Master thesis project - Mikel Urroz

Author | Mikel Urroz Oyarzabal

MASTER THESIS

PROJECT

METHOD TO ANALYZE THE IMPACT OF THE OFFICE BUILDING SHAPE ON THE ENERGY CONSUMPTION AND INDOOR CLIMATE

TECHNICAL UNIVERSITY OF DENMARK OCTOBER 2011

Technical University of Denmark

Mikel Urroz Oyarzabal Page 1 of 111

Method to analyse the impact of the office building shape on the energy consumption and indoor climate

Master thesis project Sustainable Energy – Energy Savings

Author:

Mikel Urroz Oyarzabal Student number: s094129 _______________________ Email: [email protected]

Supervisors:

Svend Svendsen Professor DTU Department of Civil Engineering Tel: (+45) 45251854 Email: [email protected]

Michael Jørgensen PhD student DTU Department of Civil Engineering Tel: (+45) 45251934 Email: [email protected]

Technical University of Denmark Department of Civil Engineering

Building 118 2800 Kgs. Lyngby

Tel: (+45)45883282 Email: [email protected]

www.byg.dtu.dk

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Preface This report is the result of the project carried out during the 4th semester of the Master of Science Engineering in Sustainable Energy - Energy Savings at Technical University of Denmark. The work load corresponds to 30 ECTS credits accomplished from March to October 2011.

This project was carried out under supervision of Professor Svend Svendsen and PhD student Michael Jørgensen.

Software programs used for the project are IES<VE> version 6.1.1, Google Sketch-Up and Microsoft Office package.

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Abstract The use of daylight as a light source in office buildings influences both, the energy performance and the indoor environment. This study shows that if the building shape enhances daylight access by increasing window area, the energy savings in artificial lighting overcome the increased heating and cooling energy demand. An optimal office building shape with an appropriate orientation (for the optimization of passive solar gain in heating season and prevention of overheating in cooling season) can result in a total energy consumption of 49.9 kWh/m2. Such an office building can be designed with minimum requirements of BR10 regarding insulation levels and building services.

Keywords: building shape, relative compactness, glazing area, daylight

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Table of contents Preface .......................................................................................................................................... 3

Abstract ......................................................................................................................................... 5

Table of contents ........................................................................................................................... 7

Table of figures .............................................................................................................................. 9

Table of tables ............................................................................................................................. 11

1. Introduction ........................................................................................................................ 13

2. Aim of the study .................................................................................................................. 15

3. Related work – Literature study .......................................................................................... 16

4. Description of standards ..................................................................................................... 20

5. Method ................................................................................................................................ 24

5.1. Setting up the models ................................................................................................. 24

5.1.1. Building shape catalogue .................................................................................... 27

5.2. Building energy simulation program: IES<VE> ............................................................ 35

5.3. Implementation of models in IES<VE> ........................................................................ 36

5.3.1. Overview ............................................................................................................. 36 5.3.2. Model IT .............................................................................................................. 36 5.3.3. Apache ................................................................................................................. 36 5.3.4. SunCast ................................................................................................................ 40 5.3.5. Radiance .............................................................................................................. 41 5.3.6. MacroFlo ............................................................................................................. 41

5.4. Investigations prior to simulations .............................................................................. 42

5.4.1. Simplified method to find an appropriate room division ................................... 42 5.4.2. Consequences of removing partition walls ......................................................... 46

5.5. Investigation process ................................................................................................... 49

6. Results ................................................................................................................................. 50

6.1. Validation of energy performance and indoor climate ............................................... 50

6.2. Relative compactness .................................................................................................. 64

6.3. Glazing area ................................................................................................................. 65

6.4. Impact of building relative compactness - A sharper analyses ................................... 67

6.4.1. Impact of building relative compactness on heating energy demand ................ 68 6.4.2. Impact of building relative compactness on cooling energy demand ................ 70 6.4.3. Impact of relative compactness in low energy buildings .................................... 72

6.5. Building shape and orientation ................................................................................... 75

6.6. Low energy buildings ................................................................................................... 78

7. Discussion ............................................................................................................................ 83

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8. Conclusion ........................................................................................................................... 88

9. Future work ......................................................................................................................... 90

10. References ........................................................................................................................... 91

Appendix 1: Capabilities of Building Energy Simulation Programs ............................................. 93

Appendix 2: Examples of existing office buildings in Copenhagen ............................................. 97

Appendix 3: Composition and thermal properties of building components .............................. 99

Appendix 4: Reference lighting systems ................................................................................... 101

Appendix 5: Simplified method for calculation of daylight penetration .................................. 104

Appendix 6: Extra rectangular buildings for analyses of relative compactness ........................ 106

Appendix 7: Assumptions for standard and low energy buildings............................................ 107

Appendix 8: Energy performance of standard buildings........................................................... 108

Appendix 9: Energy performance of low energy buildings ....................................................... 110



Table of figures Figure 1: CO2 emissions are responsible for 63% of man-made global warming (1) .................. 13 Figure 2: Example of buildings with different relative compactness .......................................... 17 Figure 3: Simulated heating loads as a function of RC of building shapes displayed in Figure 2 (9) ................................................................................................................................................ 17 Figure 4: Relative cooling load as a function of window area for various window-to-wall ratios using a single clear glazing. *The energy use is relative because it is compared to a reference building (8) .................................................................................................................................. 18 Figure 5: Relative annual total building energy use as a function of window area for various glazing types in Tunis climate (8) ................................................................................................ 18 Figure 6: Example of office buildings located in Copenhagen .................................................... 25 Figure 7: Occupancy profile during weekdays ............................................................................ 37 Figure 8: Heating set point temperatures during weekdays....................................................... 38 Figure 9: Cooling set point temperatures ................................................................................... 39 Figure 10: Reference room division with average room illuminance levels (lux) during the year ..................................................................................................................................................... 43 Figure 11: Room division alternatives and the respective amount of rooms ............................. 43 Figure 12: Relative error on artificial lighting energy demand of room division alternatives 1 to 6 ................................................................................................................................................... 44 Figure 13: Room division alternatives for the self-shading buildings ......................................... 45 Figure 14: Relative error on artificial lighting demand of alternatives 1 to 4 ............................. 45 Figure 15: Example of L-building shape with concluding room division methodology .............. 46 Figure 16: Simple model division in two spaces for testing heat and mass transfer in the interior when using partition walls ............................................................................................. 47 Figure 17: Temperatures of the rooms for 29th of June when the model is divided in two rooms by a partition wall........................................................................................................................ 47 Figure 18: Temperatures of the rooms for 29th of June when the partition wall is replaced by a hole with equal surface ............................................................................................................... 48 Figure 19: Graphic representation of the investigation progress ............................................... 49 Figure 20: Air temperatures in building R1 (rectangular shape) from 14th to 20th of July (summer season) ......................................................................................................................... 51 Figure 21: Air temperatures in building R1 (rectangular) from 6th to 12th of January (winter season) ........................................................................................................................................ 52 Figure 22: Air temperatures in building R12 (rectangular shape) with orientation at 90° from 14th to 20th of July (summer season) ........................................................................................... 52 Figure 23: Air temperatures in building R12 (rectangular shape) with orientation at 90° from 6th to 12th of January (winter season)............................................................................................... 53 Figure 24: CO2 concentration in a room of rectangular building R1 throughout the year ......... 53 Figure 25: Daylight penetration versus glazing area for all buildings ......................................... 54 Figure 26: Example of buildings with reduced daylight access in low floors .............................. 55 Figure 27: Heating, cooling, ventilation, lighting and total energy consumption of buildings versus relative compactness ....................................................................................................... 64

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Figure 28: Heating, cooling, ventilation, lighting and total energy consumption of buildings versus glazing area. Results for different orientation of the buildings were included in the graphs. ......................................................................................................................................... 66 Figure 29: Lighting energy demand and total energy demand versus glazing area when buildings with limited daylight access are excluded ................................................................... 66 Figure 30: Building dimensions of R4 (left) and R17 (right) ........................................................ 68 Figure 31: R4 (left) and R17 (right) with equal glazing area ........................................................ 68 Figure 32: Heating energy demand of ‘standard buildings’ with no glazing ............................... 69 Figure 33: Heating energy demand of ‘standard buildings’ with equal glazing area .................. 69 Figure 34: Heating energy demand for (rectangular) standard buildings with no glazing ......... 70 Figure 35: Cooling energy demand of ‘standard buildings’ with no glazing ............................... 71 Figure 36: Cooling energy demand of ‘standard buildings’ with equal glazing area .................. 71 Figure 37: Heating energy demand of ‘low energy buildings’ with no glazing ........................... 73 Figure 38: Heating energy demand of ‘low energy buildings’ with equal glazing area .............. 73 Figure 39: Cooling energy demand of ‘low energy buildings’ with no glazing............................ 74 Figure 40: Cooling energy demand of ‘low energy buildings’ with equal glazing area ............... 74 Figure 41: Sun-path in summer and winter seasons ................................................................... 75 Figure 42: Solar gain throughout the year of R12 oriented in a North-South axis (0° azimuth). 75 Figure 43: Solar gain throughout the year of R12 when oriented in East-West axis (90° azimuth) ..................................................................................................................................................... 76 Figure 44: Heating energy demand of rectangular buildings depending on the orientation ..... 76 Figure 45: Cooling energy demand for rectangular buildings depending on the orientation .... 77 Figure 46: Lighting energy demand for rectangular buildings depending on the orientation ... 77 Figure 47: Total energy demand of low energy buildings (rectangular shape) with regard of the orientation................................................................................................................................... 78 Figure 48: Contribution of each end use on total energy demand for R12 low energy building oriented east-west ...................................................................................................................... 79 Figure 49: Energy use by end use for standard buildings with East-West orientation ............... 79 Figure 50: Energy use by end use for low energy buildings with orientation East-West ........... 80 Figure 51: Solar gain and cooling load for R12 standard building oriented east-west ............... 81 Figure 52: Solar gain and cooling load for R12 low energy building oriented east-west............ 81 Figure 53: Solar gain and cooling load for R12 low energy building oriented east-west when using external solar shadings ...................................................................................................... 82 Figure 54: Energy demand by end use of R12 under different conditions ................................. 82 Figure 55: Tendency of heating, lighting and total energy demand in ‘standard buildings’ ...... 86 Figure 56: Tendency of heating, lighting and total energy demand in ‘low energy buildings’ ... 87 Figure 57: Building R 15 on the optimal orientation for passive solar gain ................................ 88 Figure 58: Quadratic 9 storey office building is placed in Kalvebod Brygge 3, 1560 Copenhagen ..................................................................................................................................................... 97 Figure 59: 7 storey rectangular office building of about 19,000 m2 of total floor area; placed in Knippelsbrogade 4, 1400 Copenhagen ....................................................................................... 97 Figure 60: 6 storey office building reminding of T-shaped buildings, erected in Kalvebod Brygge 47, 1560 Copenhagen ................................................................................................................. 97 Figure 61: 5 storey rectangular office building shape with patio placed in Strandgade 3, 1402 Copenhagen ................................................................................................................................ 98



Figure 62: 6 storey connected rectangular buildings making up U-shaped building configuration in Nicolai Eigtveds Gade 20, 1402 Copenhagen .......................................................................... 98 Figure 63: Daylight penetration in office buildings. The green hatch represents the floor area with daylight factor above 2% ................................................................................................... 104 Figure 64: Useful daylight area versus lighting energy demand of buildings (excluding buildings that could be affected by self-shading effect) .......................................................................... 105

Table of tables Table 1: Minimum insulation requirements for different building elements (14)...................... 21 Table 2: Recommended design values of the indoor temperature for design of buildings and HVAC systems for office buildings (15) ....................................................................................... 22 Table 3: Recommended ventilation rates for non-residential buildings with default occupant density for low-polluted buildings (15) ....................................................................................... 22 Table 4: Recommended CO2 concentration above outdoor for energy calculations and demand control (15) .................................................................................................................................. 22 Table 5: Recommended design illumination levels for office buildings (15) .............................. 23 Table 6: Examples of length of deviations corresponding to 3 and 5 % of the time (15) ........... 23 Table 7: Proposed building shapes according to the number of cells ........................................ 26 Table 8: Quadratic buildings ....................................................................................................... 27 Table 9: Rectangular building shape alternatives ....................................................................... 28 Table 10: L-shaped building shape alternatives .......................................................................... 29 Table 11: T-shaped building design alternatives ......................................................................... 30 Table 12: U-shaped building alternatives ................................................................................... 31 Table 13: H-shaped building alternatives .................................................................................... 32 Table 14: Quadratic buildings with patio .................................................................................... 33 Table 15: Rectangular buildings with patio ................................................................................. 34 Table 16: U-values of building components ................................................................................ 37 Table 17: Internal gains ............................................................................................................... 40 Table 18: Surface properties used as input for simulation of daylight distribution in Radiance 41 Table 19: Total internal conduction gain of the rooms with both test conditions throughout a year .............................................................................................................................................. 48 Table 20: Percentage of lettable area with daylight factor above 2 % for all buildings ............. 54 Table 21: Energy performance of quadratic buildings ................................................................ 56 Table 22: Energy performance of rectangular buildings ............................................................. 57 Table 23: Energy performance of L-shape buildings ................................................................... 58 Table 24: Energy performance of T-shape buildings ................................................................... 59 Table 25: Energy performance of U-shape buildings .................................................................. 60 Table 26: Energy performance of H-shape of buildings .............................................................. 61 Table 27: Energy performance of quadratic shape of buildings with patio ................................ 62 Table 28: Energy performance of rectangular buildings with patio ........................................... 63 Table 29: Composition and thermal properties of building components of standard buildings 99 Table 30: Composition and thermal properties of building components for low energy buildings ................................................................................................................................................... 100 Table 31: Percentage of total building floor area with daylight factor above 2 % ................... 105

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Table 32: New rectangular buildings with aspect ratio 1/8 and 1/12 representing extreme building alternatives .................................................................................................................. 106 Table 33: Assumptions for energy calculations of buildings with no glazing and equal glazing area............................................................................................................................................ 107 Table 34: Energy performance of 'standard buildings R1-R12 .................................................. 108 Table 35: Energy performance of ‘standard buildings’ R13-R20 .............................................. 109 Table 36: Energy performance of ‘low energy buildings’ R1-R12 ............................................. 110 Table 37: Energy performance of ‘low energy buildings’ R13-R20 ........................................... 111



1. Introduction Natural factors like tiny changes in the Earth’s path around the sun, volcanic activity and fluctuations within the climate system have driven changes in our planet throughout its history. However, human activities such as burning fossil fuels, cutting down rainforests and farming livestock are having an increasing influence on our climate.

Energy from the sun warms up the surface of the Earth radiating some of the heat back out towards space. Some gases in the atmosphere act like the glass in a greenhouse, allowing the sun’s energy in but preventing heat from escaping. Greenhouse gases such as water vapour – the most abundant greenhouse gas – are naturally present in the atmosphere making life as we know it possible. Without this “greenhouse effect” the global average temperature on Earth would be -18°C instead of the 15°C it is today. However, human activities are releasing immense additional amounts of greenhouse gases into the atmosphere, enhancing the greenhouse effect and therefore warming the climate (1).

The combustion of fossil fuels is one of the main sources of carbon dioxide (CO2), which is the most commonly man-made greenhouse gas. Over the last couple of centuries, increasing amounts of coal, oil or gas have been burnt to power machines, generate electricity, heat buildings and transport people and goods. Since the Industrial Revolution, the concentration of CO2 in the atmosphere has increased by around 37% (1).

The 15 countries that were EU members at the time when Kyoto Protocol treatment was signed in 1997 compromised to reduce their collective emissions in the 2008-2012 period to 8% below 1990 levels. Furthermore, in 2007 EU leaders endorsed an integrated approach to climate and energy policy committed to transforming Europe into a highly energy-efficient, low carbon economy by 2020. These climate and energy targets are known as “20-20-20” and consist of a reduction in EU greenhouse gas emissions of at least 20 % below 1990 levels, 20% of EU energy consumption to come from renewable resources and a 20 % reduction in primary energy use compared with projected levels to be achieved by improving energy efficiency. Furthermore, the EU leaders also offered to increase the emission reduction to 30% if other major emitting countries in the developed and developing worlds commit to do their fair share under a global climate agreement. United Nations negotiations on such an agreement are currently on-going (2).

As buildings are responsible of 40 % of the energy consumed in Europe, they are the core of the European Union’s success in achieving the “20-20-20” targets. An enormous unrealised energy-saving potential lies dormant in buildings. The EU’s Energy Performance of Buildings Directive (EPBD), introduced in 2002 and recast in 2010, is the main legislative instrument for improving the energy performance of building sector. The EPBD requires all EU countries to enhance their building regulations, to introduce energy certification schemes for buildings and to have inspections of boilers and air-conditioners (2) (3).

Figure 1: CO2 emissions are responsible for 63% of man-made global warming (1)

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Within the building sector, office buildings are, together with retail, those with the largest energy consumption and CO2 emissions. Among building services, HVAC systems comprising heating, ventilation and air conditioning, are the largest energy end use in office buildings (about 50 % of the total energy consumption) followed by lighting (from 15 % to 35 %) (4).

Important decisions concerning the building design are taken by architects in the conceptual design stage, where opportunities for integration of sustainable design strategies are the greatest. Decisions made at this stage have great impact on building performance. Building shape, orientation and envelope configuration are some of the factors that can make a difference on the building energy consumption and indoor climate. Often, architects make decisions on building shape based on aesthetics only, leaving energy efficiency and costs out of the picture. In order to optimize the building design all three factors should be integrated driven by a close collaboration between architects and engineers from the early design stage. As the energy use for heating, cooling, ventilation and lighting is influenced by the building shape, a high-quality designed building can greatly reduce the energy consumption and consequently the CO2 emissions (5) (6).

There are numerous advantages of using daylight for illumination in buildings. In addition to the benefits of supplying significant light for free, natural lighting provides great physical and psychological benefits to the building occupants leading to an increase of their productivity. If intelligent lighting systems such as daylight sensors are integrated, the natural light can replace the artificial lighting when available, reducing internal loads due to the lower power density and consequently reducing cooling load (7).

The interplay among heating, cooling and ventilation systems, configuration of building envelope, lighting, exterior and interior shadings, etc. can be modelled in detail in contemporary building energy simulation tools that can analyse the model on an hourly basis for an entire year. The modelling is not always accurate. However, the real value of the modelling is to find the relative importance of changes to the building’s envelope and energy systems (6).

Some research has been carried out concerning the impact of the building shape on the energy consumption. In order to minimize the heat loss through the building envelope very compact building shapes were recommended for cold climate conditions, whereas the opposite was recommended for warm climates. However, these recommendations were given only based on heating or cooling loads (depending on the predominant for each climate zone). Other end uses such as ventilation and lighting, as well as the interplay of the different loads were not object of investigation. As internal loads such as people, equipment, lighting, etc. are very high, heating load might not be the prevailing energy demand for office buildings, consequently leading to different conclusions than for residential buildings in cold climates.



2. Aim of the study The main objective of this project is to find out the impact of the office building shape on the energy consumption and indoor climate under Danish weather conditions. A wide range of building geometries, different building heights, aspect ratios and orientations were required to be investigated throughout this study.

Office buildings behave differently than other buildings such as residential. High internal gains due to occupants, electronic equipment and artificial lighting are characteristics of this type of building. This fact changes the contribution from each end energy use (heating, cooling, ventilation, lighting and DHW) relying mostly on ventilation and electrical lighting.

It is “easy” to some extend design a fashionable office building with the state of the art in HVAC systems and building components resulting in low energy consumption. However, these projects typically do not have very tight economical restrictions.

This project aims to find out if the building shape can make a difference on the energy performance of office buildings, even for the poorest building properties (insulation) and HVAC systems allowed by current standards.

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3. Related work – Literature study A growing body of literature claims that the building shape has significant impact on the energy consumption. This fact encouraged engineers to find optimization of building shape in order to minimize energy use. Most of these optimization studies are based on simplified building thermal models that can provide approximate thermal performance of residential buildings, but they are generally inaccurate in predicting the energy performance of commercial buildings with strong interactions between building thermal inertia and internal gains, which might significantly affect heating, ventilation and cooling loads. Therefore, prediction of energy performance of such buildings depending on several parameters requires detailed whole building energy simulation on hourly basis (8).

Building shape is not an easy parameter to quantify. However, a shape coefficient is required if different building forms are to be compared. Early publications make use of relative compactness* as a shape coefficient indicator.

As an example, Figure 2 shows a selection of different building shapes with identical volume. It is easily remarkable that the external wall area varies from one shape to another. The relative compactness is increasing inversely proportional to the external wall area. The higher the relative compactness, the less wall area is exposed to the external weather conditions and therefore the lower is the heat loss/heat gain (depending on the climate) through the building envelope (8) (9) (10) (11).

The influence of the building compactness on the energy consumption is independently analysed for heating and cooling loads as well as total energy consumption. Figure 3 shows the resulting heating loads of the building shapes introduced in Figure 2 as a function of their relative compactness. A fairly high interrelation of heating load and RC can be noticed. As expected, building shapes with highest relative compactness achieve the lowest heating loads.

Investigations reported by Ourghi, Al-Anzi and Krarti show a clear correlation between building relative compactness and cooling loads in warm climates. The higher is the building relative compactness, the lower are the exterior wall areas, and consequently the lower are the cooling loads and total energy uses (8). Nevertheless, other publications addressed that this

* Relative compactness (RC) is the most commonly used numeric indicator to account the geometry of buildings in a simple manner. It is purely shape dependent and it is defined as the ratio of the volume to the exterior wall area of the building divided by the volume to exterior wall ratio of a reference building. Since most buildings have orthogonal polyhedral shapes, the cube is the most compact form that can be used as a reference (8).

𝑅𝐶 =(𝑉 𝐴𝑒𝑥𝑡 )𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔(𝑉/𝐴𝑒𝑥𝑡)𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒

(Eq.4.1)

Where,

V building conditioned volume, m3 Aext area for all exterior walls for given building, m2 Aext,ref

area for all exterior walls for reference building, m2

Information box 1



trend depends on the window-to-wall ratio (WWR). If WWR is low the tendency mentioned above is fulfilled. That is, an increase in RC leads to a decrease in energy use. However, if WWR is relatively high the trend depends on the building shape due most likely to variation in solar exposure for different building shapes. For example, an L-shaped building with similar RC as a U-shaped building has different wall area and orientation for solar exposure. This difference causes variation in solar gains and consequently in cooling loads. Therefore the relative compactness will not be representative of thermal performance in these cases (12).

The correlation between cooling load and window area is stronger than it is with the relative compactness. As it can be seen in Figure 4 the cooling load (relative cooling load in this case) increases with window area for any window-to-wall ratio.

Figure 2: Example of buildings with different relative compactness

Figure 3: Simulated heating loads as a function of RC of building shapes displayed in Figure 2 (9)

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Figure 4: Relative cooling load as a function of window area for various window-to-wall ratios using a single clear

glazing. *The energy use is relative because it is compared to a reference building (8)

As heating load is determinant for cold climates, cooling load is determinant for warm climates. The review of literature shows that total energy consumption tends to be defined by the determinant load for each climate. Hence, if heating load decreases as the relative compactness increases, total energy consumption of these buildings will also tend to decrease. Total energy consumption can be interpreted as the sum up of all the energy use in a building accounting heating, cooling, ventilation, lighting and hot water demand (DHW). However, no detail analysis of such integration is found in the review of the literature.

If the building shape is different than a square the way its facades are oriented becomes important. The influence of the building orientation was found to have an impact on the energy performance of buildings, but its effect is basically independent of the building shape (12).

Although it does not capture geometric information of the building, the glazing type is another parameter considered in several publications. Figure 5 shows the relative energy use as a function of the window area for different glazing types. It can be notice that the energy use increases as the window area. Only the rate of increase depends on the glazing type.

Figure 5: Relative annual total building energy use as a function of window area for various glazing types in Tunis

climate (8)



Semantic design variables are those that have no relation to geometry of the building. They define various essential performance characteristics of the building such as thermal, lighting or acoustical. They are not worth of interest as they do not capture geometric information of the building.

Summary:

From the literature review, the following conclusions can be found:

x The higher RC of the building shape, the lower is the heating load; x The higher RC of a building shape, the lower is the cooling load in warm climates if

WWR is low. When the WWR is high this trend can change; x The higher window area of the building shape, the higher is the cooling load; x RC does not sufficiently capture morphological properties of the design. External

openings (windows) have a strong influence on the cooling energy demand.

Since glazing area seems to play an important role on the energy performance of buildings, some glazing area indicators should be distinguished. There are two commonly used indicators: window-to-wall ratio and window-to-floor area.

Window-to-wall ratio (WWR): This ratio is defined as the total window area divided by the total wall area of the building.

Window-to-floor area (WFA): This is the ratio defined as the total window area divided by the total building area.

Note that given two building geometries with identical RC and WWR, the total window area of the buildings can be different.

RC1 = RC2

WWR: 50% Wall area: 112 m2

Glazing area: 56 m2

RC1 = RC2

WWR: 50% Wall area: 72 m2

Glazing area: 36 m2

Information box 2

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4. Description of standards The merger of several countries into the European Union led the crucial need of a common standardization among the member states. The European Commission is the executive body responsible for proposing legislation and implementation of decisions in the Union. Since Denmark entered into the EU, European Standards are to be fulfilled in Danish territory. Reducing energy consumption and eliminating wastage are among the main goals of the European Union. EU support for improving energy efficiency will prove decisive for competitiveness, security of supply and for meeting the commitments on climate change made under the Kyoto protocol. Due to the large potential for reducing consumption in buildings, EU has introduced legislation to ensure that they consume less energy. A key part of this legislation is the Energy Performance of Buildings Directive (EPBD), which requires all EU countries to enhance their building regulations and introduce energy certification schemes for buildings (2).

“Energy requirements have been a topic in Danish Building Regulations since 1961, when the first nationwide Building Regulation came into force. Since then, the energy requirements have been stepwise tightened several times. The most recent tightening, from 1st of January 2011, requires 25% better energy performance of all new buildings compared with the 2008 requirements. Targets for the next tightening (additionally 25%) in 2015 have already been defined in the Building Regulations, and there is an on-going work to define the expected requirements for 2020. The EBPD enforced a shift from requirements for the final energy requirements for space heating (previous to 2006) to primary energy requirements for the gross energy consumption of buildings, including space heating, domestic hot water, cooling, electricity consumption for operating the building [i.e. fans, pumps, lighting (only in non-residential buildings)] and potential penalty for indoor temperatures >26 ᵒC.” (13)

More precisely, Danish Building Regulations (BR10) requires that the total demand of the building for energy supply for heating, ventilation, cooling and domestic hot water and lighting per m2 of heated floor area must not exceed 71.3 kWh/m2/year plus 1650 kWh/year divided by the heated floor area. Furthermore, for office buildings to be classified as class 2015-low energy buildings, the requirement for supplied energy for heating, ventilation, cooling, domestic hot water and lighting per m2 heated floor area must not exceed 41 kWh/m2/year plus 1100 kWh/year divided by the heated floor area (14).

BR10 state that buildings must guarantee, under their intended operational conditions, a healthy, safe and comfortable indoor climate in rooms occupied by any number of people for an extended time period. For this to happen, good thermal indoor climate and air quality as well as abolition of noise pollution must be ensured. Design criterion will be discussed later.

An appropriate thermal indoor environment can be maintained in the building by the erection of a heating and if necessary, a cooling system. Fresh and clean air can be provided by direct openings to the exterior or mechanical ventilation systems. If the last one is chosen a heat recovery unit with a dry temperature efficiency no less than 70% must be part of the system. Moreover, for installations with constant air volume, the power consumption of the air movement must not exceed 1800 J/m3 fresh air – specific fan power (SFP) 1.8 W/(l/s) (14).



Lighting of buildings is also considered under the indoor climate conditions. According to the regulations, offices must have windows in order to let daylight come in and therefore make sure that in combination with electric lighting the spaces are well lit. As a guideline, daylight factor of 2% at the workspaces can be accepted by Danish standards (14).

If the energy performance framework set above is used, the individual building elements must be insulated such that the heat loss through them does not exceed the values in Table 1.

Table 1: Minimum insulation requirements for different building elements (14)

Table of U values U value (W/m² K)

External walls and basement walls in contact with the soil. 0.30

Suspended upper floors and partitions to rooms/spaces that are unheated or heated to a temperature more than 8 K lower than the temperature in the room/space concerned.

0.40

Ground slabs, basement floors in contact with the soil and suspended upper floors above open air or a ventilated crawl space. 0.20

Suspended floors below floors with underfloor heating adjoining heated rooms / spaces. 0.50

Ceiling and roof structures, including jamb walls, flat roofs and sloping walls directly adjoining the roof. 0.20

External doors, rooflights, doors and hatches to the outside or to rooms/spaces that are unheated and these as well as glass walls and windows to rooms that are heated to a temperature more than 5 K below the temperature in the room concerned.

1.80

Other requirements than energy performance and indoor climate can be found in Danish Building Regulations. For example, limitations regarding design, layout and fitting out of new buildings are relevant for new office buildings. However, these limitations were disregarded as this investigation aimed to go beyond legal restrictions concerning building shape.

The European standard EN 15251 deals with indoor environmental parameters for design and assessment of energy performance of buildings. It gives recommendations of input values for design and dimensioning of room conditioning systems for heating and cooling load calculations. Table 2 shows recommended design values according to the thermal indoor category that is aimed. These recommendations assume that the clothing of the building occupants is lighter in summer than in winter. As building users might feel cold in the early morning in summer period due to their light clothes, the minimum recommended temperature is 23°C for thermal category II. This implies the possibility of heating requirements in summer season, which would be easily avoided if occupants would wear appropriate clothing (thin sweater or jacket would most likely be enough) in the early morning. Assuming the awareness of the building occupants on this matter, the range of temperatures throughout the year could be between 20°C and 26°C.

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Table 2: Recommended design values of the indoor temperature for design of buildings and HVAC systems for office buildings (15)

Operative temperature (ᵒC) Type of building or space Category Temperature range for heating,

° 1 clo) Temperature range for cooling, °C

0.5 clo)

Landscaped office I 21 – 23 23.5 – 25.5 II 20 – 24 23 – 26 III 19 – 25 22 – 27

When it comes to designing ventilation systems, total ventilation rate for a room is calculated with Equation 5.1 below. Table 3 shows recommended ventilation rates for landscape office buildings depending on the indoor climate category. An indoor air quality category II is aimed for the buildings in this study, therefore a ventilation rate of 1.2 l/s·m2 is recommended during occupied hours.

EN15251 recommends a supplementary ventilation rate of 0.1 to 0.2 l/s·m2 during un-occupied hours so that the air is fresh by the time users get to the building in the mornings. However, infiltration can be calculated as a part of this ventilation rate (15). Danish building regulations simultaneously states a limitation for air leakages on 1.5 l/s/m2 of the heated floor area when tested at a pressure of 50 Pa (14).

= (Eq.5.1) Where,

Total ventilation rate of the room, l/s n Design value for the number of the persons in the room Ventilation rate for occupancy per person (Table 3), l/s, pers A Room floor area, m2 Ventilation rate for emissions from building (Table 3), l/s, m2 Table 3: Recommended ventilation rates for non-residential buildings with default occupant density for low-polluted

buildings (15)

Type of building or

space Category Floor area

(m2/person)

qp qB qtot (l/s, m2)

for occupancy (l/s, m2)

for low-polluted building

Landscaped office

I 15 0.7 1.0 1.7 II 15 0.5 0.7 1.2 III 15 0.3 0.4 0.7

Indoor air quality can be also classified according to CO2 concentration level in the rooms. Table 4 shows acceptable concentration (CO2 ppm) levels for energy calculations.

Table 4: Recommended CO2 concentration above outdoor for energy calculations and demand control (15)

Category Corresponding CO2 above outdoors (ppm)

Outdoor CO2 concentration level

(ppm) (16)

Indoor maximum CO2 concentration level (ppm)

I 350 390

740 II 500 890 III 800 1190



In order to enable people to perform visual tasks efficiently, appropriate lighting must be assured. Table 5 shows recommended design values for lighting level at the working places. The design illumination levels can be supplied by means of daylight, artificial lighting or a combination of both. For health, comfort and energy reasons daylight is preferred over the use of artificial light. EN15251 suggests that an evaluation of the quality of lighting with respect of glare is also essential in calculation of energy performance of buildings as it may affect the use of controls and window screens. Since investigation of this matter is not directly related to the building shape, glare analysis as well as colour rendering of the lighting system will not be part of further discussion.

Table 5: Recommended design illumination levels for office buildings (15)

Type of building Space Maintained luminance, Êm, at working areas, (lux) Remarks

Office buildings

Single offices 500 at 0.8 m Open plan offices 500 at 0.8 m Conference rooms 500 at 0.8 m Corridors 200 at 0.1 m

Finally, EN 15251 also recommends a maximum deviation of 3 – 5 % on the parameters defining the indoor environment based on working hours and total hours. See Table 6.

Table 6: Examples of length of deviations corresponding to 3 and 5 % of the time (15)

3 % / 5 % of period Daily (min) Weekly (hours) Monthly (hours) Yearly (hours) Working hours 15/24 1/2 5/9 61/108 Total hours 43/72 5/9 22/36 259/432

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5. Method This chapter describes the method that was used for carrying out the investigation. The method is described in three main bodies: The creation of the models, decision of an appropriate building energy simulation tool and the implementation of the models in the simulation software. After an analysis of the performance of the simulation tool some software limitations were found and the methodology to overcome these obstructions was described as part of this chapter. To close the chapter, an introduction to the logical procedure of the investigation is given.

5.1. Setting up the models A wide range of different building geometries was chosen for this study. The selection was to show different building forms, distribution of storeys and window areas while keeping some common parameters such as total floor area and volume. A building floor area of 10,000 m2 was considered to be sufficient to illustrate a large office building. A floor-to-floor height of 3m was assumed for all building alternatives, leading to a total building volume of 30,000 m3.

An office building is assumed to provide a comfortable and functional working space for the users. Building occupants must be able to move from one location within the building to another in a relatively short time period. One-storey office buildings of 10,000 m2 would result to be inappropriate with regard of functionality. Consequently, one and two-storey buildings are disregarded in this investigation. Three, five, seven and nine storeys were assumed to be representative of realistic building proportions for office buildings in Copenhagen area.

Some existing office buildings in Copenhagen (Denmark) were used as starting point for selection of realistic building geometries. Figure 6 shows some examples of the geometries used as reference proportions. Appendix 2 shows detailed dimensions and location of such examples. Based on these, several building design alternatives were drawn up based on a single quadratic cell. It was easy to build up proportional models based on such cell. Simply increasing the number of cells and putting those together different shapes were proposed. The floor plans include: quadratic, rectangular, L-shape, T-shape, U-shape, H-shape and quadratic and rectangular buildings with a patio (Table 7).

Based on the total floor area (10,000 m2), the storeys (3, 5, 7 and 9) and the shape proportions from Table 7, dimensions of building design alternatives were found as follows: First, total building area, was divided by the number of the storeys, st. For example, 10,000 m2 divided by 3 storeys gives a floor area of 3,333 m2. Second, if a shape with 4 cells was to be built (four times long as it is wide), the floor area (in this case 3,333 m2) should be divided by the number of cells desired, n (4 cells), giving as a result the area that a single cell must have for the building shape to be with an aspect ratio of 1/4 and yet of 10,000 m2. Finally, the edge of the cell should be calculated as the square root of the cell area, . Following these steps, dimensions of different building shapes with the same floor area and volume could be found. = (Eq.6.1)

= (Eq.6.2)



= √ (Eq.6.3)

Where,

A Floor area, m2 Cell area, m2

Total building floor area, m2

st Number of storeys n Number of cells of desired shape (from Table 7); a Length of sides of the cell.

Figure 6: Example of office buildings located in Copenhagen

Once the proportions that the buildings should have were found (Table 7), Google Sketch-up appeared to be a quick, useful and still accurate tool for carrying out model generation. A graphic representation of resulting building models, a reference name, external dimensions and relative compactness are shown for each building respectively from Table 8 to Table 15. These models were later imported to a building energy simulation tool for energy performance and indoor climate investigation.

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Table 7: Proposed building shapes according to the number of cells

1 cell 2 cells 3 cells 4 cells

4 cells 5 cells 6 cells

8 cells


12 cells 16 cells



7 cells 8 cells

11 cells



5.1.1. Building shape catalogue

Table 8: Quadratic buildings

A=B

3 st

orey

s

Quadratic 1 A=57.7m; B=57.7m; h=9m

RC: 0.66

5 st

orey

s


RC: 0.87

7 st

orey

s


RC: 0.96

9 st

orey

s

Quadratic 4 A=33,33m; B=33,33m; h=27m

RC: 0.99

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Table 9: Rectangular building shape alternatives A=2B A=3B A=4B

3 st

orey

s

R1 A=81.6m; B=40.8m; h=9m

RC: 0.65

R5 A=100m; B=33.3m; h=9m

RC: 0.64

R9 A=115.5m; B=28.9m; h=9m

RC: 0.63

5 st

orey

s

R2 A=63.3m; B=31.6m; h=15m

RC: 0.85

R6 A=77.5m; B=25.8m; h=15m

RC: 0.82

R10 A=89.4m; B=22.4m; h=15m

RC: 0.79

7 st

orey

s

R3 A=53.5m; B=26.7m; h=21m

RC: 0.93

R7 A=65.5m; B=21.8m; h=21m

RC: 0.89

R11 A=75.6m; B=18.9m; h=21m

RC: 0.85

9 st

orey

s

R4 A=47.1m; B=23.6m; h=27m

RC: 0.96

R8 A=57.7m; B=19.2m; h=27m

RC: 0.91

R12 A=66.6m; B=16.7m; h=27m

RC: 0.86



Table 10: L-shaped building shape alternatives

A=B A=2B A=3B

3 st

orey

s

L1 A=33.3m; B=33.3m; h=9m

RC: 0.64

L5 A=57.7m; B=28.9m; h=9m

RC: 0.63

L9 A=77.5m; B=25.8; h=9m

RC: 0.61

5 st

orey

s

L2 A=25.8m; B=25.8m; h=15m

RC: 0.82

L6 A=44.7m; B=22.4m; h=15m

RC: 0.79

L10 A=60m; B=20m; h=15m

RC: 0.76

7 st

orey

s

L3 A=21.8m; B=21.8m; h=21m

RC: 0.89

L7 A=37.8m; B=18.9m; h=21m

RC: 0.85

L11 A=50.7m; B=16.9m; h=21m

RC: 0.81

9 st

orey

s

L4 A=19.2m; B=19.2m; h=27m

RC: 0.91

L8 A=33.3m; B=16.7m; h=27m

RC: 0.86

L12 A=44.7m; B=14.9m; h=27m

RC: 0.82

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Table 11: T-shaped building design alternatives

A=B A=2B A=3B

3 st

orey

s

T1 A=28.9m; B=28.9m; h=9m

RC: 0.63

T5 A=51.6m; B=25.8m; h=9m

RC: 0.61

T9 A=70.7m; B=23.6m; h=9m

RC: 0.60

5 st

orey

s

T2 A=22.4m; B=22.4m; h=15m

RC: 0.79

T6 A=40m; B=20m ; h=15m

RC: 0.76

T10 A=54.8m; B=18.3m; h=15m

RC: 0.74

7 st

orey

s

T3 A=18.9m; B=18.9m; h=21m

RC: 0.85

T7 A=33.8m; B=16.9m; h=21m

RC: 0.81

T11 A=46.3m; B=15.4m; h=21m

RC: 0.78

9 st

orey

s

T4 A=16.7m; B=16.7m; h=27m

RC: 0.86

T8 A=29.8m; B=14.9m; h=27m

RC: 0.82

T12 A=40.8m; B=13.6m; h=27m

RC: 0.79



Table 12: U-shaped building alternatives

A=B A=2B 2A=B

3 st

orey

s

U1 A=25.84m; B=25.8m; h=9m

RC: 0.57

U5 A=47.1m; B=23.6m; h=9m

RC: 0.56

U9 A=43.6m; B=21.8m; h=9m

RC: 0.55

5 st

orey

s

U2 A=20m; B=20m; h=15m

RC: 0.71

U6 A=36.5m; B=18.3m; h=15m

RC: 0.69

U10 A=33.8m; B=16.9m; h=15m

RC: 0.67

7 st

orey

s

U3 A=16.9m; B=16.9m; h=21m

RC: 0.76

U7 A=30.9m; B=15.4m; h=21m

RC: 0.73

U11 A=28.6m; B=14.3m; h=21m

RC: 0.71

9 st

orey

s

U4 A=14.9m; B=14.9m; h=27m

RC: 0.77

U8 A=27.2m; B=13.6m; h=27m

RC: 0.73

U12 A=25.2; B=12.6m; h=27m

RC: 0.71

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Table 13: H-shaped building alternatives

A=B A=2B 2A=B

3 st

orey

s

H1 A=21.8m; B=21.8m; h=9m

RC: 0.55

H5 A=40.8m; B=20.4m; h=9m

RC: 0.54

H9 A=34.8m; B=17.4m; h=9m

RC: 0.52

5 st

orey

s

H2 A=16.9m; B=16.9m; h=15m

RC: 0.67

H6 A=31.6m; B=15.8m; h=15m

RC: 0.65

H10 A=27m; B=13.5m; h=15m

RC: 0.61

7 st

orey

s

H3 A=14.3m; B=14.3m; h=21m

RC: 0.71

H7 A=26.7m; B=13.4m; h=21m

RC: 0.68

H11 A=22.8m; B=11.4m; h=21m

RC: 0.63

9 st

orey

s

H4 A=12.6m; B=12.6m; h=27m

RC: 0.71

H8 A=19.2m; B=11.6m; h=27m

RC: 0.68

H12 A=22.8;B=11.4; h=27m

RC: 0.62



Table 14: Quadratic buildings with patio

A=B A=2B A=3B

3 st

orey

s

O1 A=20.4m; B=20.4m; h=9m

RC: 0.56

O5 A=33.3m; B=16.7m; h=9m

RC: 0.53

O9 A=43.3m; B=14.4m; h=9m

RC: 0.50

5 st

orey

s

O2 A=15.8m; B=18.8m; h=15m

RC: 0.69

O6 A=25.8m; B=12.9m; h=15m

RC: 0.63

O10 A=33.5m; B=11.2m; h=15m

RC: 0.58

7 st

orey

s

O3 A=13.4m; B=13.4m; h=21m

RC: 0.74

O7 A=21.8m; B=10.9m; h=21m

RC: 0.65

O11 A=28.4m; B=9.5m; h=21m

RC: 0.59

9 st

orey

s

O4 A=11.8m; B=11.8m; h=27m

RC: 0.74

O8 A=19.2m; B=9.6m; h=27m

RC: 0.64

O12 A=25m; B=8.3; h=27m

RC: 0.57

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Table 15: Rectangular buildings with patio

A=2B A=3B A=4B

3 st

orey

s

Q1 A=36.5m; B=18.3m; h=9m

RC: 0.54

Q5 A=50m; B=16.7m; h=9m

RC: 0.53

Q9 A=61.7m; B=15.4m; h=9m

RC: 0.51

5 st

orey

s

Q2 A=28.3m; B=14.1m; h=15m

RC: 0.66

Q6 A=38.7m; B=12.9m; h=15m

RC: 0.63

Q10 A=47.8m; B=12m; h=15m

RC: 0.60

7 st

orey

s

Q3 A=23.9m; B=12m; h=21m

RC: 0.69

Q7 A=32.7m; B=10.9m; h=21m

RC: 0.65

Q11 A=40.4m; B=10.1m; h=21m

RC: 0.61

9 st

orey

s

Q4 A=21.1m; B=10.5m; h=27m

RC: 0.68

Q8 A=28.9m; B=9.6m; h=27m

RC: 0.64

Q12 A=35.6;B=8.9; h=27m

RC: 0.60



5.2. Building energy simulation program: IES<VE> A wide variety of building energy simulation programs have been developed over the last decades. A paper published by the US Department of Energy makes a comparison of the features and capabilities of twenty major building energy simulation programs such as BLAST, DOE2.1E, ECOTEC, EnergyPlus, IDA ICE, IES <VE>, TRNSYS, etc. This comparison was made based on the information provided by the program developers in the categories of: zone loads, building envelope and daylighting; infiltration, ventilation and multi-zone airflow; HVAC systems; and economical evaluation. Tables 1 to 5 in Appendix 1 show such comparison of the simulation programs. According to table 1 IES <VE> seems to be the most detailed software in thermal analysis, followed by EnergyPlus, IDA ICE and Tas. Regarding the other categories of the comparison, capabilities of IES<VE> are identical to others tools (EnergyPlus, ESP-r, IDA ICE, DeST, BSim, Tas, TRNSYS…) (17).

IES <Virtual Environment> (IES<VE>) is a building energy simulation software that provides design professionals with a range of design oriented building analysis within a single software environment. The model uses a 3-D geometric representation of the building that supplies with basic information to other application modules within the software such as ApacheSim, MacroFlo, Apache, Radiance, etc. ApacheSim is a dynamic thermal simulation tool based on first-principles mathematical modelling of building heat transfer processes. The program provides an environment for the detailed evaluation of building and system designs, allowing them to be optimized with regard to comfort criteria and energy use. ‘ApacheSim’ can be linked dynamically to ‘MacroFlo’ for natural ventilation and infiltration analysis, to ‘ApacheHVAC’ for component based system simulation, to ‘SunCast’ for detailed shading and solar penetration analysis and ‘Radiance’ for daylight and electric lighting simulation. Among the issues that can be addressed with ‘ApacheSim’ are thermal insulation (type and placement), building dynamics and thermal mass, building configuration and orientation, climate response, glazing, shading, solar gain, solar penetration, internal gains, air tightness, natural ventilation, mechanical ventilation, mixed-mode systems and HVAC systems. Conduction, convection and radiation heat transfer processes for each element of the building fabric are individually modelled and integrated with models of room heat gains, air exchanges and plant. The simulation is driven by real weather data and may cover any period from a day to a year. The time-evolution of the building’s thermal conditions is traced at intervals as small as one minute (18).

‘Vista’ is a graphics driven tool for data presentation and analysis of results. It provides facilities for interrogating the results in detail or at various levels of aggregation, and includes functions for statistical analysis. Simulation results include:

x over 40 measures of room performance including air and radiant temperature, humidity, CO2, sensible and latent loads, gains and ventilation rates;

x comfort statistics; x Natural ventilation rates through individual windows, door and louvers; x Surface temperatures for comfort analysis x Plant performance variables x Loads and energy consumption x Carbon emissions

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From all this it was concluded that IES <VE> appeared to be an appropriate tool for the purpose of this investigation.

5.3. Implementation of models in IES<VE>

5.3.1. Overview

As mentioned before, IES<VE> is an integrated suite of applications linked by a common user interface. Relevant <Virtual Environment> modules for this investigation are summarized below:

x ModelIT – for geometry creation and editing x Apache – for thermal analysis x SunCast – shading visualization and analysis x Radiance – daylighting analysis x MacroFlo – natural ventilation and indoor air movement x ApacheSim – for dynamic thermal simulation linked to previous modules x Vista – for result analysis

In order to carry out detailed simulation of the energy performance and indoor climate of the buildings all these modules were used. In the following all these are explained in more detail.

5.3.2. Model IT

Model IT is the program where the geometric representation of the model is drawn. However, Google Sketch Up appeared to be quicker at generation of models in 3D. Therefore, this software was used for creating the external walls and internal partitions of the models. These were later imported into Model IT, where windows were included in the following proportions: Identical window-to-wall ratio (WWR = 50%) was used in all building alternatives with constant floor-offset of 0,80m and window height of 1.5m.

5.3.3. Apache

Apache is the thermal analysis tool that provides ApacheSim (the dynamic simulation tool) with the thermal data required for calculating the performance of buildings. The building location and weather data, constructions data and room data must be specified in this module.

Building location and weather data The climate is an important driving force affecting indoor thermal conditions in the buildings. In this case, the location was set to Copenhagen (Denmark) since the investigation was to be carried out under Danish climate conditions. Apache uses the nearest available weather data, which in this case was from Kastrup (Denmark).

Construction data Construction data consists of thermal and physical properties of the building components used in the models. Thermal transmittance of constructions was set according to the minimum insulation requirements stated in Danish Building Regulations 2010 (see Chapter 4). Thermal transmittance of building components are summarized in Table 16. More detailed information about composition of building components can be found in Appendix 3.



Table 16: U-values of building components

Building component Thermal transmittance - U-value (W/m2K) Roof 0.20 External walls 0.30 Ground floor 0.20 Windows

x Light transmittance 0.76 [-] x Solar gain coefficient 0.40 [-]

1.80

Room data In the Apache module each room has a set of attributes that describe conditions in the room. This data, known as Room Data, provides input to the thermal analysis program. In Apache view Room Data is displayed on five tabs:

x General x Room conditions x System x Internal gains x Air exchange

Heating and cooling set points as well as thermal modeling settings need to be set in the next tab, room conditions. Next, parameters describing the HVAC system serving the room are set in the tab named ‘System’. Internal gains to the room were specified in the tab with the same name. Finally, infiltration and auxiliary ventilation air exchanges were set in the last tab. Detailed description of the input used in each tab is described in the following sections.

The building occupancy and the usage pattern will determine the energy demand for heating and cooling as well as for ventilation and lighting of the building. For example, the lighting should be turned on only when the building is being used. That is why some general occupancy profiles must be set in the first place. Typical office buildings are occupied from Monday to Friday with a time schedule of 8am to 5pm. Therefore, this occupancy profile seemed to be realistic for this investigation. Something additional to consider is that not everybody is in his/her working place all the time during this time schedule (e.g. holidays, conferences, meetings outside of the building, etc.) and consequently a diversity factor of 85% was assumed to cope with this issue. The occupancy profile is shown in Figure 7.

Figure 7: Occupancy profile during weekdays

0.00

0.20

0.40

0.60

0.80

1.00

Occ

upan

cy fa

ctor

[-]

Time [h]

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x General No input needs to be set the first tab ‘General’ as only information such as room name and ID, templates, floor area and volume of the room are shown in this tab.

x Room conditions The operation of room heating and cooling plant must be specified under this tab. A heating profile specifies whether the heating system is in operation or not according to building occupation. The occupancy profile shown in Figure 7 was used for this purpose. Furthermore, the temperature set points for heating control must be set. In order to achieve an efficient control of temperatures and energy use, a variable temperature profile was set for the heating system control. As it can be seen in Figure 8, an indoor temperature of 16°C is ensured during unoccupied hours (outside working hours and weekends) and a minimum temperature of 20.5°C during occupied hours (working hours). If an indoor temperature of 20°C is to be provided by the time the building is occupied at 8am, the thermal capacity of the heating system and thermal mass of the building must be considered. The heating system might take some time to provide an indoor temperature of 20°C when the building has been unoccupied for a while. That is why the set point temperature of 20.5°C was set at 7am instead (1 hour before occupancy). In addition, the minimum temperature of 16°C during unoccupied hours ensures that the building does not get too cold so that the thermal capacity of the heating system would not be able to heat the building up to 20°C in an hour.

Figure 8: Heating set point temperatures during weekdays

The operation of the cooling system is theoretically the same as for the heating system. A profile with cooling set point temperatures must be set for the control of the cooling system. This profile is shown in Figure 9. A set point temperature of 25.5°C will ensure that the cooling system will not let the indoor temperatures get above 26°C.

15

16

17

18

19

20

21

22

23

Set p

oint

tem

pera

ture

[°C]

Time [h]



Figure 9: Cooling set point temperatures

x System Parameters related to the HVAC system at room level were set in this tab. The use of multicolumn radiators was assumed for the heating system of the office buildings. Therefore, a heating plant radiant fraction of 0.2 was set accordingly to what is stated in the guidelines of IES<VE> (19). The cooling plant radiant fraction however was set to zero because cooling was handled entirely by convection (cool air).

The seasonal coefficient of performance of the heating system (SCoP) was assumed to be 1 for all the buildings as it was assumed that district heating system is providing heat supply and an energy factor will be apply afterwards. The ventilation heat recovery effectiveness was set to 0.7 in order to fulfill minimum requirements of BR10. The COP of the chillers providing cool air in the buildings was assumed to be 2.5 and the specific fan power (SFP) for the ventilation system was of 1.8 W/(l/s), as that is the minimum required by BR10 (14).

The domestic hot water (DHW) used in office buildings is determined by the amount of users and the user pattern. A DHW use of about 100 litres/m2 year was assumed for all building alternatives resulting on an energy consumption of 5 kWh/m2 year.

x Internal gains Heat sources such as occupants, appliances and artificial lighting must be considered in the simulations. They will have an impact on the energy consumption (increasing cooling load) and thermal comfort (higher indoor temperatures). Internal gains assumed for the office buildings are summarized in Table 17.

As mentioned before, the occupancy of the office buildings was assumed to be 85 % during occupied hours. This assumption was consequently applied for internal gains dependent on occupancy: People, equipment and task lighting.

23

24

25

26

27

28

29

30

Set p

oint

tem

pera

ture

[°C]

Time [h]

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Table 17: Internal gains

Internal gain Description Variation profile Diversity factor [-] People The occupancy density was set to 15

m2/person according to recommendations for office buildings in EN15251 x Maximum sensible gain: 90 W/person

(6W/m2)

Occupancy profile 0.85

Equipment x Maximum sensible gain: 6 W/m2 Occupancy profile 0.85 Lighting Total power density of 10.6 W/m2 consisting

of: x 9.3 W/m2 for general lighting x 1.3 W/m2 for task lighting

Occupancy profile x Dimming profile x On/off

1 (general lighting) 0.85 (task lighting)

A minimum lighting level of 200 lux must be ensured during the whole occupied time for general lighting. However, as Danish Building Regulations suggest this illumination can be provided by daylight, artificial light or a combination. The use of daylight can reduce the energy consumption of artificial lighting and simultaneously decrease the heat gain from luminaires. If the artificial lighting is to be dimmed according to the availability of daylight in the buildings, Radiance module must be used. This module will ensure that ApacheSim takes into account the illumination levels in the building where daylight is available and dimm the output of artificial lighting to required levels. Task lighting however was assumed to be in operation when illuminance is below 500 lux during working hours, and turned off the rest of the time; assuming such a behavior from the building occupants. The use of Radiance module is described later in 5.3.5.

Typical power density in standard open plan offices runs about 10 W/m2 (20). Better efficiency could easily assumed, but as worse case scenarios were going to be assumed for building services, standard luminaires were used. More details about the luminaires can be found in Appendix 4.

x Air exchanges Finally, air exchanges such as infiltration and ventilation were specified in this tab. When buildings are not tight enough, the infiltration can have a significant impact on the heating energy demand due to the additional heat loss. An infiltration of 0.12 l/s m2 was assumed for all buildings.

The mechanical ventilation supplying fresh air to the office space was set as auxiliary ventilation in this tab. Following the advice of EN 15251, a ventilation rate of 1.2 l/s m2 during occupied hours was set aiming an indoor climate of category II (15).

5.3.4. SunCast

SunCast is an application integrated in IES<VE> that performs shading and solar insolation analysis. Therefore, this tool calculates impact such as building orientation or self-shading effect. The calculations are performed for any sun position along the year. After a quick simulation, the application enables the possibility to tick the SunCast link in ApacheSim in order to integrate the shading calculations into the building performance analysis.



5.3.5. Radiance

Radiance is a software package developed by the Lighting Systems Research group at the Lawrence Berkeley Laboratory in California, USA. It was developed as a research tool for predicting the distribution of visible radiation in illuminated spaces. It can be used to calculate lighting levels, daylight factors or glare for daylight and/or artificial lighting. It is now integrated with the rest of IES software packages. The use of such program was required in order to enable dimming of artificial lighting wherever the available daylight would allow it.

For Radiance to calculate the illumination level in a room, a sensor must be placed in the room first. Unfortunately, Radiance is limited regarding placement of sensors. The program only allows the placement of one sensor in each room. Consequently the models must be subdivided in as many rooms as sensors are required. If results were to be accurate, the grid created with room division should be very dense. Hence, the model would be divided in many rooms. In this case, the time that the software takes for performing the simulation might get too long. On the other hand, if the grid is very coarse, therefore the rooms are few, measured daylight levels would not be representative of the entire room and results would be inaccurate. The room division of the models needed to be investigated and an optimal room division finding the balance between accuracy and simulation speed was required. This investigation is described later in chapter 5.4.1.

Once the models were divided in rooms for the purpose of placing as many sensors as required, the partition walls would not let the daylight pass through to the inner rooms. Therefore, holes replacing the 100 % of the surface of partition walls were placed in the models. This would let Radiance measure illuminance levels and yet simulate an open plan office space. When setting sensors in the rooms, Radiance does not always place them right in the centre of the room. Since measurements were to be taken under the same conditions for all the models, their position was corrected to the centre in all the models.

Moreover, Radiance requires input values for the reflectance of the surfaces and the visual light transmittance of the windows, so that it can calculate realistic distribution of daylight in the interior of the model. Input values used for the simulations are summarized in Table 18.

Table 18: Surface properties used as input for simulation of daylight distribution in Radiance

Surface Reflectance [-] External wall (exterior) 0.37 External wall (interior) 0.72 Roof (interior) 0.80 Ground (exterior) 0.20 Floor 0.25 Ceiling 0.80

Visible light transmittance [-] External windows 0.76

5.3.6. MacroFlo

As Apache and SunCast, MacroFlo is an integrated program within the IES<VE>. It analyses the infiltration and natural ventilation in buildings. It uses a zone airflow model to calculate bulk air movement in and through the building, driven by wind and buoyancy induced pressures.

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Since the models are divided in rooms and these partitions are knocked out with holes, the air movement must be ensured enabling MacroFlo link when performing the dynamic simulation with ApacheSim. In order to confirm the need of using this program an investigation of the air movement in the interior of a simple model was carried out. This investigation is described in 5.4.2.

5.4. Investigations prior to simulations

5.4.1. Simplified method to find an appropriate room division

As explained in 5.3.5 above, this investigation was necessary for simplification of the models before they could be implemented in IES<VE>. One storey rectangular building of 40 m x 80 m was used for this analysis. The model was divided in rooms of 5 m x 5 m, which were assumed to be small enough for an accurate measuring of daylight in the building. Figure 10 shows average illuminance levels (lux) along the year in the rooms. On the left figure, only the numerical values are shown. On the right, some rooms with similar illuminance had been grouped into zones (notice a different colour for each zone). Based on this, further room division alternatives were created in a logical order. First, an alternative with 25 rooms was found. As the number of rooms and simulation time was quite long, alternatives with fewer rooms were required. Alternatives 2, 3 and 4 were consequently proposed reducing considerably the amount of rooms up to 9 rooms. Figure 11 shows the room division alternatives. The lighting energy demand of these models was calculated and their relative error with respect to the lighting energy demand with respect to the reference room division can be found in Figure 12.

Results suggest that a single ring (formed by the rooms directly facing the exterior) was not accurate enough. The deviation was about 24 % for alternatives with a single ring: Alternatives 3 and 4. Furthermore, the difference on the performance of these two alternatives was found to be insignificant, which led to the conclusion that the corners of alternative 4 were representative enough. Based on these two conclusions, alternative 5 and 6 were created. Deviation of alternative 5 appeared to be lower than the other. Consequently, alternative 5 was found to be the most representative room division for this kind of building. The amount of rooms with respect to the reference room division was reduced to 1/8 implying a deviation of 5 %. The simulation time was reduced by 80 % (from 2h 10 min -reference division- to 25 min - alternative 5).



395 231 230 226 233 222 231 386

232 18 12 12 12 13 19 230

228 12 4 3 3 3 12 228

228 11 3 2 2 3 12 226

227 12 3 2 2 3 11 227

227 12 3 2 2 2 12 228

226 12 3 2 2 3 12 227

225 12 3 2 2 3 11 227

227 12 3 2 2 3 12 226

226 11 2 2 2 3 12 227

227 12 3 2 2 3 12 228

226 12 3 2 2 3 12 226

228 12 3 2 2 3 12 228

227 12 4 3 3 4 13 228

232 18 12 11 12 12 19 232

400 233 228 228 227 227 232 397

395 231 230 226 233 222 231 386

232 18 12 12 12 13 19 230

228 12 4 3 3 3 12 228

228 11 3 2 2 3 12 226

227 12 3 2 2 3 11 227

227 12 3 2 2 2 12 228

226 12 3 2 2 3 12 227

225 12 3 2 2 3 11 227

227 12 3 2 2 3 12 226

226 11 2 2 2 3 12 227

227 12 3 2 2 3 12 228

226 12 3 2 2 3 12 226

228 12 3 2 2 3 12 228

227 12 4 3 3 4 13 228

232 18 12 11 12 12 19 232

400 233 228 228 227 227 232 397

Figure 10: Reference room division with average room illuminance levels (lux) during the year

Figure 11: Room division alternatives and the respective amount of rooms

25 rooms 21 rooms 17 rooms 9 rooms

17 rooms 13 rooms

1 2 3 4

5 6

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Figure 12: Relative error on artificial lighting energy demand of room division alternatives 1 to 6

Once the method of dividing the model in rooms was established for rectangular buildings, other shapes such as U-shaped buildings required a further investigation of the self-shading effect. Figure 13 shows 4 different alternatives for the division of the outer ring that might be influenced by the self-shading effect. As it can be seen in the figure, the alternatives are based on the conclusions found previously. The differences between the alternatives are highlighted in red. A reference room division such as the one for rectangular buildings shown in Figure 10 was used for assessment of the accuracy of the alternative room divisions. Based on the relative errors of the artificial lighting demand with respect to the reference model, alternative 4 was found to be the most attractive solution. The deviation is slightly higher than the other options but the model is considerably simpler, leading simultaneously to models that take less simulation time (50 min for alternative 1 versus 35 min for alternative 4). Consequently, a deviation of 0.47 % from the simplest model to the most accurate was considered negligible. The similarities of the fourth alternative with the chosen model configuration for rectangular buildings are remarkable.

Summarising the conclusions found in the investigation, it was found that detailed measurement of daylight is required in the corners of the models, whereas a single sensor can be placed along the building sides. The simplified method for the room division is summarized below.

Alternative 1 Alternative 2 Alternative 3 Alternative 4 Alternative 5 Alternative 6% 4.57 5.96 23.99 24.25 4.98 5.95

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26.00Re

lativ

e er

ror [

%]



Figure 13: Room division alternatives for the self-shading buildings

Figure 14: Relative error on artificial lighting demand of alternatives 1 to 4

Room division methodology The investigation with regard of the room division was satisfactory finding a good balance between accuracy and simulation time. The amount of rooms required in the models and consequently the simulation time was reduced by 80 % implying a deviation of 5.64 % on the daylight measurement. Considering the large amount of simulations to carry out for the investigation the time required for generation of the models and the simulation time was of great importance.

The room division for the models must be carried out following the example (Figure 15) below:

Alternative 1 Alternative 2 Alternative 3 Alternative 4% 5.17 5.67 5.54 5.64

0

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2

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4

5

6

7

8

Rela

tive

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]

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x A: Two rings must be distinguished on the perimeter of the building (1 and 2 coloured with light and darker grey respectively). The length of the rooms in these rings will be defined by the specific building shape, whereas the width is set to 5m by default;

x B: Corners of the models must be defined with a room (5m x 5m);

x C - D: Rest of the rooms in the model can be merged together considering that the rooms must be rectangular (sensors are placed in the centre of the room) and therefore the blank space in the interior of the models must be subdivided e.g. as in Figure 15.

Figure 15: Example of L-building shape with concluding room division methodology

5.4.2. Consequences of removing partition walls

It is well known that the way to divide interior spaces into several separate and intimate spaces is by using partition walls. If the materials used for this kind of wall are opaque and considerably dense (gypsum, concrete, insulation, etc.), the wall shows a thermal resistance as well as a physical barrier to noise, air and light from its surrounding spaces. According to the conclusions from 5.4.1 the models must be divided in rooms for Radiance application to be able to measure daylight penetration in the models within acceptable deviations. Partition walls should only be placed with the purpose of placing daylight sensors where necessary. Therefore they should not influence daylight, heat and/or mass transfer from one room to another if the model is to simulate an open-plan office building. Placement of holes with the same surface area as the partition walls should have the effect that is sought. However, a simple model should be tried out in order to confirm this method works as expected.

Model IT application should be used for modifying the interiors. A simple model divided in two spaces by a partition wall was tested (Figure 16).

A

BCD1

A

21



Figure 16: Simple model division in two spaces for testing heat and mass transfer in the interior when using partition

walls

A yearly simulation was performed for each of the cases: with and without the partition wall but a hole. One of the rooms was kept with the same conditions as in chapter 5.3, whereas the ventilation and infiltration air exchanges were removed for the other room. Furthermore, the cooling system was disabled so that the temperatures in the rooms would not be cut off at 26 °C, but it would rise independently in each room. By this way, the differences in temperatures would be more notorious. If indoor environment in the rooms was indeed very different, the methodology would not work. If thermal conditions and heat transfer from one room to another was mostly through convection, similar temperatures should be found.

Figure 17 shows the air temperatures when the model is divided in two rooms by a partition wall for a day in June. The temperature difference from one room to another are very obvious, e.g. at 2pm the difference is of 11.9 °C. Figure 18 shows the air temperatures when the partition wall is replaced by a hole. In this case the temperature difference between the rooms at 2pm is only 1 °C. This indicates that the fact of replacing the partition wall with the hole seems to mix the air of the rooms leading to a more homogeneous environment.

Figure 17: Temperatures of the rooms for 29th of June when the model is divided in two rooms by a partition wall

00:00 06:00 12:00 18:00 00:00

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Air temperature: Room 001 (test with partition wall.aps) Air temperature: Room 002 (test with partition wall.aps)

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Figure 18: Temperatures of the rooms for 29th of June when the partition wall is replaced by a hole with equal surface

Furthermore, the internal conduction gain of the rooms was investigated with the same method. As the “external conduction” of the model represents the heat loss (by conduction) through the building envelope that is in contact with the external weather conditions, the “internal conduction” represents only the heat transferred by conduction through the partition walls. Since the heat transferred from one room to another does not leave the building envelope, the heat gained in a room should be lost from the other one leading to a heat balance (see Table 19). It is remarkable that when the rooms are divided with a partition wall the conduction is considerably higher than when the wall is replaced by a hole. Since the temperature of the rooms are similar in the second case (with the hole) and the conduction among the rooms is that low, it can be concluded that the air is mixed when the spaces are divided by holes and the heat transfer happens by convection instead. Therefore, the internal room divisions of the models can be replaced by holes that will let light, air and heat pass through, making the models comparable to real open-plan office spaces.

Table 19: Total internal conduction gain of the rooms with both test conditions throughout a year

Internal conduction gain (MWh)

Partition wall between rooms Hole between rooms

Room 1 -1.250 -0.172

Room 2 1.250 0.172

00:00 06:00 12:00 18:00 00:00

55

50

45

40

35

30

25

20

15

Tem

pera

ture

(°C)

Date: Tue 29/Jun

Air temperature: Room 001 (test with hole.aps) Air temperature: Room 002 (test with hole.aps)



5.5. Investigation process Often research work requires modifying directions of along the investigation. The process followed in this investigation is described in the following. Finding out the impact of the office building shape on the energy consumption was main objective for this project. That is why a wide variety of building performance simulations was required as a starting point. The window to wall ratio (WWR = 50) used for this investigation was constant for all buildings and as the relative compactness changed from one building shape to another, the total glazing area was proportionally changed.

The impact of the glazing area on the energy consumption of buildings was found to be stronger than the influence of relative compactness. In fact, it was hard to see any influence of the relative compactness. Therefore, contribution of each of these factors required to be investigated separately. Rectangular buildings were appropriate for carrying out a simplified analysis of the models with no glazing (WWR = 0) and with equal window to floor area (WFA = 7%), so that the glazing would not be overlapping the impact of the relative compactness. Once this investigation was conclusive, the impact of building compactness was also investigated for low energy buildings.

Figure 19: Graphic representation of the investigation progress

Boxes in yellow represent simulations that included real measurement of daylight and therefore lighting energy savings were included. Grey boxes did not take into account daylight measurements for the following reasons. The unclear influence of relative compactness was regarding thermal transfer, where the impact of daylight would be only indirect (lower internal gains from artificial lighting). Second and most important, the simplification of the models by room division would not let an accurate measurement of daylight due to the change of window dimensions.

Finally, as conclusions regarding building shape were found throughout the simulation of the buildings illustrated in the building catalogue 5.1.1, only the thermal performance of the buildings envelope was to be investigated. Investigation of rectangular buildings was assumed to be enough in order to get such conclusions.

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6. Results Results obtained from simulations with IES<VE> are presented in this chapter. First, the performance of the building shapes is compared to the requirements summarized in chapter 5 energy-wise as well as with respect of indoor climate. Later, the energy performance of the buildings is analysed from different perspectives such as the impact of relative compactness, glazing area, orientation, etc.

6.1. Validation of energy performance and indoor climate Fulfilment of the energy performance and indoor climate of the buildings will be analysed in this sub-chapter.

Energy performance of buildings Results of the energy consumption of the buildings are shown from Table 21 to Table 28 (page 56). Heating and cooling loads, ventilation and artificial lighting energy demand as well as energy for domestic hot water (DHW) are shown separately and summed up as the total energy consumption for each building design. According to results obtained from IES<VE> the total energy demand of all building alternatives is below 71.4 kWh/m2 and therefore they all fulfil with the energy requirements stated in BR10.

x Heating load The heating load is the total heat per unit time that must be supplied to the building in order to maintain set temperature levels that ensure required thermal conditions. Several factors determine the heating load: insulation levels of the building, air tightness, internal gains (people, lighting and equipment), solar gain and heat loss through natural or mechanical ventilation (in this case mechanical), etc. All these factors are considered by ApacheSim when calculating the heating load. The resulting “Boilers load” in Vista represents the sum of the loads (outputs) of room heating and air heating of the HVAC system. This was later multiplied by an energy factor of 0.80 due to the assumed use of district heating for the heating supply to the building. BR10 suggest the use of such energy factor in this case (14).

x Cooling load “Chillers load” represents the sum of all loads for the chillers of the system. This load was divided by the COP of the chillers (2.5 kW/kW) and therefore transformed to electricity use. Later, an electricity factor of 2.5 was multiplied so that the energy use is comparable to heating energy demand.

x Ventilation energy demand Energy used by fans, pumps and controls within the mechanical ventilation system is considered here. As the ventilation system handles the air conditioning of the buildings, the energy used by the mechanical ventilation increases when there is a cooling load. ApacheSim assumes an increase of the energy use for the fans and pumps of 10 % of the energy used by chillers when air conditioning is in operation. Therefore, total ventilation energy demand is calculated as the sum of both: the energy used by fans, pumps and controls for providing the ventilation air exchange required by the standards, and the additional energy use of the



mechanical ventilation system when air conditioning is used. The energy supplied in this case is electricity and the factor 2.5 was used for comparison with the energy use for heating.

x Lighting energy demand In Vista the “total lights energy” represents the electricity consumption associated with lighting. This is, more precisely the energy required for supplementing the daylight available in the building with artificial lighting in order to achieve minimum illumination requirements. The energy factor of 2.5 was used one more time as the energy is to be compared with other end uses.

x Domestic hot water (DHW) energy demand As the buildings are simulated under same conditions, domestic hot water demand would not vary from one building alternative to another. The user pattern of 100 l/m2 year was assumed for all building alternatives. The energy use was estimated to be 5 kWh/m2 year.

x Total energy demand The energy demand of end uses mentioned above was summed up after the implementation of the conversion factors.

Indoor environment of buildings Indoor air temperatures of two buildings are shown as an example for both, summer and winter seasons for a week time. In both cases the indoor air temperatures stay within the limits described in page 20 throughout the whole year. As all building simulations were performed under the same conditions it was assumed that all the resting buildings fulfil thermal comfort requirements similarly.

Figure 20: Air temperatures in building R1 (rectangular shape) from 14th to 20th of July (summer season)

Mon Tue Wed Thu Fri Sat Sun Mon

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(°C)

Date: Mon 14/Jul to Sun 20/Jul

Air temperature: 51 rooms (r 1.aps)

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Figure 21: Air temperatures in building R1 (rectangular) from 6th to 12th of January (winter season)

Figure 22: Air temperatures in building R12 (rectangular shape) with orientation at 90° from 14th to 20th of July (summer season)

It can be noticed both in Figure 20 and Figure 22 that temperatures exceed the limit of 26 °C, but this is only during unoccupied hours. As the requirements in unoccupied hours were set lower, the cooling system was not required. Similarly, the heating system was only used for keeping the building temperatures at a minimum of 16 °C during unoccupied hours.


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Figure 23: Air temperatures in building R12 (rectangular shape) with orientation at 90° from 6th to 12th of January (winter season)

Indoor air quality is other of the main concerns of the indoor climate of buildings. As it can be seen in Figure 24 the minimum ventilation rate set by EN15251 appears to be sufficient to keep the CO2 concentration in the buildings below 620ppm throughout all the year. Since the rest of the buildings have equal occupancy and ventilation rate, similar CO2 concentrations are expected. Consequently it can be concluded that the indoor air quality of the buildings fulfil with the requirements described in chapter 4.

Figure 24: CO2 concentration in a room of rectangular building R1 throughout the year


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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

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)

Date: Wed 01/Jan to Wed 31/Dec

Room CO2 concentration: Room 001 (r 1.aps)

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There is not a widely known objective formula for judging an acceptable level of daylight penetration in the buildings. BR10 recommends a 2 % daylight factor at the working places, but as lettable area is assumed to be 88 % of the floor area in office buildings, it is not clear if whether the entire floor area should fulfil with the recommendation or not. Table 20 summarizes the percentage of the lettable area with daylight factor above 2 %. Standards are not objective with the requirements with respect of daylight yet, so most likely the client should state how much the percentage of the working places that require daylight is.

Table 20: Percentage of lettable area with daylight factor above 2 % for all buildings

Building shape Percentage of lettable area with daylight factor above 2 % (%)

1 2 3 4 5 6 7 8 9 10 11 12

Quadratic 32 43 51 58 Rectangular 37 48 58 65 43 55 66 73 46 62 72 82

L-shaped 40 52 61 69 46 59 69 76 50 65 76 84 T-shaped 44 55 65 73 48 61 69 80 53 68 79 86 O-shaped 55 65 70 73 71 81 84 85 66 79 92 94 Q-shaped 62 71 77 78 70 78 80 82 72 81 83 86 U-shaped 49 61 70 79 52 70 80 85 56 70 77 81

H-shaped 53 66 75 80 59 74 84 88 66 76 82 85 * Calculation method is described in Appendix 5. A table with the percentage of the total floor area with daylight factor above 2% can also be found in the appendix.

Daylight penetration does not necessarily need to be higher when increasing glazing area of the building. As it can be seen in Figure 25, some buildings with glazing area between 2,500 m2 and 3,500 m2 get the same daylight as others with less glazing. This is due the self-shading effect that some buildings experience. Example of such buildings is shown in Figure 26.

Figure 25: Daylight penetration versus glazing area for all buildings

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[%]

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Rectangular Quadratic

L-shape T-shape

O-shape Q-shape

U-shape H-shape



Figure 26: Example of buildings with reduced daylight access in low floors

The energy performance of the buildings is shown in the following tables. This information box describes how to read the results in such tables. As daylight showed such strong impact on the energy consumption of office buildings, daylight penetration is also shown in these tables although it is not an energy performance indicator. Find an example below:

3 st

orey

s

R1 DP: 32%

0º 90º

H: 8.83 8.66 C: 3.07 2.47 V: 15.17 14.96 L: 35.98 35.58 D: 5.00 5.00 E: 68.04 66.67

Information box 3

Graphic representation of the building

Orientation (azimuth)

Daylight penetration (floor area with DF above 2%) (%)

Reference name of the building

Amount of storeys

End energy uses:

H: Heating energy demand (kWh/m2 year) C: Cooling energy demand (kWh/m2 year) V: Ventilation energy demand (kWh/m2 year) L: Lighting energy demand (kWh/m2 year) D: Domestic hot water energy demand (kWh/m2 year) E: Total energy consumption (kWh/m2 year)

Energy use (kWh/m2 year)

O-shape

Q-shape H-shape

U-shape

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Table 21: Energy performance of quadratic buildings

3 st

orey

s Squa

red

1

DP: 29 %

0º

H: 8.34 C: 2.70 V: 15.04 L: 38.38 D: 5.00 E: 69.46

5 st

orey

s Squa

red

2

DP: 37 %

0º

H: 8.36 C: 3.54 V: 15.33 L: 32.57 D: 5.00 E: 64.80

7 st

orey

s Squa

red

3

DP: 45 %

0º

H: 8.86 C: 4.48 V: 15.66 L: 28.05 D: 5.00 E: 62.05

9 st

orey

s Squa

red

4

DP: 51 %

0º

H: 9.37 C: 5.51 V: 16.02 L: 24.45 D: 5.00 E: 60.35



Table 22: Energy performance of rectangular buildings

3 st

orey

s

R1 DP: 33 %

R5 DP: 37 %

R9 DP: 40 %

0º 90º 0º 90º 0º 90º

H: 8.83 8.66 H: 9.45 9.16 H: 9.50 9.10 C: 3.07 2.47 C: 3.44 2.45 C: 3.83 2.42 V: 15.17 14.96 V: 15.30 14.95 V: 15.43 14.94 L: 35.98 35.58 L: 33.02 32.52 L: 31.50 30.85 D: 5.00 5.00 D: 5.00 5.00 D: 5.00 5.00 E: 68.04 66.67 E: 66.21 64.08 E: 65.26 62.31

5 st

orey

s

R2 DP: 42 %

R6 DP: 48 %

R10 DP: 54 %

0º 90º 0º 90º 0º 90º


7 st

orey

s

R3 DP: 51 %

R7 DP: 58 %

R11 DP: 63 %

0º 90º 0º 90º 0º 90º


9 st

orey

s

R4 DP: 55 %

R8 DP: 64 %

R12 DP: 72 %

0º 90º 0º 90º 0º 90º


0° 90°

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Table 23: Energy performance of L-shape buildings

3 st

orey

s

L1 DP: 35 %

L5 DP: 40 %

L9 DP: 44 %

0º 90º 180º 270º 0º 90º 180º 270º 0º 90º 180º 270º

H: 9.25 9.28 9.26 9.27 H: 9.85 9.80 9.90 9.77 H: 10.39 10.23 10.48 10.23 C: 2.85 2.81 2.83 2.85 C: 3.25 2.77 3.24 2.82 C: 3.64 2.82 3.57 2.80 V: 15.09 15.08 15.08 15.09 V: 15.23 15.06 15.23 15.08 V: 15.37 15.08 15.34 15.07 L: 33.97 33.93 33.95 33.94 L: 31.36 30.85 31.27 31.14 L: 29.21 28.61 28.38 28.16 D: 5.00 5.00 5.00 5.00 D: 5.00 5.00 5.00 5.00 D: 5.00 5.00 5.00 5.00 E: 66.15 66.09 66.13 66.15 E: 64.68 63.48 64.63 63.80 E: 63.62 61.74 62.77 61.26

5 st

orey

s

L2 DP: 46 %

L6 DP: 52 %

L10 DP: 57 %

0º 90º 180º 270º 0º 90º 180º 270º 0º 90º 180º 270º


7 st

orey

s

L3 DP: 54 %

L7 DP: 61 %

L11 DP: 67 %

0º 90º 180º 270º 0º 90º 180º 270º 0º 90º 180º 270º


9 st

orey

s

L4 DP: 61 %

L8 DP: 67 %

L12 DP: 74 %

0º 90º 180º 270º 0º 90º 180º 270º 0º 90º 180º 270º


0°Type equation here.






Table 24: Energy performance of T-shape buildings

3 st

orey

s

T1 DP: 38 %

T5 DP: 42 %

T9 DP: 47 %

0º 90º 180º 270º 0º 90º 180º 270º 0º 90º 180º 270º


5 st

orey

s

T2 DP: 49 %

T6 DP: 54 %

T10 DP: 60 %

0º 90º 180º 270º 0º 90º 180º 270º 0º 90º 180º 270º


7 st

orey

s

T3 DP: 57 %

T7 DP: 60 %

T11 DP: 70 %

0º 90º 180º 270º 0º 90º 180º 270º 0º 90º 180º 270º


9 st

orey

s

T4 DP: 64 %

T8 DP: 71 %

T12 DP: 75 %

0º 90º 180º 270º 0º 90º 180º 270º 0º 90º 180º 270º


0° 90°

180° 270°

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Table 25: Energy performance of U-shape buildings

3 st

orey

s

U1 DP: 43 %

U5 DP: 46 %

U9 DP: 49 %

0º 90º 180º 270º 0º 90º 180º 270º 0º 90º 180º 270º


5 st

orey

s

U2 DP: 54 %

U6 DP: 62 %

U10 DP: 61 %

0º 90º 180º 270º 0º 90º 180º 270º 0º 90º 180º 270º


7 st

orey

s

U3 DP: 61 %

U7 DP: 71 %

U11 DP: 68 %

0º 90º 180º 270º 0º 90º 180º 270º 0º 90º 180º 270º


9 st

orey

s

U4 DP: 69 %

U8 DP: 75 %

U12 DP: 71 %

0º 90º 180º 270º 0º 90º 180º 270º 0º 90º 180º 270º


0°

90° 180°

270°



Table 26: Energy performance of H-shape of buildings

3 st

orey

s

H1 DP: 46 %

H5 DP: 52 %

H9 DP: 58 %

0º 90º 0º 90º 0º 90º


5 st

orey

s

H2 DP: 58 %

H6 DP: 65 %

H10 DP: 67 %

0º 90º 0º 90º 0º 90º


7 st

orey

s

H3 DP: 66 %

H7 DP: 74 %

H11 DP: 72 %

0º 90º 0º 90º 0º 90º


9 st

orey

s

H4 DP: 70 %

H8 DP: 77 %

H12 DP: 75 %

0º 90º 0º 90º 0º 90º


0° 90°

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Table 27: Energy performance of quadratic shape of buildings with patio

A=3B A=4B A=5B

3 st

orey

s

O1 DP: 48 %

05 DP: 63 %

O9 DP: 58 %

0º 0º 0º

H: 11.04 H: 12.87 H: 13.27 C: 2.88 C: 3.50 C: 4.61 V: 15.10 V: 15.31 V: 15.70 L: 26.31 L: 18.64 L: 20.99 D: 5.00 D: 5.00 D: 5.00 E: 60.33 E: 55.33 E: 59.58

5 st

orey

s

O2 DP: 57 %

O6 DP: 71 %

O10 DP: 70 %

0º 0º 0º


7 st

orey

s

O3 DP: 61 %

O7 DP: 74 %

O11 DP: 81 %

0º 0º 0º


9 st

orey

s

O4 DP: 64 %

O8 DP: 75 %

O12 DP: 82 %

0º 0º 0º




Table 28: Energy performance of rectangular buildings with patio

L=2W L=3W L=4W

3 st

orey

s

Q1 DP: 54 %

Q5 DP: 62 %

Q9 DP: 64 %

0º 90º 0º 90º 0º 90º


5 st

orey

s

Q2 DP: 63 %

Q6 DP: 68 %

Q10 DP: 71 %

0º 90º 0º 90º 0º 90º


7 st

orey

s

Q3 DP: 67 %

Q7 DP: 70 %

Q11 DP: 73 %

0º 90º 0º 90º 0º 90º


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orey

s

Q4 DP: 68 %

Q8 DP: 72 %

Q12 DP: 75 %

0º 90º 0º 90º 0º 90º


0° 90°

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6.2. Relative compactness Results of end energy uses were analysed and sorted with regard of the building relative compactness. As it can be seen in Figure 27 below, none of the graphs shows a clear correlation between the building relative compactness and the energy uses. This happened due to the variation of the glazing area in the buildings, as despite the WWR was constant for all buildings the total glazing area in each building was different. Therefore, the glazing area overlapped the impact of the relative compactness.

It is remarkable however that similar tendency can be seen for cooling and ventilation energy demand. As mentioned previously, the energy use for ventilation is calculated summing up two values: a constant energy demand based on the air exchange per floor area (equal for all buildings) and a variable energy demand determined by the cooling load of each building. This variable energy demand is responsible for similar tendency in the plots. Note that the scale of the energy use is different for each graph.

Figure 27: Heating, cooling, ventilation, lighting and total energy consumption of buildings versus relative

compactness

R² = 0.0906

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Similarly, it is noticeable that the graphs showing lighting and total energy demand respectively show plots that look alike. This leads to think that lighting and total energy demands are strongly related.

6.3. Glazing area As the exterior of buildings is not only defined by the geometry but also its external openings, glazing area can be an important factor influencing the energy performance of buildings. In this case the energy use for the building services and total energy demand are plotted with respect to the glazing area of the buildings. See Figure 28.

The impact of the glazing area on the energy use for heating in office buildings seems to be straight forward independently of the orientation due to the fairly high correlation of the results (R2=0.8383). For cooling and ventilation energy use however, the correlation drops significantly (R2 = 0.6287 and R2=0.6294 respectively) showing a clear impact of the building orientation. As buildings from the investigation have different glazing surface area heading all four orientations, the solar gain through the external openings of the building is different at each orientation, and consequently the cooling load suffers a significant variation. Since the results for different orientations of the same building are included in the plots, the unclear tendency of the cooling and ventilation energy demand is reasonable. If all buildings had identical glazing area at each orientation higher correlations would be expected for both heating and cooling energy use.

As larger window area lets more daylight get into the office space, less artificial lighting is required to keep appropriate lighting levels and therefore the energy demand for lighting is reduced. The plot shown in Figure 28 shows this effect with a clear tendency (correlation R2 = 0.9583). However, the interrelation is not linear when the glazing area of the buildings gets about 2,500m2 – 3,000m2. This can be explained because buildings with such glazing area in this investigation correspond to buildings shown in Figure 26 (page 55). As daylight access is limited in some parts of the buildings, the increased glazing area does not correspond with proportional energy savings on artificial lighting. Therefore, large window areas do not always result in lower lighting demand, but only when the building design is optimized for a maximum daylight penetration. In fact, Figure 29 proves that if buildings with limited daylight access are not considered, the interrelation of glazing area versus lighting energy demand is linear.

The building orientation does not seem to have a strong impact on lighting energy demand due most likely to the typical overcast sky in Denmark; which leads to similar illuminance levels in the interiors independently of the orientation.

The graph at the bottom of Figure 28 represents the total energy demand with respect to the glazing area of the buildings. As lighting seems to be the predominant energy demand among the building services, the total energy demand is closely related to it. Note that plots look very much alike with a deviation caused by the cooling demand. Hence, buildings with larger window areas and therefore with low artificial lighting energy demand will be performing best as far as the windows allow sufficient daylight penetration. See that interrelation of the trend-line of the total energy demand versus glazing area also gets linear and higher when buildings with limited daylight access are excluded.

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Figure 28: Heating, cooling, ventilation, lighting and total energy consumption of buildings versus glazing area. Results for different orientation of the buildings were included in the graphs.

Figure 29: Lighting energy demand and total energy demand versus glazing area when buildings with limited

daylight access are excluded

R² = 0.8383

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Heat

ing

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Ligh

ting

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Total energy demand



6.4. Impact of building relative compactness - A sharper analyses It is hard to conclude how large the impact of the relative compactness of buildings is with relation to the impact of the glazing area when both factors are so closely related. Looking at results analysed in 6.2 and 6.3 it was noticed that the glazing area of the building design has a stronger impact on the energy consumption than what the relative compactness does. However, a further investigation was carried out in order to find out the individual contribution of each factor on the building performance.

Rectangular buildings seemed to be the most appropriate for this analysis as the self-shading effect would be avoided, consequently lowering the risk of external factors influencing the results. Rectangular buildings presented in the building catalogue in page 28 were to be used for this study. However, the building aspect ratios of the buildings seemed to show a fairly limited range of building options, resulting in a study of relatively compact buildings only. Hence, these buildings were enlarged with 8 more shown in Appendix 6, which represent more extreme alternatives. In overall twenty rectangular buildings were used for this investigation. Two scenarios were used on the analyses. First, all the models were simulated with no glazing (WWR = 0) for orientations at 0° and 90° azimuth. Later, as these results would not be realistic enough representing actual performance of buildings as there are no windows in the models, the same buildings were simulated with similar glazing area and both cases were compared. Finally, the same investigation was carried out for low energy buildings with significant changes in material properties of building components and performance of building services.

Daylight could not be measured accurately in these investigations as in the first case with no glazing, daylight would not enter into the buildings; and in the second as the total glazing area was to be the same in all buildings, the WWR from each building alternative was modified. Since the room division method used for measuring of daylight described in 5.4.1 was developed for buildings with uniform distribution of windows and constant window to wall ratio (WWR = 50%), the current room division of the models would not lead to realistic measurement of daylight in the buildings. Therefore, as daylight would not be measured, Radiance link was not used and it was assumed that the lighting system would provide the full lighting needs in the buildings.

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6.4.1. Impact of building relative compactness on heating energy demand

According to the results shown in Figure 32 the heating energy demand of the most and least compact alternatives of the study (R4 and R17 respectively, see Figure 30) increased from 3.8 kWh/m2 to 6.5 kWh/m2 (difference of 2.7 kWh/m2) regardless the orientation. Therefore the heating demand rose about 60 % due to the additional heat loss through the building envelope. This seems considerably high although it only represents 3.8 % of the acceptable total annual energy consumption of buildings according to current standards BR2010, which is about 71 kWh/m2 (14).

RC: 0.96 RC: 0.55 Building envelope: 6,037 m2 Building envelope: 10,570 m2

Figure 30: Building dimensions of R4 (left) and R17 (right)

When the models were simulated with equal glazing area (see example in Figure 31) similar relative difference was found on the heating energy demand of the buildings. However, the absolute heating demand was higher due to the higher heat transmission coefficient of the glazing (U-value) in relation to the wall. This time, the orientation of the building does have an impact on the heating energy demand. As all building alternatives have equal glazing area the impact of the orientation is independent of the building shape. Hence, the relative improvement on the heating energy demand when setting the building on the optimal orientation is similar for all buildings (see Figure 33).

RC: 0.96 RC: 0.55

Building envelope: 6,037 m2 Building envelope: 10,570 m2 Glazing area: 726 m2 Glazing area: 726 m2

Figure 31: R4 (left) and R17 (right) with equal glazing area

27m

23.5m 47m

200m

16m

9m

27m

23.5m 47m

9m

16m

200m



Figure 32: Heating energy demand of ‘standard buildings’ with no glazing

Figure 33: Heating energy demand of ‘standard buildings’ with equal glazing area

R² = 0.8968

R² = 0.9036

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Heating buildings at 0°Heating buildings at 90°Linear (Heating buildings at 0° )Linear (Heating buildings at 90° )

R17

R4

R² = 0.88

R² = 0.92

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It can be noticed that several buildings with relative compactness between 0.60 – 0.70 are relatively out of the trend-line although the correlation of results is still considerably high. Figure 34 below shows the same values as Figure 32, but sorted by number of storeys. It seems that buildings with three storeys are more sensible to variation of relative compactness. This tendency will be discussed in a later chapter.

Figure 34: Heating energy demand for (rectangular) standard buildings with no glazing

6.4.2. Impact of building relative compactness on cooling energy demand

It was noticed from previous chapters that the cooling energy demand is increasing with the glazing area. Figure 35 shows the energy required for cooling in buildings with no glazing. The energy demand in this case is below 1 kWh/m2 per year for all buildings. This means that the cooling energy demand is indeed determined basically by glazing area and the contribution of the building relative compactness is minimal.

As soon as buildings were simulated with windows the cooling energy demand increased depending on the orientation. Figure 36 shows a slightly higher dependency of the energy use on the relative compactness, although since all building alternatives have equal glazing area similar cooling energy demand was expected for all cases. Two possible reasons could be causing such tendency: the building thermal inertia or the heat loss through the building envelope (more compact buildings imply smaller building envelope and consequently lower heat loss, concluding in higher cooling energy demand). This is discussed later, after the simulation of the models with more energy efficient parameters typical in low energy buildings.

R² = 0.9978 R² = 0.9935 R² = 0.9893

R² = 0.9835

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Figure 35: Cooling energy demand of ‘standard buildings’ with no glazing

Figure 36: Cooling energy demand of ‘standard buildings’ with equal glazing area

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Cooling buildings at 0°Cooling buildings at 90°Linear (Cooling buildings at 0° )Linear (Cooling buildings at 90° )

R² = 0.92

R² = 0.79

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6.4.3. Impact of relative compactness in low energy buildings

The same investigation described previously in sections 6.4.1 and 6.4.2 was carried out for low energy buildings. Assumptions used for both analyses can be compared in Appendix 7. According to results shown in Figure 37 and Figure 38 the variation in heating energy demand of the buildings investigated is below 1 kWh/m2 whether the building has windows or not. Moreover, the impact of the building orientation seems to be similar to the impact that it has on standard buildings: alternatives with the long axis towards east and west perform better due to the higher solar gain in winter season.

As expected, when the low energy buildings are simulated with no glazing (WWR = 0) the cooling energy demand is close to zero as it can be seen in Figure 39. In this case the solar gain is not accounted and the improved thermal resistance of the building envelope prevents the heat to get into the building more efficiently. On the other hand, due to the lower thermal transmittance of the external walls and typical air tightness (lower infiltration), the heat cannot get out of the building and consequently the cooling energy demand is expected to be higher when windows are considered in simulations. In fact, the cooling energy demand in low energy buildings is higher than in standard buildings. See Figure 40.

When comparing the heating demand of standard buildings and low energy buildings it was noticed that the curve is more gentle for the second case. That is due to the lower thermal transmittance of the building components that make insignificant variation on the heating energy demand of low energy buildings regardless the relative compactness. Lower cooling energy demand was also found for low energy buildings when the models were not counting with glazing. However, since cooling energy demand is mostly determined by solar gain, and the solar gain coefficient of windows was kept constant for both studies, results of cooling energy demand show similar plots for both standard and low energy buildings (Figure 35 and Figure 39). However, the improvement of the U-values of building components in low energy buildings only works against the cooling energy demand, making the heat loss through the building envelope more difficult and therefore increasing the cooling energy demand to higher levels than in standard buildings.

Finally, it is remarkable that the cooling energy demand tends to be slightly higher for more compact buildings (see Figure 36 and Figure 40). This effect will be discussed in a later chapter.



Figure 37: Heating energy demand of ‘low energy buildings’ with no glazing

Figure 38: Heating energy demand of ‘low energy buildings’ with equal glazing area

R² = 0.84

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Figure 39: Cooling energy demand of ‘low energy buildings’ with no glazing

Figure 40: Cooling energy demand of ‘low energy buildings’ with equal glazing area

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6.5. Building shape and orientation From the results of the energy performance of the office buildings shown in Table 21 - Table 28 it can be concluded that buildings with the long axis oriented towards East and West perform best independently of the building shape. For this orientation the solar gain is optimized in winter time due to the low sun-path that allows solar radiation into the building during most part of the day (see Figure 41). The solar gain in summer time is simultaneously minimized as the sun moves over the building hitting east and west orientations stronger than south.

Figure 41: Sun-path in summer and winter seasons

As an example, Figure 42 shows the solar gain of a building oriented in a North-South axis and Figure 43 shows it for East-West orientation of the same building. It is remarkable by comparing the plots that the solar gain is higher during winter (heating season) and lower in summer (cooling season) in the second case.

Figure 42: Solar gain throughout the year of R12 oriented in a North-South axis (0° azimuth)


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Figure 43: Solar gain throughout the year of R12 when oriented in East-West axis (90° azimuth)

It is obvious that the impact of the orientation on the energy consumption of the buildings is only noticeable when the building aspect ratio is different than 1; Hence, when the building shape is different than quadratic. The longer a building is with respect to its width (big aspect ratio), more remarkable becomes the improvement of the orientation. As it can be noticed in Figure 44 and Figure 45 below, this impact is stronger for cooling than for heating energy use. This seems reasonable since internal loads (people, equipment and lighting) are fairly high in office buildings and therefore the heating load is not the biggest issue. Consequently, building shapes with smallest glazing areas facing East and West, and simultaneously largest glazing area in the South will show the lowest heating and cooling energy demand.

Figure 44: Heating energy demand of rectangular buildings depending on the orientation


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Figure 45: Cooling energy demand for rectangular buildings depending on the orientation

With regard to other building services such as ventilation energy use similar behaviour is expected since it is strictly dependent on cooling energy demand. For the energy use of lighting however, the impact of the orientation that can be seen in Figure 46 was not expected. Daylight calculations are assumed to be measured with overcast sky, neglecting influences of clear sky or direct radiation (21). Hence, daylight penetration in the buildings should be equal independently of the orientation and consequently the lighting energy demand for both orientations should be similar. This tendency will be further discussed in a later chapter.

Figure 46: Lighting energy demand for rectangular buildings depending on the orientation

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6.6. Low energy buildings The models used for the investigation described in 6.4 were used one more time for a new analysis. This time they were simulated with a window to wall ratio (WWR = 50%) that would allow a reliable measurement of daylight (by using the methodology described in 5.4.1). The models were simulated as standard buildings first and as low energy buildings later, so both cases could be compared. Detail information on the energy performance of these buildings are shown in Appendix 8 and Appendix 9.

It was shown in previous chapters that buildings perform within the energy requirements of BR10. However for office buildings to be classified as 2015-low energy buildings they should perform within a maximum total energy consumption of 41 kWh/m2 year (14). Figure 47 shows the total energy demand for rectangular buildings versus the glazing area. It is noticed that most of the buildings oriented with their long axis towards north and south do not fulfil energy requirements mentioned above. On the other hand, the energy demand of majority of east-west oriented buildings is below this limit. Moreover, the figure shows that the total energy demand does not decrease with the glazing area as it used to happen for standard buildings. In fact the opposite effect can be seen for north-south oriented buildings. Cooling and ventilation are closely related as they are determined by the tendency of overheating. As buildings receive more daylight due to the increased windows the lighting energy demand decreases significantly, but due to the excessive solar gain in summer, cooling and ventilation energy demand increase in a larger proportion. This effect is apparently much stronger in buildings oriented with the long axis towards north and south. Such trend can be seen in the figure below.

Figure 47: Total energy demand of low energy buildings (rectangular shape) with regard of the orientation

Figure 48 shows the contribution of each end use of a low energy building. It is remarkable that the ventilation (share that is determined by the cooling) and the cooling energy demand together are higher than the lighting. Therefore their influence on the total energy

R12

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consumption is higher in most cases. In buildings such as R1, with small glazing area and therefore very low daylight penetration, lighting energy demand may still prevail.

Figure 48: Contribution of each end use on total energy demand for R12 low energy building oriented east-west

Figure 49 and Figure 50 show the total energy demand broken down into energy uses for standard and low energy buildings respectively. It can be noticed that the heating and lighting energy demand decreased significantly in low energy buildings. The improvement of the thermal resistance of the building envelope is responsible for such reduction in heating energy demand. A more energy efficient lighting system was used for simulations of low energy buildings. Consequently, it seems reasonable to see lower energy consumption for this end use. On the contrary, cooling energy demand increased significantly due to the better insulated and tighter building envelope, which prevents the heat loss (beneficial in summer). Although the overall energy demand of the ventilation system decreased due to the lower specific fan power (SFP), the variable contribution determined by the cooling energy use increased just as the cooling energy demand.

Figure 49: Energy use by end use for standard buildings with East-West orientation

Heating; 1.79

Cooling; 10.49

Ventilation (cooling); 3.67

Lighting; 9.06

Ventilation ; 8.8

DHW; 5.00

Fixed contributors

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Figure 50: Energy use by end use for low energy buildings with orientation East-West

As overheating in buildings became such an issue for low energy buildings, the cooling load of a standard building and a low energy building were analysed and compared in Figure 52 and Figure 52. It is easily remarkable that the cooling energy demand for low energy buildings is considerably higher. In addition, the cooling season seems longer in low energy buildings. In any case, the excessive solar gain appears to be the reason of high cooling energy demand in the buildings. Solar gain and daylight penetration are both determined by the windows: For large windows daylight brings excellent energy savings of lighting energy demand, and simultaneously high cooling energy use due to the large solar gain. For small windows instead, the limited access of daylight results in high lighting energy demand although the cooling is lower. The first option seems more attractive if the excessive solar gain can be cut off in summer. Then, overheating due to solar gain had to be prevented in such way that daylight penetration could be still available in the building throughout the entire year. Multiple shading systems are currently used for avoiding direct solar radiation. External shading systems are most efficient for avoiding overheating as the heat is radiated before it gets in the building. The use of overhangs might be also beneficial, as they let low sun radiation (in winter) but prevents from direct radiation in summer (21). Figure 53 shows the solar gain and cooling load of a low energy building when an external shading system is used. The solar gain is prevented during the warmest time period of the year resulting in considerably lower cooling energy demand. Since the investigation of solar shading systems is out of the boundaries of this project, results from this simulation were only used as an example. However, an optimized shading system with an appropriate control strategy could probably prevent overheating also in April and October, when it seems that some cooling was also required.

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eman

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] R1 R2R3 R4R5 R6R7 R8R9 R10R11 R12R13 R14R15 R16R17 R18R19 R20



Figure 51: Solar gain and cooling load for R12 standard building oriented east-west

Figure 52: Solar gain and cooling load for R12 low energy building oriented east-west


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Figure 53: Solar gain and cooling load for R12 low energy building oriented east-west when using external solar

shadings

Finally, the energy demand of a standard building, a low energy building and a low energy building with external shading systems from the previous example are compared in Figure 54. It is assumed that once the cooling energy demand can be controlled by use of shading systems, the contribution of the end energy uses to the total energy consumption appears similar to the contributions of the standard buildings. Letting basically the total energy consumption of the office buildings be determined by the ventilation and lighting energy demand.

Figure 54: Energy demand by end use of R12 under different conditions


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7. Discussion In this project three main issues have been addressed regarding the office building shape; the impact of the relative compactness, glazing area and orientation of the buildings with respect to energy performance and indoor climate. Existing office buildings were used as starting point for the selection of realistic building shapes. Alternatives had different relative compactness but identical window to wall ratio (WWR = 50 %). Windows were evenly distributed along the facades. The models that were to be investigated with their implementation in IES<VE> resulted in a wide variety of buildings with different forms, heights and total glazing areas.

Minimum insulation requirements according to BR10 were assumed for the investigation of the building shapes. Energy performance of building services was also set according to the minimum requirements of Danish Building Regulations; assuming that this was the worst case scenario. Results from IES<VE> suggest that despite using poor insulation and building services, all building alternatives performed with a total energy demand below 71 kWh/m2 and indoor environment Class II.

Through the analysis of the results it was found that the glazing area had a great impact on the energy performance of buildings, whereas the influence of relative compactness could be hardly seen as it was overlapped with the impact of the glazing. The variation on relative compactness from a building shape to another implies a change on the building envelope area. As the window to wall ratio was kept constant for all buildings the total glazing area would be different in each case. If the contribution of the relative compactness was to be investigated, the contribution of the glazing area had to be isolated.

Relative compactness The building relative compactness is basically the ratio between the building envelope area and its volume. Multiple publications throughout last decades have concluded that building shapes similar to the cubic form (RC = 1) result in the lowest heating loads in cold climates (11). Agreement with such fact was not found in this study until the impact of the relative compactness was investigated individually. Two investigations were carried out; first twenty rectangular buildings were analysed with no glazing (WWR = 0 %). Later, windows were added but with a constant window to floor area, so that the impact of the glazing was equivalent in all buildings. The heating load was found to increase inversely proportional to the building envelope area. Although this effect still exists in well insulated buildings, the influence was found to be a lot smoother as the relative difference on the heating energy demand for the buildings was below 1 kWh/m2. Moreover, this tendency was found to be slightly steeper for buildings with 3 storey buildings. These are likely to have a stronger increase on the heating energy demand for a variation on the relative compactness. The large roof area compared to the wall area is characteristic of such buildings. As warm air tends to rise upwards due to its lower density, the taller is the building, the longest time the heat stays within the building envelope and therefore the lower is the heating energy demand.

Despite being in a cold climate, the excessive solar gain in summer may cause overheating in the interior of the office buildings in Denmark. As the nature of the overheating is the solar gain, and this gets through the windows it was not expected that the relative compactness would have an impact on the cooling load. However, Figure 36 shows a smooth increase of the

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cooling energy demand as the relative compactness increases. The same effect can be seen in Figure 40 for low energy buildings. As the tendency occurs similarly in both cases the air tightness or thermal transmittance of the building envelope could not be reason of such effect. Instead, the thermal inertia of the buildings seems to be responsible for such behaviour. External walls of the office buildings are internally insulated and therefore the thermal mass of the walls are not as relevant as the thermal mass of floors and ceilings (21). Consequently the larger floor area of the building with respect to the external wall area is, the higher the impact of the thermal mass. Therefore, although all buildings of the study had equal glazing area and solar gain, more heat is stored in constructions of compact buildings leading to higher cooling energy demand. The maximum relative variation of the cooling energy demand among the buildings investigated was below 1 kWh/m2. This was assumed not to be relevant enough for a further analysis in the present work, as anyway the thermal inertia of buildings is more related to the specific material properties rather than building shape itself. Nevertheless, the tendency was clearly identified although the glazing area utilized in the analysis was fairly low (WFA = 7%), therefore implying that the impact of thermal inertia could be higher when the glazing area would allow higher sun radiation into the buildings. With no doubt buildings with larger glazing areas showed a higher cooling energy demand.

Glazing area The relation with glazing area and energy demand was found for all energy end uses. The thermal transmittance of the windows (U-value: 1.8 W/m2K) is higher than other surfaces such as external walls (U-value: 0.3 W/m2K). It is therefore expected that buildings with large window area will have a higher heating energy demand due to the heat loss through the windows. The same effect can be expected in cooling energy demand as larger windows let more solar radiation into the building. However, the cooling energy demand seemed to vary for the same building when the orientation of the glazing was changed.

The larger the glazing area of the building with relation to its volume is, the higher penetration of daylight is achieved and consequently the lower energy demand for lighting was found. This tendency appeared to be independent of the orientation of the building as the simulation tool measures daylight with overcast sky (diffuse radiation). However, the lighting energy demand does not linearly decrease for larger glazing areas. Exceptions were found on buildings with difficult daylight access due to the specific building form (E.g. quadratic building shapes with a patio in the interior). The additional glazing area does not correspond with proportional energy savings on lighting energy demand but only with a higher heating load.

Building shape and orientation The solar radiation over the building facades differs with the sun position along the year. Solar gain is desired in winter time in order to reduce heating load. However, it should be avoided in summer when it may cause overheat. Rectangular building shapes were found to be the most appropriate for optimizing this effect. If they are oriented with the long axis in east-west, the exposed facades to the solar radiation in summer are smaller than the south façade, which maximizes the solar gain in wintertime. Simultaneously, results suggest that the longer a building is with respect to its width, the better is the solar passive gain throughout the year if this orientation is used.



Low energy buildings When the building envelope is better insulated and more air tight, the heat transfer with the exterior climate condition gets more complicated. The energy performance of rectangular buildings was investigated with the implementation of such properties to the building components. The heating energy demand of the buildings was therefore greatly reduced for all buildings; minimizing the relative difference on heating demand for all building alternatives, despite the enormous variation on building envelope area.

On the other hand, the cooling energy demand was found to be the main contributor to the total energy use. The excessive solar gain and the reduced heat loss through the building envelope were the main factors influencing such behaviour in this kind of buildings. Building orientation for solar protection is therefore of more importance in this kind of buildings.

An example of the energy performance of a rectangular building with the optimal orientation mentioned before was simulated with the use of an additional external shading system. The results showed that the cooling energy use is reduced significantly. Making ventilation and lighting energy demand be main energy users. It is therefore assumed that if low energy buildings are well prevented from overheating, a good building design that enhances daylight penetration will result in great energy savings in lighting energy demand and consequently in total energy consumption.

Interrelation among end energy uses and total energy demand The energy performance of the buildings changes depending on the building shape. It was shown that the total energy demand of the office buildings is directly related to the lighting energy demand; if the building design is poor, lighting can rise up to 50 % of the total energy consumption.

Heating and cooling energy demand increase simultaneously as the glazing area gets larger. Ventilation energy demand showed similar tendency than cooling energy demand, as the mechanical ventilation was the mechanism used to cool down the air temperatures within the building. These three energy uses increased for increasing glazing area, whereas the lighting energy demand decreased in the invert direction. The total energy demand as the sum up of the mentioned end energy uses had similar tendency as the lighting energy demand. However, this tendency was interrupted as soon as the daylight access was not ensured in the entire glazing area; being the building shape responsible for such an exception.

Heating versus lighting energy demand It was noticed that the tendency of the energy savings from lighting to reduce the total energy consumption was not limitless. As glazing area is enlarged lighting energy demand decreases while heating energy demand increases due to a higher heat loss. When this heat loss implies a heating energy demand that the savings on lighting cannot overcome, the tendency of the total energy consumption changes its direction upwards (Figure 55).

As heat loss through the building envelope for low energy buildings is lower, this limitation is assumed to be moved further away to larger glazing areas. Consequently, the potential energy savings due to the use of daylight are even higher in this case. As shown in Figure 56, the tendency of the total energy consumption does not follow the tendency of the lighting

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demand. This is due to the large influence of the cooling energy demand. However, it is assumed that if overheating is solved by means of solar protection, the total energy demand will also follow the tendency of the lighting energy demand.

The optimized glazing area for having the lowest total energy consumption with a balance between heat loss and lighting energy savings is found to be about 2,200 m2 for standard buildings. The building with the closest glazing area is R15 (2,483 m2). For more accurate results on the optimal glazing area, the simplification of the models (5.4.1) should be revised and the deviation allowed for measuring illuminance levels should be decreased. For low energy buildings it seems that a glazing area of about 3,300 m2 would be the optimal. Nevertheless, there is only one building with such glazing area and therefore conclusions based on this would be unsound. Further investigation of low energy buildings with solar shading protection and larger glazing areas would provide more reliable conclusions. As solar gain in winter depends on the orientation, buildings with the long axis oriented east-west will be allowed to have slightly higher glazing area.

Figure 55: Tendency of heating, lighting and total energy demand in ‘standard buildings’

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Figure 56: Tendency of heating, lighting and total energy demand in ‘low energy buildings’

Method of the investigation The procedure followed to carry out this investigation showed to be satisfactory to get an idea of how the office building shape has an impact on the energy consumption and indoor environment of buildings. However, it is not the only way that the investigation could have been carried out. For example, the use of a constant window to floor area (WFA) instead of using a constant window to wall ratio (WWR) would have let to a quicker results regarding the relative compactness of buildings. Nevertheless, the impact of daylight on the total energy consumption would have been perhaps more difficult to evidence. Even a third path could have been taken if a certain daylight penetration would have been aimed for every building alternative, for a further comparison of the energy performance of the buildings. In any case, as daylight measurements of complex and detailed models require long simulation time, simplifications of the models were required, and the easiest approach for getting such simplifications was driven by using a constant WWR.

Regarding the accuracy of daylight measurements, small variations were found in the lighting energy demand of the buildings when comparing different orientations. This implies a certain error in the daylight measurements that can be assigned to the deviation in the method applied. As it can be seen in the results, the deviation is always lower than 5 %, which was assumed to be acceptable. This error resulted on inaccurate prediction of the optimal glazing area (Figure 55), but did not influence on finding a conclusion. Therefore the result of applying the simplifications in the models resulted to be satisfactory.

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8. Conclusion The present study has addressed the influence of the office building shape with respect of energy consumption and indoor climate. Building compactness, transparent area and orientation all contribute to the total energy consumption in buildings.

Compact building shapes have been recommended during decades for cold climates. It was shown that the energy used by heating is reduced if the building shape is compact; however this increases the tendency of overheating in summertime and reduces the daylight availability, resulting in a higher lighting energy demand. For the total energy demand of the building to be the least, the interplay of the contributors must be analysed.

This study supports that heating, cooling and ventilation energy demand are directly related to the shape and glazing area of the buildings. Larger glazing areas result in higher heating, cooling and ventilation energy demand; whereas, the increased daylight penetration results in a lighting energy reduction. When looking at the overall energy consumption, the lighting energy savings overcomes with the increased heat loss (in winter) and solar gain (in summer) through the windows. Therefore, office buildings designed with an approach of daylight optimization were found to perform more efficiently regardless the building shape or orientation. Nonetheless, an optimal orientation and building shape may increase solar passive gain in heating season while minimizing the solar gain it in cooling season. Rectangular buildings are ideal for optimizing such effect. Figure 57 shows building shape R15, which was found to perform the best among the buildings of the study. The glazing area is the closest to the optimal for balancing heat loss and lighting energy savings. This building results in an energy consumption of 49.9 kWh/m2 year. Compared it to the most compact building shape, Quadratic 4, the heating energy demand was increased by 37 % (from 9.37 to 12.91 kWh/m2), while the lighting energy demand was reduced by 57 % (from 24.45 to 10.56 kWh/m2). In overall, the total energy consumption was reduced by 17 % by optimizing the use of daylight through the building shape.

Figure 57: Building R 15 on the optimal orientation for passive solar gain

Low energy buildings may perform differently if solar protection is not carefully designed. The heat transfer through the building envelope is minimal in this type of buildings. It was noticed

W S

E



that heating energy demand decreases to similar levels regardless the relative compactness or glazing area of the alternatives investigated. However, the cooling demand prevails to other energy uses being the highest contributor to the total energy consumption. Hence, the prevention of overheating in low energy buildings is a key factor for getting close to zero energy buildings. It is assumed that if overheating issue is solved properly, ventilation and lighting energy demand will be the main contributors to the total energy consumption of low energy buildings. A further reduction of ventilation energy use by means of passive ventilation systems could make lighting energy demand the main contributor. Here, the building shape enhancing daylight into the building and reducing or even replacing artificial lighting would be critical for making possible to get into a zero energy office building by 2020.

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9. Future work There are several factors that were found to contribute on the energy performance of buildings but were not addressed in this project. The thermal inertia of buildings related to the relative compactness of buildings showed a clear impact for heating and cooling energy demand. The literature suggests that external walls are not as relevant as internal walls, surfaces or furniture in the interior. Due to the simplification of the models, such factors were not investigated in this project, but their implementation would let to a better understanding of its impact in future studies.

On the other hand, building shapes with a patio in the centre of the building envelope showed an interesting behaviour. They showed to be an exception of the trend that suggested lower total energy consumption for a higher daylight penetration in the buildings. Some of these buildings have a very good daylight access, but the heat loss through the patio did not result in direct decrease of the total energy consumption; due most likely to the additional heat loss. Different designs of the patio could solve this problem by two ways. Better reflectance of the surfaces, specially the ground would reflect daylight upwards resulting in a better and more efficient distribution of daylight in the rooms facing the patio. Second, if the patio was covered by a transparent envelope daylight would still come in while the heat loss could be lower in winter. The overheating in summer would probably need to be prevented by ventilation (natural ventilation preferably) as daylight would be still required. The implementation of such buildings in the present study would give an idea of how good are those solutions compared to an optimized rectangular building shape for solar passive gain through the year.

A further implementation of shading systems in this study would result in a better understanding to which level the overall building design is able to minimize energy consumption.

Internal gains such as people, equipment and artificial lighting can have a strong influence on heating and cooling energy demand within the same building. This, together with the occupant’s behaviour on individual controls of lighting, set point temperatures or use of shading devices can have an influence on the accurate prediction of the energy performance of buildings. Some of these factors could be investigated in order to predict how much will the energy consumption predicted with simulation programs such as IES<VE> divers from real energy consumption.



10. References 1. Commision, European. Climate Action. [Online] European Commision, October 2010. [Cited: 10 September 2011.] http://ec.europa.eu/clima/policies/brief/causes/index_en.htm.

2. European Commision. Energy Performance of Buildings Directive. www.epbd-ca.org. [Online] European Commision, 08 06 2010. [Cited: 23 07 2011.] http://www.epbd-ca.org/.

3. European Union. Implementing the Energy Performance of Buildings Directive. Brussels : s.n., 2011.

4. A review of buildings energy consumption information. Pérez-Lombard, Luis, Ortiz, José and Pout, Christine. Sevilla : ELSEVIER, 2007, Vol. Energy and Buildings 40.

5. Floor shape optimization for green building design. Wang, Weimin, Rivard, Hugues and Zmeureanu, Radu. Montreal : ELSEVIER, 2006, Vol. Advanced Engineering Informatics 20.

6. Kibert, Charles J. Sustainable Construction. Green building design and delivery. New Jersey : John Wiley & Sons, Inc., 2008.

7. The Effect of Healthy Workplaces on the Well-being. Bergs, John. Amersfoort : s.n., 2002.

8. A simplified analysis method to predict the impact of shape on annual energy use for office buildings. Ourghi, Ramzi, Al-Anzi, Adnan and Krarti, Moncef. 2006, ELSEVIER.

9. Building morphology, transparence and energy performance. Pessenlehner, Werner and Mahdavi, Ardeshir. s.l. : Eighth International IBSA Conference, 2003.

10. Design of buildings shape and energetic consumption. Depecker, P, et al., et al. s.l. : Building and Evnironment, 2001, Vol. 36.

11. Building form for cold climatic zones related to building envelope from heating energy conservation point of view. Koçlar Oral, Gül and Yilmaz, Zerrin. Istanbul : Energy and Buildings. Elsevier, 2002, Vol. 35.

12. Impact of building shape on thermal performance of office buildings in Kuwait. AlAnzi, Adnan, Seo, Donghyun and Krarti, Moncef. s.l. : Energy Conversion and Management. Elsevier, 2009, Vol. 50.

13. Aggerholm, Søren, Thomsen, Kirsten Engelund and Wittchen, Kim B. Implementation of The EPBD in Denmark. Aalborg : European Commission, 2010.

14. The Danish Ministry of Economic and Business Affairs. Building Regulations. Copenhagen : Danish Enterprise and Construction Authority, 2010.

15. EN 15251. Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics. s.l. : CEN (European Commitee of Standarization), 2007.

16. Earth System Research Laboratory, Global Monitoring Division. [Online] August 2011. [Cited: 12 09 2011.] http://www.esrl.noaa.gov/gmd/ccgg/trends/.

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17. Contrasting the capabilities of building energy performance simulation programs. Crawley, Drury B, et al., et al. Washington DC : Elsevier, 2006, Vol. Building and Environmental 43.

18. Crawley, Drury B., et al., et al. Contrasting the Capabilities of Building Energy Performance Simulation Programs. Washington DC : US Department of Energy, 2005.

19. Apache-Tables User guide. Integrated Environmental Solutions Limited. Glasgow : IES-VE, 2011.

20. Enck, H. Jay. Improved Energy Efficiency through Better Lighting Design. Buildings - Helping Facilities Professionals Make Smarter Decisions. [Online] 08 01 2007. [Cited: 30 07 2011.] http://www.buildings.com/ArticleDetails/tabid/3321/ArticleID/4939/Default.aspx.

21. Limited, Integrated Environmental Solutions. Radiance user guide. Glasgow : s.n., 2010.

22. Daylight in buildings (course held at Technical University of Denmark). Svendsen, Svend. Lyngby : DTU, 2010.

23. Thermal inertia in buildings. Sole, Josep. Madrid : URSA Insulation, S.A., 2008.

24. ROCKWOOL. Revolutionary insulation material made out of wool and aerogel. [Online] 2011. [Cited: 01 08 2011.] http://www.aerowool.nl/aerorock-id.

25. Petersen, Steffen. Building energy and technical services - Integrated design (Course material). Copenhagen : Danish Technical University, 2010.

26. Support, IES Technical. [email protected]. s.l. : Integrated Environmental Solutions Limited, 2009.



Appendix 1: Capabilities of Building Energy Simulation Programs

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Appendix 2: Examples of existing office buildings in Copenhagen For the investigation to be based on realistic office building shapes, some existing office buildings in Copenhagen area were analysed and used as a reference. Find below an image of the existing building on the left and a graphic representation of the building with external dimensions on the right for each example respectively.

Figure 58: Quadratic 9 storey office building is placed in Kalvebod Brygge 3, 1560 Copenhagen

Figure 59: 7 storey rectangular office building of about 19,000 m2 of total floor area; placed in Knippelsbrogade 4,

1400 Copenhagen

Figure 60: 6 storey office building reminding of T-shaped buildings, erected in Kalvebod Brygge 47, 1560 Copenhagen

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Figure 61: 5 storey rectangular office building shape with patio placed in Strandgade 3, 1402 Copenhagen

Figure 62: 6 storey connected rectangular buildings making up U-shaped building configuration in Nicolai Eigtveds Gade 20, 1402 Copenhagen



Appendix 3: Composition and thermal properties of building components

Table 29: Composition and thermal properties of building components of standard buildings

Building component Composition Thickness

(m) Conductivity

(W/mK) Resistance (m2K/W)

Roof

Stone chippings

Bitumen layer

Cast concrete

Glass-fibre quilt

Cavity

Ceiling tiles

0.01

0.005

0.15

0.17

0.10

0.01

0.96

0.50

1.13

0.04

0.056

0.17

External walls

Brickwork

Dense slab insulation (styrofoam)

Concrete block

Gypsum plastering

0.10

0.07

0.10

0.015

0.84

0.025

0.51

0.42

Ground floor

Clay

Brickwork

Cast concrete

Dense slab insulation (Styrofoam)

Chipboard

Synthetic carpet

0.75

0.25

0.10

0.085

0.025

0.01

1.41

0.84

1.13

0.025

0.15

0.06

Internal ceiling-floor

Synthetic carpet

Cast concrete (dense)

0.01

0.10

0.06

1.40

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Table 30: Composition and thermal properties of building components for low energy buildings

Building component Composition Thickness

(m) Conductivity

(W/mK) Resistance (m2K/W)

Roof

Stone chippings

Bitumen layer

Cast concrete

Aerowool*

Cavity

Ceiling tiles

0.01

0.005

0.15

0.17

0.10

0.01

0.96

0.50

1.13

0.019

0.056

0.17

External walls

Brickwork

Aerowool*

Concrete block

Gypsum plastering

0.10

0.17

0.10

0.015

0.84

0.019

0.51

0.42

Ground floor

Clay

Cast concrete

Dense slab insulation (Styrofoam)

Chipboard

Synthetic carpet

0.75

0.10

0.20

0.025

0.01

1.41

1.13

0.025

0.15

0.06

Internal ceiling-floor

Synthetic carpet

Cast concrete (dense)

0.01

0.10

0.06

1.40

*Aerowool is a recently developed insulation material from ROOCKWOOL with a very low thermal conductivity. (23)



Appendix 4: Reference lighting systems Luminaires used for general lighting make up a power density of 9.3W/m2 according to the information data sheet handed by the producer in this example:

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Task lighting can be provided with an individual lamp as the one presented below. A consumption of 20W can be assumed therefore for each task light. As the occupancy determine the amount of task lighting (1 task light/15m2 of floor area), a power density of 1.3W/m2 was assumed.



Lighting systems are usually more efficient in low energy buildings. Therefore luminaires providing similar illumination with a lower power density as the one shown below should be used for simulation of lighting energy in low energy buildings.

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Appendix 5: Simplified method for calculation of daylight penetration Radiance is the IES<VE> module responsible for measuring illuminance levels in the sensors and forward the data to Apache, where this data is processed and the energy use of the artificial lighting is calculated accurately. However, the illuminance data is not accessible to the user but is internally used by the multiple applications involved. If the daylight available in the buildings is to be found, new Radiance simulations must be performed for each room individually. This was quite inconvenient as the daylight available in the buildings was to be compared. A simplified method for the calculation of daylight penetration in the buildings was carried out.

The window to wall ratio (WWR) used for the study was kept constant to 50 % for all building shapes. As the position of the windows was also equal in all building alternatives, the daylight penetration (depth) was assumed to be equal regardless the orientation of the buildings. Figure 63 shows that a daylight factor of 2 % is reached to a depth of 4.5 m. This was multiplied by the perimeter of the building with such window area, resulting in an approximation of the floor area with daylight factor above 2 %. This simplified calculation could be used for simple building shapes such as quadratic, rectangular, L-shape and T-shapes. Other building shapes with factors that could be influencing the penetration of daylight such as self-shading effect were excluded. As a result, the total useful day-lit building area of the buildings was related to the lighting energy use with a very high correlation. This made possible to use a formula for calculating the useful day-lit building area for any kind of building within this study. Table 31 shows the percentage of the total building area with daylight above 2 %, calculated by using formula displayed on Figure 64.

Figure 63: Daylight penetration in office buildings. The green hatch represents the floor area with daylight factor above 2%



Figure 64: Useful daylight area versus lighting energy demand of buildings (excluding buildings that could be affected by self-shading effect)

Table 31: Percentage of total building floor area with daylight factor above 2 %

Building shape Percentage of lettable area with daylight factor above 2 % (%)

1 2 3 4 5 6 7 8 9 10 11 12

Quadratic 29 37 45 51 Rectangular 33 42 51 57 37 48 58 64 40 54 63 72

L-shaped 35 46 54 61 40 52 61 67 44 57 67 74 T-shaped 38 49 57 64 42 54 60 71 47 60 70 75 O-shaped 48 57 61 64 63 71 74 75 58 70 81 82 Q-shaped 54 63 67 68 62 68 70 72 64 71 73 75 U-shaped 43 54 61 69 46 62 71 75 49 61 68 71

H-shaped 46 58 66 70 52 65 74 77 58 67 72 75

y = 1.3856x2 - 251.4x + 10459 R² = 0.9781

0

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Appendix 6: Extra rectangular buildings for analyses of relative compactness

Table 32: New rectangular buildings with aspect ratio 1/8 and 1/12 representing extreme building alternatives

A=8B A=12B 3

stor

eys

R13

A=163.3m; B=20.4m; h=9m RC: 0.58

R17 A=200m; B=16.7m; h=9m

RC: 0.55

5 st

orey

s

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RC: 0.64

7 st

orey

s

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orey

s

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R20 A=115.4m; B=9.6m; h=27m

RC: 0.65



Appendix 7: Assumptions for standard and low energy buildings

Table 33: Assumptions for energy calculations of buildings with no glazing and equal glazing area

Standard buildings Low energy buildings

U-values of building components

External walls

Roof

Ground floor

Windows

0.30 [W/m2K]

0.20 [W/m2K]

0.20 [W/m2K]

1.80 [W/m2K]

0.10 [W/m2K]

0.10 [W/m2K]

0.10 [W/m2K]

0.80 [W/m2K]

Internal gains

General lighting

Task lighting

Equipment

People

9.3 [W/m2]

1.3 [W/m2]

6.0 [W/m2]

6.0 [W/m2]

4.5 [W/m2]

1.3 [W/m2]

6.0 [W/m2]

6.0 [W/m2]

Air exchanges

Ventilation rate

Infiltration

1.20 [l/s m2]

0.12 [l/s m2]

1.20 [l/s m2]

0.07 [l/s m2]

Apache system

x Heating system:

Seasonal efficiency

x Cooling system:

COP

x Mechanical ventilation system:

Heat recovery efficiency

SFP of ventilation system

1 [-]

2.5 [-]

0.70 [-]

1.8 [W/(l/s)]

1 [-]

2.5 [-]

0.85 [-]

1.25 [W/(l/s)]

of 111

Appendix 8: Energy performance of standard buildings

Table 34: Energy performance of 'standard buildings R1-R12

3 st

orey

s

R1 DP: 33 %

R5 DP: 37 %

R9 DP: 40 %

0º 90º 0º 90º 0º 90º


5 st

orey

s

R2 DP: 42 %

R6 DP: 48 %

R10 DP: 54 %

0º 90º 0º 90º 0º 90º


7 st

orey

s

R3 DP: 51 %

R7 DP: 58 %

R11 DP: 63 %

0º 90º 0º 90º 0º 90º


9 st

orey

s

R4 DP: 55 %

R8 DP: 64 %

R12 DP: 72 %

0º 90º 0º 90º 0º 90º




Table 35: Energy performance of ‘standard buildings’ R13-R20

3 st

orey

s R13 DP:

55 %

R17 DP: 69 %

0º 90º 0º 90º

H: 12.22 11.49 H: 14.00 12.89 C: 5.25 2.94 C: 6.34 3.23 V: 15.93 15.12 V: 16.32 15.23 L: 23.12 22.35 L: 16.30 15.56 D: 5.00 5.00 D: 5.00 5.00 E: 61.51 56.90 E: 57.95 51.92

5 st

orey

s

R14 DP: 73 %

R18 DP: 81 %

0º 90º 0º 90º

H: 13.14 11.98 H: 15.34 13.60 C: 7.66 4.21 C: 9.75 5.21 V: 16.77 15.56 V: 17.51 15.92 L: 14.39 13.54 L: 10.10 9.88 D: 5.00 5.00 D: 5.00 5.00 E: 56.97 50.29 E: 57.70 49.61

7 st

orey

s

R15 DP: 80 %

R19 DP: 86 %

0º 90º 0º 90º

H: 14.48 12.91 H: 17.03 14.86 C: 9.79 5.51 C: 12.40 6.82 V: 17.51 16.01 V: 18.43 16.48 L: 10.82 10.56 L: 7.83 7.78 D: 5.00 5.00 D: 5.00 5.00 E: 57.60 49.99 E: 60.69 50.94

9 st

orey

s

R16 DP: 84 %

R20 DP: 89 %

0º 90º 0º 90º

H: 15.88 13.99 H: 18.17 15.71 C: 11.66 6.68 C: 14.63 8.18 V: 18.17 16.43 V: 19.21 16.95 L: 8.62 8.49 L: 6.22 6.32 D: 5.00 5.00 D: 5.00 5.00 E: 59.34 50.59 E: 63.23 52.16

of 111

Appendix 9: Energy performance of low energy buildings

Table 36: Energy performance of ‘low energy buildings’ R1-R12

3 st

orey

s

R1 DP: 33 %

R5 DP: 37 %

R9 DP: 40 %

0º 90º 0º 90º 0º 90º


5 st

orey

s

R2 DP: 42 %

R6 DP: 48 %

R10 DP: 54 %

0º 90º 0º 90º 0º 90º


7 st

orey

s

R3 DP: 51 %

R7 DP: 58 %

R11 DP: 63 %

0º 90º 0º 90º 0º 90º


9 st

orey

s

R4 DP: 55 %

R8 DP: 64 %

R12 DP: 72 %

0º 90º 0º 90º 0º 90º




Table 37: Energy performance of ‘low energy buildings’ R13-R20

3 st

orey

s R13 DP:

55 %

R17 DP: 69 %

0º 90º 0º 90º

H: 2.58 2.18 H: 2.88 2.35 C: 8.59 5.77 C: 10.93 7.13 V: 11.81 10.83 V: 12.64 11.31 L: 12.01 12.09 L: 10.28 9.87 D: 5.00 5.00 D: 5.00 5.00 E: 39.99 35.87 E: 41.73 35.66

5 st

orey

s

R14 DP: 73 %

R18 DP: 81 %

0º 90º 0º 90º

H: 2.46 1.97 H: 2.83 2.18 C: 12.87 8.84 C: 16.43 11.16 V: 13.31 11.90 V: 14.56 12.72 L: 9.22 8.73 L: 8.14 7.68 D: 5.00 5.00 D: 5.00 5.00 E: 42.87 36.44 E: 46.95 38.74

7 st

orey

s

R15 DP: 80 %

R19 DP: 86 %

0º 90º 0º 90º

H: 2.62 2.04 H: 3.15 2.36 C: 16.29 11.41 C: 20.49 14.19 V: 14.51 12.80 V: 15.98 13.77 L: 7.16 6.97 L: 5.65 5.49 D: 5.00 5.00 D: 5.00 5.00 E: 45.57 38.21 E: 50.26 40.82

9 st

orey

s

R16 DP: 84 %

R20 DP: 89 %

0º 90º 0º 90º

H: 2.82 2.16 H: 3.35 2.47 C: 19.37 13.76 C: 24.06 16.92 V: 15.59 13.62 V: 17.22 14.73 L: 6.35 6.10 L: 2.86 2.71 D: 5.00 5.00 D: 5.00 5.00 E: 49.13 40.65 E: 52.49 41.82

Master thesis project - Mikel Urroz

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