol. Earth Syst. Sci. Discuss. 4 439–73

34 General Assembly Eecutive Board Centre Managment all partners 8 representatives: Team 6 user partner centre director, representatives, centre industry, NTNU, SINTEF communications, advisor & coordina- tor, work package leaders

Scientific Committee

WP1 WP2 WP3 WP4 WP5 WP6 analytical policy responsive energy local energy pilot projects framework measures, and energy flexible system and living NTNUfor design - NORWEGIANinnovation efficient UNIVERSITY neigh- optimisationOF SCIENCE labs ANDand planning TECHNOLOGY and business buildings bourhoods in a larger of ZEN models system

Faculty of The Norwegian University of Science Architecture and and Technology is a public research Design (AD) university with campuses in the cities of Trondheim, Gjøvik, and Ålesund, and has become the largest university in Norway. Faculty of Humanities (HF) NTNU has the main national responsibility for education and research in engineering and technology, originated Faculty of Information from Norwegian Institute of Technology Technology and (NTH). Electrical Engineering In addition to engineering and natural (IE) sciences, the university offers higher education in other academic disciplines ranging from social sciences, the arts, Faculty of Engineering (IV) medical and life sciences, teacher education, architecture and fine art.

Moreover, NTNU works in close collabo- Faculty of Medicine Faculties and and Health Sciences ration with SINTEF, Scandinavia’s largest departments (MH) independent research institution and one of Europe’s largest organizations in contract research. SINTEF has specialized expertise in technology, medicine Faculty of Natural Sciences (NV) and the social sciences. NTNU and SIN- TEF are co-located in Trondheim.

NTNU’s collaboration with business and Faculty of Social and industry has a strong focus on innova- Educational Sciences tion and entrepreneurship. Thanks to (SU) the cooperation with especially SINTEF and Statoil, NTNU is ranked as number Faculty of Economics one in the world for collaboration with and Management industrial partners, according to the (OK) Times Higher Education (THE) World University Ranking in March 2017. NTNU is also one of the highest ranked institu- NTNU University tions on the Leiden Ranking indicators Museum (VM) for collaboration with industry [1].

35 STRATEGIC RESEARCH AREAS

Lore m ipsu NTNU is also involved in a wide research program (2014-2023) regarding different main topic; through interdisciplinary cooperation, NTNU’s strategic research areas aim to address complex challenges of great importance for society [16].

SUSTAINABILITY ENERGY OCEANS HEALTH Aims regard sustain- It focuses on the NTNU Oceans gen- The main research ar- able use and conser- development and erates and supports eas regard the health vation of biodiver- integration of a re- key research activ- promotion, preven- sity and ecosystem newable energy ities on maritime tion and empower- services; transition supply. Aims regards transport - autono- ment; the diagnos- towards a circular minimizing the eco- mous ships; deep- tic and therapy; the economy and sus- logical footprint in ocean research; polar ICT-systems, welfare tainable produc- developing the ex- science and technol- technology and or- tion; climate change isting resources, and ogy; sustainable sea- ganization on health mitigation and ad- to invest in research food and marine bio services. The aim s aptation; transition related to renewable resources; marine to create innovative towards smart sus- energy sources, stor- minerals and renew- solutions to complex tainable cities and age solutions, ener- ables; marine envi- health challenges. built environment. gy consumption and ronment, society and transportation. sustainability.

NTNU also participates in several projects in the EU Framework Programmes. Sev- eral researchers at NTNU have received basic research grants from the European Research Council (ERC).

CAMPUSES IN TRONDHEIM

NTNU in Trondheim collects most of the courses and students of the whole university; of the nearly 180.000 inhabitants, more then 36.000 are students. That means that about the 15 of the population is made of students, both Norwegian and for- eigners. In Trondheim the University is divided in eleven different campuses, which host different faculties and departments; the first one that has been built and also the biggest is Gløshaugen Campus.

The location of the different Campus in the city is showed in the following Figure 18:

16. web-site Norwegian University of Science and Technology

36 Olavskvartalet Solsiden Trondheim city centre

Kalvskinnet

Tunga Øya Tiholt

Elgeseter Gløshaugen

Moholt

Lerkendal Dragvoll

Figure 15: location of the eleven campuses of NTNU in Trondheim.

STUDENTS AT NTNU

NTNU welcomes students from all over the world, and offers more than 60 international master programmes as well as PhD programmes, which all are taught in English. PhD vacancies are announced on the university website and are paid as academic staff, that offers one of the world’s best PhD fellowships as well as em- ployment benefits under Norwegian law. Norwegian University of Science and Technology counts more than 40 000 students (registered during the 2018 academic year) and more of the 85% of them at- tend courses at the Campus in Trondheim (Gløshaugen). NTNU calls foreign students from all over the world and it has 300 cooperative or exchange agreements with 60 universities worldwid.

37 42031 Figure 16: numbers related to students at NTNU. 36104

3572

NTNU NTNU international students in students in students at Norway Trondheim NTNU

Besides these, there are 4175 registered students in further education programmes (experience-based master’s and ICT courses) and 7220 bachelor’s and master’s degrees awarded (data from 2018).

STUDENTS’ LIFE

The NTNU provides both accommodation and services to the students; nearby the Campus and spread all around the cities there students residences for both Norwe- gian and international people. There are nine student residences in Trondheim and all of them are located near the campuses. The organization responsible for the welfare of the students is the SIT and it provides housing for about three thousand students. Moreover, In the campuses there are eleven libraries, seventeen canteen or cafés, one bookstore, eighteen reading rooms, thirteen computer labs.

18 17

13 11

3 1

libraries canteen or bookstore reading computer gyms cafs rooms labs Figure 17: facilities for students on or near the campuses on Trondheim.

The main campus in the one located in Gløshaugen;10 it hosts most of the university 7 science and engineering buildings. 7 3 38

libraries canteen or bookstore reading computer gyms cafs rooms labs

NTNU campuses NTNU Gløshaugen campus GLØSHAUGEN CAMPUS

LOCATION AND ACCESSIBILITY

Olavskvartalet Solsiden Trondheim city centre

Kalvskinnet

Tunga Øya Tiholt

Elgeseter Gløshaugen

Moholt

Lerkendal Dragvoll

Gløshaugen campus is in a strategic point in the pattern of the city and it also near to the centre (1.8 km), as showed in the following scheme.

24 minutes 8 minutes

10 minutes 9 minutes Figure 18: travel time by transport type to reach the city centre from Gløshaugen Campus.

39 FACILITIES

campus area 18 libraries 17 canteen or caf bookstore 13 reading11 room SW NN computer lab 50 100

3 1 The NTNU Gløshaugen campus provides many services to the students inside the campuslibraries are. In canteenGløshaugen or campusbookstore therereading are three libraries,computer seven canteengyms or cafés, one bookstorecafs “Akademika”, ten readingrooms rooms, six computerlabs labs.

10 7

7 3

libraries canteen or bookstore reading computer gyms cafs rooms labs

NTNU campuses Figure 19: facilities for students in Gløshaugen cam- NTNU Gløshaugen campus pus

40 BOUNDARIES AND BUILDINGS’ USE

In Gløshaugen Campus there are different buildings type, all with mixed intended use; they can be seen in the following Figure 20:

office+educational office+labs+ educational SW NN office+labs 50 100

Figure 20: map of the different type of use of the buildings in the Campus. OE

OLE In the Campus there are several type of buildings; most of them are both office, laboratories and educational. OL Due to this variety, the Campus can be considered as a neighbourhood of a city.

41 ELEVATION OF THE TERRAIN 40

35 40

40 45 30 35 50

45 40 50 35 30 45 40

SW 35 N 50 100 N

Figure 21: map of the elevation of the terrain in Gløshaugen campus

From a topography point of view, it is worth to say that the NTNU Gløshaugen campus, for the northern half, has a relatively uniform elevation of about 47 m asl; the southern part of the campus presents a steep trend, from 50 m to 35 m asl.

HISTORICAL BACKGROUND

SW NN

42 GLØSHAUGEN CAMPUS’ RENOVATION PROJECT

Over the next ten years, all NTNU’s academic communities in Trondheim will be gathered from dispersed locations to a single campus in the area around Gløshau- gen. The Norwegian State will fund new buildings totalling 92.000 square metres and modernization of up to 45.000 square metres of existing areas at NTNU. The relocation of the campus means that the daily activities of nearly 40 000 students and 7000 employees will take place in the centre of Trondheim. In addition to giving the university a stronger presence in the area, this will improve the basis for business establishment, innovation, collaboration with the university’s partners and new services in the area. The main aim of the project is to ensure that NTNU’s campus becomes a strategic tool for realizing NTNU’s academic ambitions: different academic groups can work more closely together to solve the challenges that our society faces in the years ahead. In January 2016, NTNU Campus Development launched the first phase of the project, which involved defining the strategic direction for the future campus. In 2018, the Campus Development project progressed to the second phase: defining overarching needs and potential. On April 25, 2019, the City Council of Trondheim decided the planning programme for university and campus purposes.

campus development project

programme group

campus Rector development project Rectorate Council NTNU

project group

project manager

counsellor project members

workers learning arenas

collaborators students NTNU 2020 volunteers NTNU 2040 NTNU 2060

This chapter has been developed in collaboration with Sara Corio., Master Thesis student in Buildings Engineerign and Architecture at University of Genoa, Italy.

43 chapter 3 BUILDINGS AND ENERGY: BACKGROUND AND DEFINITIONS In this chapter an overview of the relationship between buildings and energy will be presented. First, energy requirement for buildings in Norway will be analysed; following, definition regarding the embodied, operational and demolition factors will be given. Moreover, it will explain what a low energy building is and the techniques to reach its performances.

45 BUILDINGS AND ENERGY: BACKGROUND AND DEFINITIONS

The consumption of energy in buildings is dramatically increased over the past decades due to population growth; as a consequence, also the GHG emission and coal consumption have been increased considerably. Total global energy consumption is expected to increase by approximately 32% before 2035 [17]; for this reason building energy efficiencyembodied can provide a solution to energy shortages and also to greenhouse gases carbonemissions; related to the buildings sector, the emissions that have to be considered2 are those of CO2 (carbon dioxide). Consequently to the energy buildingconsumption, buildings generate nearly the 40% of annual global GHG emissions,operations generate both from building operations (e.g. heating, ventilation, electricity, air conditioning2 system,..) and embodied carbon of materials. Emissions due to the operation represent the 72% of the total emission, while the embodied carbon the 28% [18].

Figure 22: the scheme show which percentage of the emissions related to the buildings is due to the operations part and which is correlated to the embodied embodied carbon [19]. carbon 2 building operations 2

There six factors which influence the most the energy consumption of a building [3]: the building body, the climate, technical equipment and energy systems, operation and maintenance, human activities and behaviour, indoor environment. These factors can be divided in two different categories: physical and human influential factors; Within physical factors, climate, building body and technical equipment belong to energy systems. These are factors that have fixed parameters and are

17. Source: annex “Total energy use in buildings - analysis and evaluation methods”, by Yoshino, H., Hong., and N. Nord. 18. As written in the report “Global energy transformation” by IRENA (International Renewable Energy Agency). 19. As reported in the article “Why the building sector?” by Architecture 2030.

46 difficult to do anything about. Within Humanly influential factors belong to operation and maintenance, human activities and behaviour and indoor environment quality. These are all factors that are easily influenced and vary from user to user. When you combine these factors you get the real energy consumption, or the ac- tual energy use. The factors which influence the energy consumption in a building are illustrated in Figure 23:

Technical and Human physical influenced factors factors

operation and climate maintainence

building occupant envelope behaviour

building indoor equipment environment

BUILDING PERFORMANCE ENERGY USE Figure 23: the scheme shows the factors which influence the energy consumption in a building; they are both technical and human.

47 ENERGY REQUIREMENTS FOR BUILDINGS

In this chapter, information regarding the requirements and regulation regarding buildings in Norway are given.

TEK (the Norwegian Building Regulation) is largely a function-based regulation. This means that the technical requirements are specified in the form of either func- tions or performance in all essential areas. Function requirements are described in the guidelines to the Norwegian Building Regulation in the form of qualitative or quantitative performance. These qualitative or quantitative performances are des- ignated as pre-accepted performance. This means that by complying with these pre-accepted performances you are deemed to comply with the Norwegian Build- ing Regulation.

PAST ENERGY REQUIREMENTS Building standards have a long history in Norway, since the first energy requirements were introduced in the 1949. The Ministry of Local Government and Mod- ernisation has the responsibility for the determination of the requirements of the Technical Regulation regarding buildings [20]. The regulations give the minimum standards that buildings have to respect in order to make their construction legal. New buildings correspond to only about 1-2 % of the building stock per year. On the other hand, buildings have a long lifetime, and the current energy requirements of the regulations will therefore influence energy use for many years to come. The energy requirements have been revised and made stricter a number of times, most recently from 1 July 2017 with the new regulation TEK17.

CURRENT ENERGY REQUIREMENTS The new regulation TEK17 was introduced on July 1st of 2017 and with it, selected requirements were simplified in order to make the regulation easier to understand. Compared to the previous TEK10, some energy parameters changed and they became more restrictive in particular the ones related to the transmittance U-value [W/m2K]. New minimum U-value requirements are showed in Table 1 below [5].

U-value outer U-value roofs U-value floors on U-value windows Leakage figures walls [W/(m2K)] [W/(m2K)] ground and facing and doors, with at 50 Pa pressure open air [W/(m2K)] frames [W/(m2K)] differential: ≤ 0.22 ≤ 0.18 ≤ 0.18 ≤ 1.2 ≤ 1.5

Table 1: minimum U-values for buildings’ components introduced in the TEK17 [21].

20. Source: article “Sustainable buildings” in the website “Energy facts Norway”. 21. Source: TEK17, Norwegian Regulations on technical requirements for construction works.

48 FUTURE ENERGY REQUIREMENTS Goals for the future, according to the Norwegian Government will be focus on the development of renewable energy, usage of the energy in a more efficient way, strengthened security of supply, business development and value creation through efficient utilization of profitable renewable resources [22]. The aim is to reduce the energy consumption by 30% by 2030.

22: source: report “Power for change - energy policy against 2030”, by Norwegian Parliament.

49 LCA,LCEA and LCCA

Life cycle assessment (LCA) is a tool to evaluate the environmental impact of a process during its whole phases, i.e. “from cradle to grave”; this means that all the entire cycle has to be considered, including raw material extraction, manufacturing, use and end of life scenario (EOL). LCA evaluates all the resources inputs and it comprehend other two variants: Life Cycle Energy Analysis (LCEA) and Life Cycle Carbon Emissions Assessment (LCCA); the first one is focused on resources input and the second one on the CO2 equivalent emissions.

A scheme with the workflow of the LCA is showed in the Figure 24 [2]:

LCE LCCA

energy CO2 emissions

e u

n r

t t

l t

input c output

r water r solid waste

c u

e s

m d

o r e

n p n

w a

a o o d

a a

r m materials c liquid waste

transportation

Figure 24: different phases of the LCA workflow are presented. LCE and LCCA represent two steps of the entire process. [23]

Life Cycle Energy Analysis (LCEA) is the approach that considers all the energy input to a building during his life-span; the system boundaries of this analysis include the energy use during different phases: manufacturing, use and demolition. What is included in the LCE is showed in the following equation: LCEA = EE + OE + DE where EE is the embodied energy, OE is the operational energy and DE the demolition energy. The same equation can also be written regarding the emissions: LCCA = EE + OE + DE where EE are the embodied emissions, OE are the operational emissions and DE are the demolishing one.

Definitions of these terms and their relationship with the CO2 emission will be explained in the following section.

23: source “A review on Life Cycle Assessment, Life Cycle Energy Assessment and Life Cycle Car- bon Emissions Assessment on buildings”, C.K. Chau, T.M. Leung, W.Y. Ng. 50 EMBODIED, OPERATIONAL AND DEMOLITION ENERGY/ EMISSIONS The terms energy and emission are strictly related and dependent to each other. Operational emissions regard all the activities related to the use of the building and are the emissions emitted in order to keep the indoor environment within the de- sired range; operational energy is the necessary energy to ensure the user’s comfort and the correct functioning of systems such as heating, cooling, ventilation, etc. Hence operational emissions are related to the occupants, the embodied ones (EE) don’t depend on the occupancy because they are peculiar of the materials; embodied energy is the total energy required for the extraction, processing, manufacture and delivery of building materials to the building site.

Energy consumption produces CO2, which contributes to greenhouse gas emissions, so embodied energy is considered an indicator of the overall environmental impact of building materials and systems.

The greenhouse gas emissions can be expressed in terms of kgCO2 or kgCO2 equiv- alence calculate during the life cycle overview, meaning that emissions caused by extraction of raw materials, their manufacture, transportation, assembly, use, maintenance, repair, replacement, energy demand during operation, deconstruction, re- use, recovery, disposal and end of life aspects of the building have to be considered.

A carbon dioxide equivalent or CO2 equivalent, abbreviated as CO2-eq is a metric measure used to compare the emissions from various greenhouse gases on the basis of their global-warming potential (GWP), by converting amounts of other gases to the equivalent amount of carbon dioxide with the same global warming potential. Carbon dioxide equivalents are commonly expressed as million metric tonnes of carbon dioxide equivalents, abbreviated as MMTCDE. The carbon dioxide equivalent for a gas is derived by multiplying the tonnes of the gas by the associated GWP:

MMTCDE = (million metric tonnes of a gas) * (GWP of the gas) [24]

24. As defined by the “Eurostat”, the statistical office of European Union, 51 The carbon emissions of building materials are made up of the direct carbon emission and the indirect carbon emission. The carbon emissions of the raw materials and building materials’ producing process are the two important parts to evaluate the direct carbon emission. Indirect carbon emission which was generated from the depreciation of equipment and buildings, the management in each link and the environmental process of garbage processing and transportation should be esti- mated.

Figure 25 below shows the different energy and emissions that concur during the Life Cycle Energy and the Life Cycle Carbon Emissions Assessment.

Life Cycle Energy and emissions from buildings

building material’s embodied energy/emissions building’s operational energy/emissions

direct portion indirect portion

prefabrication initial embodied heating and cooling activities energy/emissions lighting and ventilation transport on site recurrent embodied operating appliances and and off site energy/emissions equipment

construction and demolition other building related installation on site energy/emissions energy use

Figure 25: the scheme shows the different energy and emissions provision that have to be considered during a Life Cycle Energy and a Life Cycle Carbon Emissions Assessment.

Until recently, only the operational energy was considered because it represents the most part of the total; however, due to the advent of energy efficient equipment and appliances, along with more advanced and effective insulation materials, the potential of curbing operational energy has increased. As a result, the current em- phasis has shifted to included embodied energy in building materials. Regarding buildings, also the demolition energy plays an important role in the Life Cycle Assessment: it is the energy required at the end of the buildings’ service life to demolish it and to transport the material to land fill site. In the Figure 26 in the following page, a conceptual scheme of the emissions related to the buildings is given.

52 n s o i s m i e

d e n ) o o d i operational i m b v a t o e emissions end of life l n a e i

r scenario i t ( n i 2-3 years 40-60 years 3-6 years time (years)

Figure 26: during the life-span of a building, (renovation) three different kind of embodied emissions have to be considered: the initial ones, the operational emissions (comprehended the emissions due to renovation) and the emissions resulting from the demolition of the building at the end of its life. [25]

25: source “Operational vs. embodied emissions in buildings: a review of current trends”, by T. Ibn-Mohammeda, R. Greenougha, S. Taylorb, L. Ozawa-Meidaa, A. Acquaye. 53 LOW ENERGY BUILDINGS

Low energy buildings have specific design that demand less operating and life cycle energy and that is built according to conventional criteria; they use both techniques and system in order to reach the comfort with low energy use and low GHGs emissions.

The main approaches that can be followed to improve the energy usage of a building, and consequently its emissions, regard the passive techniques, the use of low embodied energy materials and the adoption of systems based on renewable en- ergies.

PASSIVE TECHNIQUES Using high levels of air tightness and insulation can reduce the heat loss from the building and hence its needing of heating during winter. Dense materials can be use in building construction in order to create “thermal mass”, which reduces temperatures flow by storing heat and releasing it during the day; this helps to limit overheating during the hot season or when the level of occupancy in the building is higher. Buildings can be designed to make the best use of sunlight by orientating them relative to south and arranging windows to maximise daylight and allow sunlight in during winter, but to limit direct sunlight penetration during summer when it can cause overheating. The use of high efficiency, low emissivity glazing allows high levels of daylight in whist reducing heat losses through windows. Passive design can use wind-driven and stack-driven natural ventilation to provide cooling in summer without the need for air conditioning. To minimise heat losses during cold weather, airflow is reduced to the minimum needed to provide fresh air.

LOW EMBODIED ENERGY MATERIALS

Their use can reduce the energy consumption of a building and underrate the environmental impacts of buildings; especially the envelope has a crucial role because it influence the heating, cooling and light demand of the building. The use of modern building materials should be carried out paying attention to the energy intensity of materials, the natural resources and raw materials consumed, the recycling and safe disposal, and the impact on the environment. The carbon emissions of building materials are made up of the direct carbon emission and the indirect carbon emission. The carbon emissions of the raw materials and building materials’ producing process are the two important parts to evaluate the direct carbon emission. Indirect carbon emission which was generated from the depreciation of equipment and buildings, the management in each link and the environmental process of garbage processing and transportation should be esti- mated.

54 Lightweight building construction such as timber frame is usually lower in embodied energy than heavyweight construction. In climates with greater heating and cooling requirements and significant day– night temperature variations, embodied energy in a high level of well-insulated thermal mass can significantly offset the energy used for heating and cooling. There is little benefit in building a house with high embodied energy in the thermal mass or other elements of the envelope in areas where heating and cooling requirements are minimal or where other passive design principles are not applied. Materials with the lowest embodied energy are usually consumed in large quanti- ties; materials with high energy content are often used in much smaller amounts. As a result, the greatest amount of embodied energy in a building can be from either low embodied energy materials such as timber or high embodied energy materials such as steel. Embodied energy content varies greatly with different construction types. In many cases a higher embodied energy level can be justified if it contributes to lower operating energy.

The embodied energy of the most common building material is showed in the scheme below in Figure 27:

0 50 100 150 200 250 steel aluminium copper timber plastic concrete masonry glass fabric plaster stone ceramics

embodied energy (G) Figure 27: the graph underlines the embodied energy of some of the main building materials during their life-span; concrete, plastic and steel have a high level of embodied energy, contrariwise natural material like timber, stone andSOURCE: copper CSIRO research have a lower impact. [26]

26: source “Sustainable Manufacturing: Design and Construction Strategies for Manufactured Con- struction”, by A. E Fenner and C. J. Kibbert. 55 THE IMPORTANCE OF SYSTEMS AND RENEWABLE ENERGY

Plant systems plays an essential role in design: efficient systems guarantee significant energy savings and a high level of perceived well-being. In order to have a high efficiency of the plant, it is not enough to choose a high efficiency generator, but it is also important that all the other components, such as the regulation system, distribution system of the heat carrier to the heating elements, heating elements, have a high efficiency.

To further optimize plant consumption and reduce CO2 emissions, it is ideal to use alternative energy sources; a source is called renewable if it derived from a natural process that are continuously replenished (sun, water, wind, earth’s heat, bio- mass,..). Renewable sources that can be use to produce energy for buildings are: - solar photovoltaic: this method produces the most important renewable energy sources in terms of global installed capacity; PV (photovoltaic) cells convert sunlight directly into electricity. - solar thermal: this term is used to describe a system in which energy from the sun is harvested to be used for its heat; solar thermal collectors can be used to supply the heating system of buildings. - geothermal energy: is the second most abundant source of heat on heat after solar energy; it can be used for electricity generation, hot water, heating and cooling. - heat pumps: according to the European Standard EN14511 the definition of heat pump is: ‘encased assembly or assemblies designed as a unit to provide delivery of heat. It includes an electrically operated refrigeration system for heating. It can have means for cooling, circulating, cleaning and dehumidifying the air. The cooling is by means of reversing the refrigeration cycle.’ - wind turbines: they can be use both in large scale and domestic units; they can be used to produce electricity.

Figure 28: the best combination regarding the systems on a building, is the result of the combination between high efficiency plants and the use of renewable source for the energy production.

chapter 4 ENERGY MODELLING IN URBAN SCALE The energy modelling of an entire district or city is a new approach that is still developing. This method gives a different and wider point of view regarding the energy simulation and it will be presented in this chapter. Moreover, concepts like assessment criteria and key performance indicators will be deepen for a better comprehension of the topic.

59 ENERGY MODELLING IN URBAN SCALE

The transition from Zero Emission Building to the larger view of Zero Emission Neighbourhoods has brought to a different approach towards the energy evaluation: it will be fundamental to rate the group of buildings all together and not to consider them as single entity. Moreover, the chance of energy exchange between buildings has to be considered because one participant’s excess production may be applied towards another member’s consumption needs.

This change of perspective led to consider Urban Building Energy Model (UBEM) instead of Building Energy Model (BEM). The design development that considers the individual building properties (BEM) is already widely used in lot of countries; with this approach, the model is provided with geometry, usage schedules and construction data and it could regard both existing and new buildings. BEM needs to expand its aim to the urban scale, in order to became globally affordable; UBEM’s approach is to apply energy models to a group of buildings with the purpose of pre- dicting energy use and environmental conditions for a whole neighbourhood. [27]

Before approaching the energy-modelling of the building (or group of them), it will be necessary to point out the categories that are important to effort during the process and it will be important to create a connection between key performance indicators (KPIs) for buildings and neighbourhood assessment criteria; it will be also required to define the system boundaries of the neighbourhood. In the paragraphs/ chapter below, the definitions of the terms will be given.

SYSTEM BOUNDARIES

Since the develop of the work considers interdisciplinary knowledge and experiences from different fields and involves people from diverse professional- back grounds, for the ZEN Research Centre is really important to define a common system boundaries that can be comprehend by everyone. The boundary is different from one project to another because it is strongly related to the seven categories listed below and consequently depends on the assessment criteria and Key Perfor- mance Indicators.

The ZEN defines as parts of the system boundaries: - building assessment boundary: describes which elements of buildings should be included in the system boundary and it can differs for each category listed above. - neighbourhood assessment boundary: describes which elements of the neigh-

27. source: “Urban building energy modeling: a review of a nascent field”, by C. F. Reinhart and C. C. Davila.

60 bourhood should be included in the system boundary and also this may vary with the categories. - Life Cycle Assessment system boundary: defines what is included or not in the assessment; the ZEN Research Centre considers all the life cycle from the extraction of raw materials to deconstruction or recovery phase.

ASSESSMENT CRITERIA AND KPIS

In the last decades lot of environmental assessment method have been developed in order to evaluate the buildings performance and to quantify their environmental impact; the most acclaimed are BREEAM (UK), LEED (US) and Green Star (AU). Because of the global variation in climate, culture and economics, is not easy to ascertain if these international assessment tools are efficient outside their country. The criteria describe the relationship between the buildings and their surround- ings and it is necessary adjust some of the criteria according to the specific environment. For these reasons, the ZEN Research Centre has drawn up a range of assessment criteria for buildings and a set of key performance indicators related to them. [28]

Assessment criteria: are preliminary conditions that have to be achieved by the neighbourhood to be considered sustainable in all its aspects (social, economic, environmental). They can be mandatory or not, and they may be interconnected: it means that the achievement of one criteria depends on the achievement of another. The criteria uses Key Performance Indicators (KPIs) that can be both quantitative and qualitative. [29] Key Performance Indicators (KPIs): have to be considered because they can support priorities and needs of stakeholders in their efforts to improve the energy performance of the buildings. KPIs can be different and they are useful because they make it easier to measure and control the progress of the neighbourhood during the time and compare it to another project; they have to be used during the design, construction and operation phases.

Every assessment criteria and KPIs are related to a specific category: this topic will be discussed more deeply in the following section.

28. source: “Sustainability Assessment Criteria for Building Systems in Iran”, by H. Zabihi, F. Habib and L. Mirsaeedie. 29. source: report n. 7 “Definition, key performance indicators and assessment criteria”, by the REsearch Centre on Zero Emission Neighbourhood in Smart Cities (ZEN).

61 CATEGORIES

The Zero Emission Neighbourhoods Research Centre listed seven different categories that may have one or more assessment criteria and for each one of those a set of key performance indicators; these categories have to be considered in all the phases related to the buildings.

Energy and GHG (greenhouses gas) emissions: this two categories are strictly related and the changing of one influences the other. As deeply explained in the Chapter “Background and definitions” these topics are really important and strongly related to the buildings’ environment. To evaluate the energy efficiency of buildings, KPIs are calculated according to the building assessment boundary and include the energy demand for cooling, heating, domestic hot water, ventilation, dehumidification, lighting; regarding the energy carries, KPIs are calculated according neighbourhood assessment boundary and involve energy demand for refrigeration, transport inside buildings, data serv- ers, charging for electric vehicles, outdoor lighting. KPIs for this category are total GHG emissions and GHG emission reduction; they are calculated according to both the building assessment boundary and neighbourhood assessment boundary.

Mobility: involve both the mobility inside a neighbourhood and the one between two or more neighbourhoods. It is defined by the transport’s patterns that can be used by inhabitants and other users, to and from the neighbourhood. Due to the increasing of the population expected in the next decades, the design of a smarter mobility and the introduction of more green way of transport will be crucial. Residential mobility is a highly structure process with impact both on the house-

25000 38 ) )

n world population n s o i o i 20000 cars production 36 l m i l ( m i l (

n o n 15000 34 i t i o t a c u l u p

10000 32 d o o p

p r

d l s

5000 30 r a c w o r 0 1950 1970 1990 2010 2030 2050 2070 2090 2100 Figure 29: global population growth and cars year production in the future [30].

30. source: statistics by OICA (International Organization of Motorvehicle Manufacturers).

62 holds who move and the places they choose in their relocation behaviour. In order to reach the aim of zero emission, it is strongly important to promote sustainable transport design and upgrade the smart mobility systems both in local and regional scale. The mode of transport KPIs refers to the percentage of green mobility in the neighbourhood scale and considers all the different way of movement (walking, bus, car, cycling, metro, train, boat, bus). Another KPIs for this category consider the access to public transport, that examines if the neighbourhoods are well linked to other areas of the city through public transport.

Economy: it refers to economy sustainability expressed in terms of buildings’ life cycle costs and energy demand; the indicator for this category considers both the neighbourhood and the building boundaries because it regards the life cycle cost (LCC) that includes the investment, annual cost and demolishing cost. To realize the potential in local energy resources in ZEN, new business models and market design will be an important aspect to consider.

Spatial qualities: it is about strategies, policies, design and use of spaces; in a neighbourhood’s scale, this category is important because the most effective design of a group of buildings is the one that guarantees high spatial quality for the users. There are three different KPIs for this category, all considering the neighbourhood assessment boundary: demographic needs and consultation plan, delivery and proximity to amenities, public spaces; the latter is calculated qualitatively and it focuses on walkability. The demographic needs and consultation plan indicator identifies the stakeholders for the neighbourhood development and it is measured qualitatively. The indicator of delivery and proximity to amenities considers the number of facilities around the neighbourhood and their distance from the buildings.

Power/load: related to the energy system of a building, power is the immediate load on the grid and it is measured in kW; if it is referred to the value of energy in one hour, it is measured in kW/h. A zero emission neighbourhood has to manage the energy flows both within and between buildings and has to be flexible. The theme of energy flexibility is crucial: it can be described as the capability of the buildings to control the energy demand in accordance with external factors such as local climate, occupants’ needs and the surrounding grid, all respecting the users comfort. In the next 30 years, according to the population’s growing, more and more power from the grid will be demanded because the use of electricity will increase considerably; for that reason it will be significantly important to shift from a system based on generation-on-demand to a system in which the energy usage is controlled according to the intermittent energy production. In order to achieve the aim of a large-scale of renewable energy sources production, the energy consumptions will have to become more flexible in order not to upgrade the electricity grid due to capacity issues. The KPIs for this category considers energy flow of the operational phase and they

63 25000 25000 E ) ) O

n world population o D - i 20000 20000 electricity demand B M /

( m i l d

o n 15000 15000 i a n t a m e u l d

10000 10000 y o t p

c i i d l r t

5000 5000 c w o r l e e 0 Figure 30: global population1950 1970 growth1990 and electrici2010 - 2030 2050 2070 2090 2100 ty demand in the future [31]. year

are divided in indicators for the power/load performance and power/load flexibility. The first ones use the neighbourhood assessment boundary and include yearly net load profile, peak load, peak export, net load duration and utilization factor. Power/load flexibility indicators can refer to different assessment boundaries. The theme of flexibility is crucial for a zero emission neighbourhood in order to manage the energy flows and create and exchange with the surrounding energy system.

Innovations: in ZEN Research Centre is defined as something new that is of value the stakeholders; includes new or improved business models, processes, products and services and how the organizations can support innovations. It is extremely important to facilitate the birth and the grow of new ideas, inventions and innovation. In order to design, build, transform and manage sustainable neighbourhoods, the innovation strategy has to follow three main pillars showed in the following Figure 10.

testing and demonstration visualize and exhibit open innovation to prepare for market milestones and succes implementation stories

means that innovations are to test and demostrate is it is important to share the developed in interplay usefull in order to ensure a results in order to grow the with others instead of in less complicated process information grid and closed circuits. towards market compare the different implementation. results. Figure 31: explication of the innovation category through its main pillars [32].

31. source: report “Nuclear Energy: An Alternative Energy Source For Turkey”, by D. Akkurt, H. Akyildirim, M. Ozturk, N. Özek. 32. source: “Annual Report 2017”, by the Research Centre on Zero Emission Neighbourhood in Smart Cities (ZEN).

PALETTE COLORI

Lore m ipsu In the tab on Figure 32, all the information regarding the categories and their rela-

tionship with the assessment criteria and KPIs are listed.

e s a h p l a n o i t a r e p O

✓ ✓ ✓

e s a h p t l i u b s A

✓ ✓ ✓

e s a h p n g i s e d d e l i a t e D

✓ ✓ ✓

e s a h p n g i s e d y l r a E

✓ ✓ ✓

n o i t a r a p e r p d n a f e i r B

✓ ✓ ✓

s e s a h p g n i n n a l P ✓ ✓ ✓ and 3451 NS 3720, NS 3720, Standard ISO 52000 SN/TS 3031, SN/TS 3031, references NS-EN 15978, NS-EN 15978, NS3457-3, NS NS3457-3, B B N BN BN BN levels Building B, Building N, both BN neighbourhood 2eq /capita/yr Unit outdoor space/yroutdoor tCO 2eq 2 heated floor heated area/yr 2 a base case a base heated floor heated area/yr /m 2 /m kgCO 2eq 2eq % reduction compared to kWh/m kgCO kgCO buildings: and KPIs -peak load -peak export -net energy need - utilisation factor -total energy-total need -gross energy need Energy efficiency in Total GHG emissions Total Assessment criteria - yearly net load profile - net load duration curve Power/load performance: Power/load Energy Category GHG emissions

e s a h p l a n o i t a r e p O

✓ ✓ ✓ e s a h p t l i u b s A

✓ ✓ ✓ e s a h p n g i s e d d e l i a t e D

✓ ✓ ✓

e s a h p n g i s e d y l r a E

✓ ✓ ✓

n o i t a r a p e r p d n a f e i r B

✓ ✓ ✓

s e s a h p g n i n n a l P ✓ ✓ ✓ ZEN and Standard praxis, ZEN praxis, SN/TS 3031, SN/TS 3031, references Engineering EBC annex 52,EBC annex ISO 52000, IEA ISO 52000, research centreresearch BN BN levels Building B, Building N, both BN neighbourhood % % % KW KW KW KW kW Unit kWh/yr kWh/yr kWh/yr kWh/yr kWh/yr and KPIs -peak load - energy use -peak export - self generation - self - utilisation factor - exported energy - delivered energy Per energyPer carrier: - self consumption- self - energy generation Power/load flexibility: Power/load - daily net load profile Assessment criteria - yearly net load profile - net load duration curve Power/load performance: Power/load - colour coded carpet plot Energy Category Power/load

e s a h p l a n o i t a r e p O

✓ ✓ ✓ ✓ e s a h p t l i u b s A

✓ ✓ e s a h p n g i s e d d e l i a t e D

✓ ✓ ✓ ✓ e s a h p n g i s e d y l r a E

✓ ✓

n o i t a r a p e r p d n a f e i r B

✓

s e s a h p g n i n n a l P ✓ ✓ ✓ - and BREEAM BREEAM 15686-5, NS 3720, NS 3720, CityKEYS, 16627, ISO 16627, Standard NS 3451, NS NS 3451, references Nork prisbok 3454, NS-EN NS-EN 16258, NS-EN 16258, - B N N N N BN BN BN levels Building B, Building N, both BN neighbourhood - NOK Unit outdoor space/yr outdoor buildings) qualitative qualitative % of share 2 NOK/capita heated floor heated area/yr 2 no. of amenitiesno. meters (distance from (distance meters NOK/m NOK/m - and KPIs amenities Public Space consultation plan Mode of transport Life (LCC) cost cycle Assessment criteria Demographic needs and Demographic Delivery and proximity to Mobility Category Economy Innovation Spatial qualities

67 ENERGY-MODELLING AND BUILDING SIMULATION

In order to achieve the aim of a zero emission neighbourhoods, it is primary to develop an appropriate energy model to simulate the performance of the buildings. They are the most effective available analysis tools to evaluate the performance of a building. Before the simulation tools where used, architects and engineers had to do their own calculation based on predetermined values; this brought often to oversized energy system and poor energy usage. Today it is possible to easily tests new innovative ideas and technologies and make buildings more efficient. Energy-modelling is the computerized simulation of a building (or group of them) that focuses on energy consumption regarding utilities, air conditioning, lights, hot water,..; it can also be used to evaluate the payback of green energy resulting from solutions like wind turbines, solar and thermal panels, heat pumps.

To comprehend what energy modelling is, it is necessary understand building simulation: it is the process of using computer to build a virtual copy of a building. The structure is built from its component parts on a computer and a simulation is performed by taking that structure through the condition of an established period. Hence, building simulation is a way to quantitatively predict the future behaviour of the construction and it is used to: - predict the annual energy cost - predict the monthly energy consumption - predict annual emissions - compare and contrast different efficiency options - determinate life cycle payback of various options

Simulation tools are a meaningful support for planning and designing the energy systems in constructions; moreover they could represent a preliminary proof of energy optimization before carrying out more specific tests.

To create a proper model of a new or existing neighbourhood, at least these following task have to be developed.

input data output data 1 2 grouping 3 modelling 4 collection collection

Figure 33: the four standard different phases that have to be developed in order to do an energy simulation [33].

33. source: report “Urban building energy modelling e A review of a nascent field”, by C. F. Rein- PALETTE COLORIhart, C. Cerezo Davila. 68 1. input data collection: this task has to comprehend climate data, geometry of the buildings and energy usage schedules. - climate data: modelling microclimatic aspects is necessary to understand the behaviour of the particular building towards the surrounding environment, and between more buildings at the same time. - geometry: it is necessary consider the buildings’ shapes and windows opening ratio; this data has to be related with the terrain information (using GIS tools and databases). - energy usage data regarding the energy consumption of the buildings has to be collected; in order to calculate them, it is necessary to know information about the system that are in the constructions (e.g. heating, ventilation and air conditioning technologies) and report about users’ usage. The amount of required energy is necessary to value the quantity of energy that has to be produced from sustainable energy sources e.g. PV (photovoltaic), co-generation (CHP, combined heat and power), electrical energy storage system, etc. - GHG emissions: it is necessary to know and collect data about the embodied energy of materials and their embodied carbon in order to evaluate the total CO2 emissions; this information are strongly important to design solution in order to reach a zero emissions neighbourhood.

2. grouping: since it is difficult to collect all this information for every single building of the neighbourhood, it will be necessary for an UBEM to abstract a building stock into “reference building”, i.e. building definition that represent a group of buildings with similar properties. The creation of this archetypes provide for two different steps: segmentation of the buildings into groups depending on shape, use, age, systems, and characterization of thermal properties including construction assemblies, usage patterns and building systems.

3. modelling: the information collected in the previous step have to be combined into a thermal model, which then needs to be executed and the results have to be communicated to the user in a intelligible format.

4. output data collection: is the phase that allows the comprehension and the validation of the results. The simulation more the results are reliable, more the UBEM model will be able to support different design decisions; the errors range for a building it is usually between 7% and 21%, that are acceptable values if a group of buildings is considered.

69 REFERENCE BUILDINGS As introduced in the previous section regarding the grouping, it is useful to intro- duce some reference buildings; currently, there is no uniformed European definition to refer to; according to Annex III of the Energy Performance of Building Directory recast, RBs are “buildings characterized by and representative of their functionality and geographic location, including indoor and outdoor climate conditions”. They aim to represent the typical and average building stock in terms of climatic conditions and functionality. Using reference buildings could be efficient for the development of this work, since the buildings that have to be considered are a lot and it would take too long to collect information and model all of them properly.

There are three different methodologies to classify the reference buildings: 1. creation of an “example reference building”: it is used where there are not statistic data available and it based on studies and experts’ assumption. 2. selection of a real reference building: in this case, the RB is the most typical building in a certain category; this building is real and it has average characteristics that can be representative for other buildings of the stock. 3. creation of a “theoretical reference building”: this method uses statistical data in order to define RB as a statistical composite of the features found within a category of buildings in the stock. The building is made of the most commonly used materials and system that can be find in the stock. [34] Afterwards, the method that has been chosen and the reason why will be explained.

34. Source: report “Reference buildings for cost optimal analysis: Method of definition and application”, by S. P. Corgnati, E. Fabrizio, M. Filippi, V. Monetti.

chapter 5 TOOLS FOR ENERGY MODELLING IN URBAN SCALE In this chapter a review of the most advanced tools for energy simulation at urban scale will be presented; this phase has been necessary to understand which software would has been the best one to develop the energy simulation of the Gløshaugen Campus.

73 TOOLS FOR ENERGY MODELLING AT URBAN SCALE

In this chapter software for the simulation of smart neighbourhoods will be presented, considering the interaction between a large number of factors regarding the energy usage and the GHG emission due to the embodied energy. One of the tool will be chosen to simulate the NTNU Campus in Trondheim.

IESVE - IES Virtual Environment: Integrated Environmental Solution is a con- sulting and software company specialized in buildings’ performances; the tool IESVE is available in two different versions for architects or for engineers. With IESVE for Engineers it is possible work on a new building or on renovation project; the software offers a wide variety of modules that can be match differently depending on the study case. It can be used to test different design solutions, identify the best one and draw conclusion regarding:

- solar shading: calculates the position of the sun in the sky, track solar penetration throughout the building interior and calculate shadows. - building energy simulation: advanced dynamic thermal simulation, asses solar gain on surfaces, calculate building energy need/use. - daylight simulation and lighting design: prediction of daylight, analysis of artificial and natural lights. - HVAC system: calculate the size and the optimisation of the plant. - renewable energy: design and optimization of the system related to the thermal performances of the building. - airflow: simulate air flow by wind pressure and buoyancy forces. - climate analysis and weather.

Figure 34: IESVE software’s available modules lights [35]. airflow HVAC egress modules value/cost IESVE regulations rating system energy/carbon climate solar

This software is appropriate for the energy simulation of one building but was not created and adapted to consider a bigger neighbourhood scale; also, it is not an open source tool.

35. Source: IESVE web site.

74 CityBES - City Building Energy Saver: this web-data tool was developed by the Berkeley National Laboratory of the United States and the last update was released on December 2018. It can be used to simulate the energy performance of a city’s building stock from a small group of building to the urban scale; the tool allows to evaluate different options to reduce energy use and it is capable to model more than 10’000 buildings and identify energy savings of 30% to 50%. With CityBES is also possible to value the interaction between buildings in district-scale energy system.

weather building GIS databases

a data stock t a d

CityGML (3D city models) r e a

w EnergyPlus t CBES CityBES Visualization o f simulation

s API engine of performances s e

c a s energy urban energy energy retrofit building

s benchmarking planning analysis operations u

Figure 35: CityBES is made by three part: data, software and use case; all of them interact in order to visualize the final performances [36].

This tool was released with the aim of considering a city scale simulation but it is a web tool; this aspect could create problems related to the internet connection and sometimes it is not possible to use it.

UMI - Urban Modelling Interface: it is a tool based on Rhinoceros developed by the Sustainable Design Lab of MIT (Massachusetts Institute of Technology); its aim is to model the environmental performances of neighbourhoods and cities. This tool is divided in five different modules which are related to the daylight, energy (operational), lifecycle (embodied energy and GHG emissions), mobility (walkability around the neighbourhood) and site (floor area ratio). UMI needs other software to supported: Rhinoceros as modeller, EnergyPlus for thermal simulation, Pyton scripts for walkability evaluation and Daysim for daylight

36. Source: CityBes web site.

75 simulation; hence the tool is based on existing simulation software that have long- standing dynamic development teams. The advantage of this approach is that UMI profits from future improvementof others [37].

This tool allows to simulate the embodied energy of the buildings and GHG emissions, the energy use of the buildings, the annual outdoor thermal comfort, the neighbourhood-wide daylight availability and the walkability around the group of buildings.

daylight lifecycle [-] [KWh/kgCO2]

mode metric type result

buildings spatial daylight autonomy total value building floor continuous daylight autonomy normalized (sqm) facade&glazing sensors

energy mobility [KWh] [-]

type result by feet or by bycle

total value total energy site normalized (sqm) equipment [-] heating cooling domestic hot water overheating floor area ratio Figure 36: annual global buildings sector CO2 emission sectors: embodied carbon is [21].

The Laboratory that released the tool, provide a free guide but it contains few tutorials and most parts of the manual haven’t been written yet. Also, the number of data that have to be collect and put in the model to run the simulation is large and not often easy to obtain.

37. S o urce: conference paper “UMI - An urban simulation environment for building energy use, daylighting and walkability”, by C. F. Reinhart, T. Dogan, A. Jakubiec, T. Rakha and A. Sang.

76 CitySim: this software was released by the Ecole Polytechnique Federal de Laus- anne in 2014 and it has the aim to simulate and optimise the sustainability of urban settlements. It is a support tool for both planners and stakeholders to minimize the net use of non-renewable energy sources and the green-house gas emissions. This tool has a tree-based structure in which a district contains buildings, each building contain thermal zones, each thermal zone contain information about physic and geometry of the buildings. Each of these module contain several and specific data as showed in the Figure 37.

temperature umidity district climate data precipitation irradiation sunshine

temperature shading device buildings irradiance volume

walls composites short wave irradiation roof thermal insulation long wave irradiation ground zones surfaces temperature glazing ratio output PV production openings U-value solar production g-value thermal production

reflectance heating demand solar panels cooling demand surfaces PV ratio thermal ratio

number sensible heat occupancy radiant part latent heat profile

Figure 37: CitySIM has a three-based structure made of different modules: distrIct, buildings and thermal zones.

77 The tool focuses on the energy flows of multiple simplified building models and their interdependent relationship with their urban climate; it is able to quantify cooling and heating demand from one building to the urban scale inputting ge- ometrical and physical data. This tool present different cons: there are difficulties related to the definition of the 3D model; indeed only “.dxf” file can be imported in CitySim that it means that the geometry has to be created with Autocad 3D. Video tutorials and guides are not provide to help users to learn how to start using the program. Also, it is focused on the operational energy but it can’t evaluate the embodied energy and the GHG emissions [38].

CEA - City Energy Analyst: it is an open source tool created by ETH ( Eidgenös- sische Technische Hochschule) of Zurich in 2015; it can be used for the urban building simulation and it is one of the first initiative of computation tools for the design of low-carbon and efficient cities. It is a framework for buildings’ energy analysis at urban scale and it integrates existing methods in urban and energy planning domains; it is useful to evaluate different energy efficiency strategies in neighbourhoods.

The framework of the tools is organized in seven databases and six calculation modules, as explained in the Figure 17:

feedback urban scenario

archetypes technology performance demand data data target

4 6 distribution 5 resource 3 sensor data

1 2 weather data infrastructure

urban data Figure 38: in the scheme, the workflow of CEA is showed [38].

38. Source: conference paper “CitySIM: comprehensive micro-simulation of resource flows for sustainable urban planning”, by D. Robinson, Haldi, F. Kämpf, P. Perez, D. Rasheed, A. Wilke.

78 The databases regard the geo-referenced information of buildings, infrastructure, local resources and topography; the weather information, information about occupancy, archetypes, technology data, performance information.

Regarding the modules, they are the demand module, the resource potential module, the system technology module, the system optimization module, the decision module.

This tool is very complicated to approach and in order to use most of its modules, it is necessary to download few of other software and not all are open source. Also City Energy Analyst doesn’t have a specific model to evaluate the embodied energy or the greenhouses gases.

pro cons

urban scale closed-source IES-VE free tutorials and support better simulation for the indoor availability of several tests

open source web-data tool CityBES big model creation few tutorials available

open source based on existing softwares supported by other softwares UMI free tutorials intelligible interface open source foor information about the tool CitySIM urban scale few tutorials available intelligible interface

open source supported by many other softwares CEA urban scale complicate interface intelligible interface

Figure 39: in order to decide what tool was more suitable for the developing of the work about Gløshaugen Campus, a table with the pros and cons for each software have been done.

Following the analysis of this tool, the one that has been chosen to carry on the work regarding the Gløshaugen Campus is UMI from MIT; this tool it’s easy to approach and even if not lot of tutorials are provided, it is not complicated to understand the framework and how to use it. The results can be visualized with coloured geometry or with graphs that can be exported and opened with excel. More information about the specific futures of UMI will be given during the development of the simulation for the Gløshaugen Campus.

79 chapter 6 ENERGY MODELLING USING THE SOFTWARE UMI The simulation with the chosen software UMI is made of different steps which will be analysed in this chapter. First input data were collected and reference buildings were chosen, following the methodology that has been selected previously. The output data have been analysed and validated in order to prove that the energy model is correct.

81 ENERGY MODELLING USING THE SOFTWARE UMI

In this chapter the modelling method using the software UMI will be explained in all its phases; the work has been divided in different steps and each of them have aims to reach in order to proceed. Despite the four modelling phases that have been explained in the previous chapter “energy simulation in urban scale”, it has been necessary to increase the number of steps. The sixth phase “solution proposal” will be described in the following Chapter 5. The workflow is showed in the scheme below.

context input data model solution 1 2 3 4 simulation 5 output data 6 analisys collection creation proposal mapping climate data 3D model energy output data critical creation consumptions exporting and buildings blocking geometry data reading selection numbering reference template embodied output data solution buildings creation energy and validation approach grouping carbon scenario A buildings template properties assignment scenario B scenario A+B energy consumption

LCA data

Figure 40: phases that will be faced in order to develop the work with UMI.

CONTEXT ANALYSIS

context Theinput first data step thatmodel has been faced up in order to develop solutionthe work 1 2 3 4 simulation 5 output data 6 analisys regardscollection the analysiscreation of the Gløshaugen Campus’ context; therefore,proposal mapping climatedifferent data phases3D modelhave been deepenedenergy andoutput they data led to othercritical four different processes:creation the mapping,consumptions the numbering,exporting and the blockingbuildings and blocking geometry data reading selection the grouping of all the buildings of the buildings in the Campus. numbering reference template embodied output data solution buildings creation energy and validation approach grouping carbon scenario A buildings template properties assignment scenario B scenario A+B energy consumption

LCA data

82 MAPPING First of all it was necessary understand the position of the buildings in the area: using the tool Open Street Map, a plan scheme of the building has been drawn. This scheme helps to understand both the position of the whole campus in the urban context and the position of the buildings compared to the others. Regarding the Campus, the scheme displays that most of the buildings are all located following the same axis; this line has a North-West orientation. This regular pattern will be helpful during the modelling phase, because it will be easier to draw the surfaces since they are parallel to each other.

Another observation that can be done watching the scheme, is a first look at the geometry: plans are not regular but are made of different shapes assembled together. Also, to a single plan shape can correspond a building with different height. Since these aspect could make more difficult to model the 3D, a solution to simplify the buildings will be given in the following “blocking” phase.

SW NN campus area 50 100

Figure 41: identification of the campus area that also correspond to the project area.

83 BLOCKING As said previously, many buildings have not a regular plans and geometry, hence a phase named “blocking” has been introduced. As the word suggests, the buildings of the Campus have been analysed and then it has been decided if it would have been necessary divide them in more regular blocks. The blocking is necessary when dealing with two different situation (Figure 42): - regular/not regular plans to which correspond blocks with different heights: the building has to be split in two different simpler blocks. - regular/not regular plans to which correspond blocks that have different thermal features: if a part of a building has been renovated or added subsequently, it has to be divide. This because when the energy model will be built, blocks with different properties have to be considered as separated entities.

Figure 42: the first scheme show how buildings with different geometry have to been split in simpler blocks; similarly, buildings in which there are different thermal zones, have been divided.

Considering the buildings as the sum of simpler blocks will have both advantages and disadvantages: - the energy modelling with UMI will be easier to approach and faster for the tool to run it. - UMI will not recognize the building that has been divided as one only, but it will consider the different blocks. This means that the final conclusion have to be done regarding the different blocks and not the whole building.

The map with the different blocks is showed in Figure 43 in the following page:

84 SW 50 100 NN

Figure 43: the map shows how the different building were divided in order to create simpler blocks.

85 NUMBERING This phase has organizational motivation: number all the blocks which have been created in the previous section it will be useful to identify them in the following method phases. To not create confusion and to not redone a work, the number of the blocks are the same that are used to identify the buildings in all the official documents of the University. When a building had to be split in different blocks, this last maintain the number of the building followed by a letter. The result of this phase in showed in Figure 44 below.

301a 301c 301d 301e 301b 302a 302b 328 326b 329b 327 354 329a 325 303 323 326a 330a 322b 356 358 330b 330c 322a 304b 331a 331b 322c 304a 322d 331c 321b 324 333b 333a 321a 333c 319 321c 305b 357 334 317 321d 305a 305c 321e 306 321f 318b 318a 316 365 315a 307 314a 315b 313a 312 314b 311a 313b 311c 360c 310 311b 360b 308 360e 360a 360d 332

309

337a 337b

337c

SW 50 100 NN

Figure 44: numbers assignment to all the blocks of the Campus.

86 GROUPING Since the number of building to consider where nearly eighty and it would have taken too long to analyze them all, it has been necessary to group them in order to create what will be called “reference buildings” (RBs) in the next pages. The first step was to collect information about the construction age: the historical period could be a useful data because buildings belonging to different period will have different thermal and energy properties (Figure 45).

309

337a 337b Group A: 1910-1950 337c Group B: 1951-1960 Group C: 1961-1980 Group D: 1981-2000 Group E: 2000-2012

Figure 45: categories assignment to all the blocks of the Campus.

87 Using a precious Master Thesis’s word by Aleksandra Sretenovic [39], construction ages have been assigned to each building of the Campus. It has been decide to create five different archetypes of building according to five different construction period ranges; these last have been defined considering the different design techniques that were used in the buildings (Table 2).

301a Hovedbygningen 302a Varmeteknisk nord 319 Gamle Fysikk 302b Varmeteknisk nord 323 Gamle Kjemi GROUP A 303 Stromningstek 324 Vannkraft Lab 307 Verksted Teknisk 325 Elektro G 314a Kjemi 4 334 Skiboli 314b Kjemi 4 301b Hovedbygningen 317 IT Bygget 301c Hovedbygningen 318a IT Bygget 301d Hovedbygningen 318b IT Bygget 301e Hovedbygningen 321a Sentralbygg 1 304a Metallurgi 321b Sentralbygg 1 304b Metallurgi 321c Sentralbygg 1 305a Opredningen 321d Sentralbygg 1 305b Opredningen GROUP C 321e Sentralbygg 1 305c Opredningen 321f Sentralbygg 1 306 Geologi 322a Sentralbygg 2 308 Material Teknisk 322b Sentralbygg 2 309 Driftssentralen 322c Sentralbygg 2 310 Kjemi sydfløy 322d Sentralbygg 2 311a Kjemi 1 330a Elektro E GROUP B 311b Kjemi 1 330b Elektro E 311c Kjemi 1 330c Elektro E 312 Kjemi 2 331a Elektro F 315a Kjemi 5 331b Elektro F 315b Kjemi 5 331c Sintef-Energi 316 Kjemi 6 337a Byggteknisk 326a Elektro A 337b Byggteknisk 326b Elektro A 327 Elektro B 328 Elektro C 329a Elektro D 329b Elektro D 332 Gronnbygget 354 Kjelhuset 39. source: Master’s Thesis “Analysis of energy use at university campus”, by A. Sretenovic; supervisor: Associate Professor Natasa Nord.

88 313a Kjemi 3 313b Kjemi 3 333a Berg AVD 333b Berg AVD GROUP D 333c Berg AVD 337c Byggteknisk 356 Produktdesign 357 VM-paviljongen 358 Hogkoleringen

313a Kjemi 3 360a Realfabygget 313b Kjemi 3 360b Realfabygget 333a Berg AVD GROUP E 360c Realfabygget 333b Berg AVD 360d Realfabygget GROUP D 333c Berg AVD 360e Realfabygget 337c Byggteknisk 356 Produktdesign 357 VM-paviljongen Table 2: summary tables of the five different categories of buildings which have been created 358 Hogkoleringen for the developing of the work.

360a Realfabygget To each group will correspond a different template that it means different materi- 360b Realfabygget Percentageals, envelope techniques, number layers and consequently of distinct energy usage consump- GROUP E 360c Realfabygget tions; these simplifications will be useful in the following phases. 360d Realfabygget buildings360e perRealfabygget group Percentage number of c buildings per group

7% 8%

13%

37%

35%

Group A: 1910-1950 Group B: 1951-1960 GroupGroup C: 1961-1980 A Group B Group D: 1981-2000 GroupGroup E: 2000-2012 C Group D

89 INPUT DATA COLLECTION

context input data In ordermodel to running the simulation, the softwaresolution UMI needs a big 1 2 3 4 simulation 5 output data 6 analisys collection numbercreation of different input data. proposal mapping climate data To3D make model the collectingenergy processoutput more data clearcritical and easier to understand,creation the inputconsumptions informationexporting have beenand dividedbuildings in several catego- blocking geometry data reading selection ries that can be read in the scheme on the left. reference template embodied output data numbering This phase is the one that takes longer becausesolution all the collected buildings creation energy and validation approach grouping data had also tocarbon be ordered and put all together in excel files. Also, scenario A buildings iftemplate the input data are not precise enough, the model and conse- properties quentlyassignment the simulation could transmit wrongscenario results B that could be scenario A+B energy fare away from the real scenario. consumption

LCA data

CLIMATE DATA The software UMI requires an Energy Plus Weather File format (.epw); since this kind of extension doesn’t exist regarding specifically the Gløshaugen Campus, it has been used an “epw” file that contains the climate information recorded by a weather station placed near the Trondheim Værnes Airport. This last one is located less than 30 km far from the Campus and it is 30 metres above the sea level lower; since the macroclimate data doesn’t affect significantly the energy simulation with the tool UMI, the approximation, and its consequent error, have been considered acceptable for the developing of the work.

GEOMETRY DATA The geometry data are necessary to build the 3D model of the campus; in particular it will be necessary know information about the plans and the height of all the buildings. In order to get them, two different sources have been used: Open Street Map and Trondheim Municipality. The first was used to obtain information regarding the ground plan of each building of the Campus; an image was extract from the site and scaled correctly. The Municipality of Trondheim provides several documentation, including a 3D of the all city; since this file has been created with 3D Studio Max, it is really heavy and difficult to use. For these reason it was only possible to obtain from it information regarding the height of the buildings, but it was impractical use it for other aims. Putting together the data collected in the two different ways, the geometry will be easily input in the tool.

90 DEFINITION OF REFERENCE BUILDINGS From the five different groups based on the construction age, was chosen one building that could represent al the other buildings belonging to that group; these will be called “reference buildings” (RBs). Considering the information and the data available, the methodology that has been chosen is the one based on real buildings; for each category, buildings that have been chosen are showed in the next pages (Figure 47).

Hovedbygget Berg AVD group A (n. 301) group D (n. 333)

Realfagbygg Elektro A group E group B (n. 226) (n.360)

Sentralbygg 1 group C (n. 321)

91 Hovedbygget Berg AVD group A (n. 301) group D (n. 333)

Realfagbygg Elektro A group E group B (n. 226) (n.360)

Sentralbygg 1 group C (n. 321)

Hovedbygget Berg AVD group A (n. 301) group D (n. 333)

Realfagbygg Elektro A group E group B (n. 226) (n.360)

92 Sentralbygg 1 group C (n. 321) BUILDINGS PROPERTIES This phase is the one that it had took more time since lot of information have been collected during it. Concerning the construction, for each reference building it has been necessary collect information about the material that are present and how they were assembly in order to create layers. It has been decided to analyse six different main components for each reference building: walls (external), interior floor, ground floor, interior wall, roof and windows. The properties for each component are valid for all the same component belonging to the same category, as showed in Figure 48 in the following page.

reference building

facades interior floor interior wall ground floor roof windows

A_group_façades A_group_int floor A_group_int wall A_group_ground f A_group_roof A_group_windows B_group_façades B_group_int floor B_group_int wall B_group_ground f B_group_roof B_group_windows C_group_façades C_group_int floor C_group_int wall C_group_ground f C_group_roof C_group_windows D_group_façades D_group_int floor D_group_int wall D_group_ground f D_group_roof D_group_windows E_group_façades E_group_rint floor E_group_int wall E_group_ground f E_group_roof E_group_windows Figure 48: each component for each reference building (i.e. for each category) have been named. Each component is preceded by the letter of the correspondent category in order to create ordered lists which can be easily input and manage in the template editor of the tool UMI.

Information regarding the five reference building were collect from the FDV (For- valtning, Drift og Vedlikehold) Office that deals with the operation management and maintenance of the building in the Gløshaugen Campus. For each chosen building, many data were collected and analysed in order to ex- trapolate information regarding the five components listed before. The difficulties had concerned the quantity of files that had to be analyze (approximately one hundred for each reference-building), the file format (most of them were a “pdf” of the original drawings) and the language (all the notes and captions on the drawings were written in Norwegian and had to be translated). From the files, it was possible to get information about the materials that make up the layers of the different components and their thickness. The properties for each component of each category are listed in the Table 3 in the next page:

93 Material Thickness [mm] Thickness [m] bricks 300 0.3 air cavity 50 0.05 façade bricks 300 0.3 stone 120 0.12

medium concrete 200 0.2 floor (int) metal frame 200 0.2 light concrete 120 0.12

partition bricks 300 0.3 GROUP A stone 1 400 0.4 ground floor bricks 300 0.3

wood beam 250 0.25 roof wood joist 150 0.15 wood planking 50 0.05

glaze 5 0.005 windows air 1 0.001 glaze 5 0.005

insulation 80 0.08 façade concrete dense 150 0.15 concrete lightblocks 150 0.15

concrete dense 250 0.25 floor (int) insulation 100 0.1 screed 80 0.08

partition concrete medium 150 0.15

concrete dense 300 0.3 GROUP B ground floor insulation 80 0.08 screed 80 0.08

concrete dense 250 0.25 screed 200 0.2 roof reinforced screed 60 0.06 plaster 60 0.06

glaze 6 0.006 windows air 20 0.02 glaze 6 0.006

94 Material Thickness [mm] Thickness [m] insulation (int) 150 0.15 façade concrete dense 250 0.25

concrete medium 250 0.25 floor (int) screed 80 0.08 insulation 120 0.12

partition concrete medium 150 0.2

stone 1 200 0.2 concrete medium 200 0.2 ground floor insulation 100 0.1 GROUP C floor 50 0.05

concrete dense 150 0.15 insulation 100 0.1 roof air ventilation 200 0.2 exterior floor 50 0.05

glaze 5 0.005 air 20 0.02 windows glaze 5 0.005 gas 14 0.014 glaze 4 0.004

insulation 1 (int) 100 0.1 façade concrete dense 300 0.3

concrete medium 250 0.25 floor (int) insulation 1 120 0.12 screed 80 0.08

insulation1 100 0.1 partition hollow bricks 150 0.15

concrete foundation 400 0.4 air space 200 0.2 GROUP D ground floor insulation 80 0.08 screed 80 0.08

concrete medium 250 0.25 insulation 150 0.15 roof air ventilation 100 0.1 exterior floor 100 0.1

glaze 5 0.005 windows gas 18 0.018 glaze 5 0.005 95 Material Thickness [mm] Thickness [m] clay blocks 200 0.2 façade insulation 2 100 0.1 external material 50 0.05

lightweight concrete 250 0.25 screed floor 80 0.08 floor (int) insulation 2 150 0.15 floor 50 0.05

insulation 2 150 0.15 partition hollow bricks 160 0.16

GROUP E concrete foundation 400 0.4 air ventilation 250 0.25 ground floor insulation 2 150 0.15 screed floor 80 0.08

concrete medium 250 0.25 roof insulation 2 150 0.15 air ventilation 200 0.2

glaze 4 0.004 windows gas 20 0.02 glaze 4 0.004

Table 3: the tables show the different materials per each component of each category. Properties have been extrapolated from official document of the University.

Since for the energy modelling also thermal properties of the materials are necessary, these data were assigned using two different web data-base: Engineering ToolBox and Thermtest. Materials’ thermal properties that have been collect are: conductivity λ [W/mK], thermal resistamce R [m2K/W], density [kg/m3], solar absorptance [-], specific heat [J/kgK], thermal emittance [-], visible absorptance [-]. Having the thickness L [m] and the conductivity λ [W/mK], it was possible to calculate the thermal resistance R [m2K/W] of each material using the formula: R = L/λ[m2K/W] Thermal resistance will be used in the following steps to calculate the transmittance value U [W/mK]. The values for each material are listed in the following Tables 4, 5, 6:

96 Material Conductivity [W/mK] Thermal resistance [m2K/W] air 0.025 - air space 0.08 - air ventilation 0.08 - argon 0.016 - bricks 1.6 0.188 cavity (air) 0.06 0.228 clay blocks 0.12 1.667 concrete dense 1 0.15 concrete foundation 0.8 0.5 concrete lightweight 0.2 0.75 concrete medium 0.8 0.25 external material 0.9 0.056 floor 0.08 0.625 glaze 1.2 - glaze1 0.9 - hollow bricks 0.22 0.727 insulation 0.08 1.25 insulation 1 0.06 1.667 insulation 2 0.035 4.286 metal frame 5 2.5 plaster 0.6 0.1 reinforced plaster 0.06 0.1 screed 0.1 0.8 screed floor 0.08 - stone 2 0.06 stone 1 1.5 0.267 wood beam 0.15 1 wood joist 0.2 0.45 wood planking 0.3 0.167

Table 4: values of conductivity [W/mK] and thermal resistance [m2K/W] of each material.

97 Material Density [kg/m3] Solar abs. [-] Specific heat [J/kgK] air 1.2 - 1000 air space 1.3 - 1000 air ventilation 1.3 - 1000 argon 1700 - 520 bricks 1700 0.8 1500 cavity (air) 1.3 0 1000 clay blocks 860 0.8 1500 concrete dense 2300 0.6 657 concrete foundation 2300 0.6 670 concrete lightweight 950 0.6 460 concrete medium 2000 0.6 657 external material 1000 0.6 400 floor 1000 0.6 400 glaze 2200 0.8 900 glaze1 2200 0.6 750 hollow bricks 650 0.8 750 insulation 160 0.4 2100 insulation 1 100 0.4 2100 insulation 2 50 0.4 2100 metal frame 7000 0.6 630 plaster 1250 0.6 1088 reinforced plaster 950 0.6 750 screed 950 0.6 680 screed floor 950 0.6 680 stone 2300 0.8 837 stone 1 2000 0.8 800 wood beam 550 0.6 2300 wood joist 550 0.6 2300 wood planking 550 0.6 1200

Table 5: values of density [kg/m3], solar absorptance [-] and specific heat [J/kgK] of each material.

98 Material Thermal emittance [-] Visible abs. [-] air 0 0 air space 0 0 air ventilation 0 0 argon 0 0 bricks 0.93 0.6 cavity (air) 0 0 clay blocks 0.91 0.6 concrete dense 0.85 0.6 concrete foundation 0.85 0.6 concrete lightweight 0.85 0.6 concrete medium 0.85 0.6 external material 0.7 0.4 floor 0.7 0.4 glaze 0.1 0.1 glaze1 0.1 0.1 hollow bricks 0.8 0.6 insulation 0.9 0.4 insulation 1 0.9 0.4 insulation 2 0.9 0.4 metal frame 0.31 0.6 plaster 0.91 0.4 reinforced plaster 0.8 0.6 screed 0.8 0.6 screed floor 0.85 0.6 stone 0.96 0.8 stone 1 0.96 0.8 wood beam 0.9 0.8 wood joist 0.9 0.8 wood planking 0.9 0.8

Table 6: values of thermal emittance [-] and visible absorptance [-] of each material.

99 The thermal properties that have been assigned are based on average values, so it was necessary to validate them; in order to do that, the transmittance U-value [W/ m2K] was calculated for each component of each group and then compared to the one that was present in the Excel file made by Aleksandra Sretenovic for her Master Thesis’s word. The thermal resistance R found and tabled previously, was used to calculate the transmittance U-value for each component (wall, interior floor, ground floor, roof, windows) using the formula:

U = 1 [W/m2K] Rsi + R1 + R2 + .. + Rn + Rse

Where Rsi and Rse are respectively the internal and external heat transfer resistance that are values that can be found in the European Regulation ISO 6946:

Direction of the heat flow Surface resistance [m2K/W] Upwards Horizontal Downwards Rsi 0.1 0.13 0.17 Rse 0.04 0.04 0.04

Figure 49: values of surface resistance given by the European Regulation ISO 6946. This values have to be used for plane surfaces in the absence of specific information on the boundary conditions. The values under “horizontal” apply to heat flow directions ± 30 ° from the horizontal plane [24].

Results of the calculated transmittance U-values are showed on the next Table 7, compared to the one that was already collected during the previous study by Alek- sandra Sretenovic (called “Given U-values”). Not for each component was possible to carry out this validation since Excel data used for the Master’s Thesis by Aleksandra Sretenovic were available only regarding the façades, the interior floors and roofs. But, since this validation has been done in order to check the average thermal values taken from the web-data source, it was not strongly necessary to do the validation for each components of each category. In this case, the validation is considered ell-founded if the error between the calculated U-value and the given U-value does not exceed 15% [40]; results of calculated errors are showed in Table 8.

40. Source: “Fondamenti sugli scambi termici attraverso gli elementi dell’involucro edilizio”, by S. Bergero and A. Chiari.

100 2 Component Calculated U-value [W/m2K] Given U-value [W/m K] façade 0.99 0.93 GROUP A floor (int) 0.27 0.24 roof 0.61 0.55

2 Component Calculated U-value [W/m2K] Given U-value [W/m K] façade 0.52 0.49 GROUP B floor (int) 0.42 0.38 roof 0.41 0.46

2 Component Calculated U-value [W/m2K] Given U-value [W/m K] façade 0.47 0.43 GROUP C floor (int) 0.39 0.44 roof 0.27 0.3

2 Component Calculated U-value [W/m2K] Given U-value [W/m K] façade 0.48 0.44 GROUP D floor (int) 0.32 0.35 roof 0.3 0.31

2 Component Calculated U-value [W/m2K] Given U-value [W/m K] façade 0.21 0.21 GROUP E floor (int) 0.08 0.07 roof 0.14 0.15 Table 7: values of calculated U-values compared to the given U-values for each component.

error in % façade error in % floor (int) error in % roof GROUP A 6.4 12.1 10.9

GROUP B 6.1 10.5 10.8

GROUP C 9.3 11.3 10

GROUP D 9 8.5 3.2

GROUP E 0 14.2 6.6

Table 8: errors in percentage between calculated U-values and given U-values; the error never exceeds the 15%. 101 ENERGY CONSUMPTIONS Information regarding the energy usage were collected using the Excel file of Alek- sandra Sretenovic’s Master’s Thesis. When data were missing, average values were used. All the information were then collected in the following Table 9:

GROUP A GROUP B GROUP C GROUP D GROUP E Set-point temperature for heating 21.00 20.00 21.00 21.00 21.00 operation time [°C] Set-point temperature 22.00 22.00 22.00 22.00 22.00 for cooling [°C] Max heating 300.00 176.00 153.00 102.00 85.00 capacity [W/m2] Max cooling 40.00 42.00 40.00 40.00 40.00 capacity [W/m2] COP (heating) 0.75 0.75 0.80 0.85 0.90

COP (cooling) 2.50 2.50 2.70 2.90 3.20 Ventilation 12.00 12.00 12.00 12.00 12.00 operation time [h] Heating operation 12.00 12.00 12.00 12.00 12.00 time [h] Cooling operation 12.00 12.00 12.00 12.00 12.00 time [h] Lighting operation 12.00 12.00 12.00 12.00 12.00 time [h] Equipment 12.00 12.00 12.00 12.00 12.00 operation time [h] People occupancy 12.00 12.00 12.00 12.00 12.00 period [h]

Table 9: information collected about the energy usage in the Campus are listed.

102 LCA DATA Information regarding the Life Cycle Assessment of the material were useful in order to run the CO2 emissions simulation with the software UMI. Values about the embodied energy and embodied carbon have been collected using average material data using the ICE (Inventory of Carbon and Energy) data-base and are showed in Table 10:

Embodied energy Embodied carbon Material [MJ/kg] [kgCO2/kg]

air - - air space - - air ventilation - - argon bricks 3 0.23 cavity (air) - - clay blocks 0.85 0.2 concrete dense 0.78 0.1 concrete foundation 0.78 0.1 concrete lightweight 0.7 0.09 concrete medium 0.74 0.1 external material 2 1 floor 2 1 glaze 15 0.86 glaze1 28 1.54 hollow bricks 6 0.45 insulation 40 2.61 insulation 1 16 1.2 insulation 2 16 1.2 metal frame 25 1.91 plaster 1.8 0.12 reinforced plaster 0.78 1 screed 0.7 0.09 screed floor 0.7 0.09 stone 1.26 0.08 stone 1 1.26 0.08 wood beam 10 0.7 wood joist 10 0.7 wood planking 10 0.7

Table 10: information collected about the embodied properties of the materials are listed.

103 MODEL CREATION context input data model solution 1 2 3 4Thesimulation modelling5 phaseoutput data is made6 of three different steps: the first one analisys collection creation regard the proper 3D model designproposal using the geometry data col- mapping climate data 3D model lectedenergy previously output in dataChapter “Energycritical simulation with the software creation consumptionsUMI - input dataexporting collection and - geometrybuildings data”, then it is necessary to blocking geometry data reading selection create the five templates and finally assign them to the buildings numbering reference template embodied output data solution buildings creation accordingenergy and to theirvalidation building group.approach grouping Accuratecarbon description of the three steps are given in the following scenario A buildings template sections. properties assignment scenario B scenario A+B energy consumption 3D MODEL CREATION LCA data Using geometry data collected previously, the 3D model of the campus were built; first of all it was necessary to import the plan of Gløshaugen Campus and then the blocks that have been created in Chapter “Energy simulation with the software UMI - context analysis - blocking”: were model using a simple boxes. The blocks were then added to the layer “Building”, present by default in the software UMI. Regarding the terrain, it was created as a surface and it was considered plane since the area of the Campus it is almost flat in all its parts. The terrain surface was added to the default layer “boundary”. In this phase, also the windows were added: since UMI recognizes the apertures as a percentage on each surface; thus it was necessary to collect these information which have been then inserted into the Table 11:

Building number % north-west % south-east % north-east % south-west 301a 40 30 50 50 301b 30 50 0 10 301c 0 40 20 20 301d 30 50 10 0 301e 0 50 0 0 302a 50 30 50 10 302b 0 0 0 60 303 40 10 30 0 304a 0 10 60 50 304b 20 0 0 0 305a 0 0 20 60 305b 0 0 30 0 305c 10 0 30 50 306 30 40 30 30 307a 30 50 30 40 307b 0 40 30 30 308 30 10 30 30 309 30 10430 20 20 310 40 10 0 0 311a 20 0 40 40 311b 0 10 0 10 311c 0 0 0 40 312 20 10 40 40 313a 20 10 40 40 313b 40 10 0 0 314a 20 10 40 40 314b 40 10 0 0 315a 20 10 40 40 315b 10 10 0 0 316 30 10 40 40 317 10 10 40 40 318a 0 20 40 40 318b 0 0 50 30 319 20 40 40 40 321a 20 40 40 40 321b 0 0 40 50 321c 0 0 40 50 321d 50 40 0 0 321e 0 0 20 20 321f 20 50 40 40 322a 20 40 40 40 322b 0 0 20 20 322c 0 0 40 50 322d 50 40 0 0 323 30 40 30 10 324 0 20 40 40 325 30 20 50 0 326a 30 0 0 40 326b 70 0 0 0 327 40 40 0 0 Building number % north-west % south-east % north-east % south-west 308 30 10 30 30 309 30 30 20 20 310 40 10 0 0 311a 20 0 40 40 311b 0 10 0 10 311c 0 0 0 40 312 20 10 40 40 313a 20 10 40 40 313b 40 10 0 0 314a 20 10 40 40 314b 40 10 0 0 315a 20 10 40 40 315b 10 10 0 0 316 30 10 40 40 317 10 10 40 40 318a 0 20 40 40 318b 0 0 50 30 319 20 40 40 40 321a 20 40 40 40 321b 0 0 40 50 321c 0 0 40 50 321d 50 40 0 0 321e 0 0 20 20 321f 20 50 40 40 322a 20 40 40 40 322b 0 0 20 20 322c 0 0 40 50 322d 50 40 0 0 323 30 40 30 10 324 0 20 40 40 325 30 20 50 0 326a 30 0 0 40 326b 70 0 0 0 327 40 40 0 0 328 20 0 40 40 329a 40 40 40 40 329b 20 0 0 0 330a 0 0 90 90 330b 0 0 10 10 330c 0 0 90 90 331a 0 0 10 10 331b 0 1050 90 90 331c 40 40 20 40 332 40 30 20 20 333a 0 0 20 60 333b 40 40 40 0 333c 10 0 40 50 334 10 10 90 50 337a 40 40 40 50 337b 30 10 30 10 337c 10 0 0 0 354 0 40 30 20 356 40 30 40 0 357 30 30 10 10 358 0 30 20 30 360a 40 30 0 30 360b 40 0 0 0 360c 40 10 20 0 360d 0 30 0 30 360e 0 30 40 30 365 30 20 20 40 Building number % north-west % south-east % north-east % south-west 331a 0 0 10 10 331b 0 0 90 90 331c 40 40 20 40 332 40 30 20 20 333a 0 0 20 60 333b 40 40 40 0 333c 10 0 40 50 334 10 10 90 50 337a 40 40 40 50 337b 30 10 30 10 337c 10 0 0 0 354 0 40 30 20 356 40 30 40 0 357 30 30 10 10 358 0 30 20 30 360a 40 30 0 30 360b 40 0 0 0 360c 40 10 20 0 360d 0 30 0 30 360e 0 30 40 30 365 30 20 20 40

Table 11: windows percentage per each surface of the buildings.

Hence, the building’s window-to-wall ratio (WWR) represents the percentage of the building’s façade area that is glazed. UMI allows the specification of a different WWR value for each façade orientation. If the building is not perfectly compass-aligned, UMI will use the closest compass direction for each façade surface In this phase is also possible to add objects on the “shading layer”: things on these layers are used for operational energy simulations and they are used to calculate shadows cast on simulated buildings.

Once that the 3D geometry is complete and the openings are assigned, the solids have to be added to the layers “buildings”, or the software will not recognize them during the simulation process.

The workflow and the result of the modelling phase is showed in Figure 50 in the following page:

106 Figure 50: the first image shows the plans of the Campus that has been created using OpenStreet Map; then, highs were added in order to create solids using Trondheim Municipality 3D model as a source. At last, windows were added as a percentage for each surface.

107 TEMPLATE CREATION The template creator of the software UMI is made of several sections in which information collected in Chapter “Energy simulation with the software UMI - input data collection”: have to be added. The sections have to be filled in order since the template editor thinks as the Chinese boxes. A scheme with the workflow of the template editor in UMI is showed below in Figure 51:

Figure 51: UMI’s template editor workflow is materials showed; each section is useful to create the build- weather ing template and has to be completed carefully data in all its part. A incorrect template creation could + thickness bring to simulation problems.

constructions schedules

zone information

building template

First of wall it was necessary add to the template all the material and their properties collected in “Chapter 3 - Input data collection - building properties”; they are divided in three different categories: opaque, glazing and gases. Next, the materials are combined together in order to create the different component for each category showed previously in Chapter 3 - input data collection - building properties.

108 The next section regards the schedules, i.e. the part which contains all the information about the energy consumptions. The first data that have to be added are the one regarding the daily consumption: these information have to put in the software UMI as a percentage of a certain max- imum value, this last it will be defined in the following section. Percentage value is on the “y asses”. For the daily consumption on the “x asses” there are the hours of the day from 1 to 24. Generally, for all the type of consumption it was necessary to differentiate weekdays (WD) from weekend days (WE), since the values could have been widely different from each other. For lighting, equipment, domestic hot water (DHW), ventilation, shading and occupancy, same schedules have been assumed for all the building categories; contrariwise, for heating and cooling it was necessary to create different schedules for each category, since each reference building had diverse heating/cooling properties.

Graphs of daily schedules that have been added are showed in the following pages:

EACH GROUP - daily consumptions for ventilation

weekdays - all year weekends - all year

1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 1 24 1 24

EACH GROUP - daily usage of shading

every day - other months every day - summer

1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 1 24 1 24

109 EACH GROUP - daily consumptions for domestic hot water

weekdays - all year weekends - all year

1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 1 24 1 24

EACH GROUP - daily consumptions for lighting

every day - other months every day - summer

1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 1 24 1 24

EACH GROUP - daily occupancy level

weekdays - all year weekends - all year

1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 1 24 1 24

EACH GROUP - daily occupancy

weekdays - all year weekends - all year

1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 1 24 1 24

110 A GROUP - daily consumptions for heating

weekdays - other months weekends - other months

1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 1 24 1 24