Energy Efficiency for Everyone · 2011. 5. 31. · Energy Efficiency for Everyone i AAbbssttrraacctt At present the application of solar passive design principles in new residences

Energy Efficiency for Everyone

i

AA bb ss tt rr aa cc ttAt present the application of solar passive design principles in new residences in

Western Australia is largely limited to those who have the time and money to enlist

the help of professionals. It is proposed that by applying the principles of solar

passive design and using alternative materials in a standard project home, energy

efficient housing can become affordable for a wider range of people. The project will

analyse the energy savings in one such residence in an effort to demonstrate how

simple changes can make a difference.

The total energy consumption of a building is the sum of the on-going energy and the

initial embodied energy consumption. A numerical model, ECOTECT, was selected

as a tool to quantify the ongoing-energy consumption of the project home. However,

before using the model, an independent assessment against an instrumented building

was conducted and the limitations and sensitivity of ECOTECT were defined. A

range of solar passive design options were then proposed for the project home and the

on-going energy savings of each were quantified using ECOTECT. Finally, the

design options were assessed in terms of embodied energy savings using Life Cycle

Assessment and the total savings were determined.

It was found that the embodied energy of the house was over half of the total energy

consumption and hence CO2 emissions. Cost-effective design measures were

estimated to reduce the total energy consumption of the house by 11% through both

ongoing and embodied energy savings, demonstrating the capacity of the industry to

make positive changes.


ii

Acknowledgements

I would like to thanks all of the people who helped make this thesis possible, in

particular:

Dr David Horn for his tireless faith and support throughout the year. His time input

and dedication to my project went beyond the call of duty and I will be ever grateful

to him.

Dr Martin Anda and Phillip from the Murdoch Environmental Centre for offering

their building as the guinea pig for this project and for supplying the relevant data.

Dr Andrew Marsh and Caroline of ECOTECT for answering my continuous

questions.

Dale Alcock and Max Pirone for their support of the project and for providing

materials listings and costings.

My family for their support and assistance throughout my many long years at

university!

My flatmates, Rachel Murphy and Claire Spillman, for their continous empathy, food,

smiles, laughter and music (long live the Waifs).

To the final year class of 2002 who through their own hard work and dedication

inspired me to achieve more and who were in always there to supply hugs whenever

needed!

And to Tiger who came through for me in the most difficult of circumstances and who

managed to cheer me up with just a smile : ).


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TT aa bb ll ee oo ff CC oo nn tt ee nn tt ssAbstract i

List of Figures vi

List of Tables x

List of Symbols xii

1 INTRODUCTION.......................................................................................1

2 BACKGROUND........................................................................................3

2.1 Energy Transfer in Buildings ....................................................................... 3

2.1.1 Solar Energy ............................................................................................ 3

2.1.2 Energy Transfers.................................................................................... 10

2.1.3 Human Comfort ..................................................................................... 15

2.2 Residential Energy Consumption............................................................... 18

2.2.1 Ongoing Energy Consumption ............................................................... 18

2.2.2 Embodied Energy .................................................................................. 21

2.3 Taking Steps to Reduce Energy Consumption .......................................... 26

2.3.1 Reducing Heating and Cooling Needs.................................................... 26

2.3.2 Reducing Embodied Energy................................................................... 31

2.4 Quantifying Energy Consumption ............................................................. 32

2.4.1 Life Cycle Assessment (LCA)................................................................ 32

2.4.2 Modelling .............................................................................................. 33

3 MODEL BACKGROUND........................................................................41

3.1 Model Algorithms ....................................................................................... 41

3.1.1 Steady State Heat Balance...................................................................... 41

3.1.2 Admittance Method ............................................................................... 43

3.2 Graphical User Interface (GUI) ................................................................. 45

3.2.1 3D Drawing Interface ............................................................................ 45

3.2.2 Materials Library ................................................................................... 45


iv

3.2.3 Schedule Editor...................................................................................... 46

3.2.4 ECOTECT Weather Tool....................................................................... 46

3.3 Thermal Analysis ........................................................................................ 47

4 MODEL VALIDATION ............................................................................48

4.1 Model Configuration................................................................................... 48

4.1.1 Building Layout and Materials ............................................................... 48

4.1.2 Forcing Data .......................................................................................... 51

4.1.3 Validation Data...................................................................................... 54

4.1.4 Calibration............................................................................................. 59

4.1.5 Validation .............................................................................................. 59

4.1.6 Sensitivity Analysis ............................................................................... 59

4.2 Results and Discussion................................................................................ 61

4.2.1 Data Analysis Results ............................................................................ 61

4.2.2 Calibration............................................................................................. 64

4.2.3 Validation .............................................................................................. 66

4.2.4 Model Sensitivity................................................................................... 68

4.3 Conclusion................................................................................................... 73

5 MODEL APPLICATION..........................................................................75

5.1 Model Configuration................................................................................... 75

5.1.1 Building Layout and Materials ............................................................... 75

5.1.2 Forcing Data .......................................................................................... 77

5.1.3 Sensitivity Analysis ............................................................................... 77

5.2 Base Case Results........................................................................................ 78

5.2.1 Diurnal Variation ................................................................................... 78

5.2.2 Heat Gains ............................................................................................. 80

5.2.3 Thermal Comfort ................................................................................... 82

5.2.4 Sensitivity.............................................................................................. 84

5.3 Assessing Energy Efficiency ....................................................................... 86


v

5.3.1 Methodology.......................................................................................... 86

5.3.2 Results and Discussion........................................................................... 92

6 CONCLUSIONS AND RECOMMENDATIONS.....................................108

6.1 Recommendations for the Programmer................................................... 109

6.2 Recommendations for the Modeller ......................................................... 110

6.3 Recommendations for the Homeowner.................................................... 111

6.4 Recommendations for the Project Homebuilder ..................................... 112

REFERENCES ............................................................................................113

APPENDICES .............................................................................................121

Appendix A: Environmental Technology Centre Floor Plan ............................ 121

Appendix B: Thermal Properties of Building Materials (Environmental

Technology Centre). ............................................................................................ 122

Appendix D: Thermal Properties of Building Materials (Batavia) ................... 124

Appendix E: Materials Inventory and Embodied Energy Coefficients............. 126

Appendix F: Total Embodied Energy with Replacement and Waste Factors

(Batavia)............................................................................................................... 127

Appendix G: Cost Calculations........................................................................... 128


vi

LLii ss tt oo ff FF iigguurreessFigure 1: Solar radiation components at the earths surface (Fisk, 1982). .................... 4

Figure 2: Spectral distribution of solar radiation at the earths surface (m=1 and m=5)

(Burns, 1992)..................................................................................................... 4

Figure 3: Solar Angles for vertical and horizontal surfaces (Burns, 1992). ................. 5

Figure 4: Sun chart for Perth, Western Australia (University of Oregan, 2001). ......... 8

Figure 5: A homogeneous, isotrophic element. ........................................................ 10

Figure 6: Residential Energy Fuels 1998 (Australian Greenhouse Office, 1999). ..... 19

Figure 7: Residential Energy Usage Share (Australian Greenhouse Office, 1999). ... 19

Figure 8: Heating and cooling greenhouse gas emissions 1998 (Australian Greenhouse

Office, 1999). .................................................................................................. 20

Figure 9: Heating and cooling energy feuls 1998 (Australian Greenhouse Office,

1999). .............................................................................................................. 20

Figure 10: Seasonal Sun movement in Perth, Western Australia (Sustainable Energy

Development Office, 2002).............................................................................. 27

Figure 11: R values or resistivity of common insulating materials (Sustainable

Energy Development Office, 2002).................................................................. 29

Figure 12: Solar pergola in winter and summer (Sustainable Energy Development

Office, 2002). .................................................................................................. 31

Figure 13: A homogeneous isotrophic element. ....................................................... 34

Figure 14: The effect of the decrement factor and time lag (Clarke, 2001). .............. 44

Figure 15: Schedule editor. ...................................................................................... 46

Figure 16: Model Grid used for Murdoch Environmental Technology Centre. Five

different thermal zones were defined; the kitchen, hall, office, analysis lab and

sampling lab. Remaining infrastructure was defined as external shading. ........ 49

Figure 17: Office building with the roof divided into the office (red), hall (cyan) and

kitchen (yellow) thermal zones. ....................................................................... 50

Figure 18: Maximum, minimum and mean monthly air temperatures at Murdoch MET

station from June 2001 – June 2002. ................................................................ 52

Figure 19: Monthly average solar radiation at Murdoch MET station from June 2001-

June 2002. ....................................................................................................... 52

Figure 20: Climate data at Murdoch from July 2001 – June 2002............................. 53


vii

Figure 21: Temperature data collected from the office, analysis lab and sampling lab

over the period Oct. 2001 – Feb. 2002. Temperature data is from the Murdoch

MET station..................................................................................................... 54

Figure 22: Temperature readings from thermistors placed around the office space. .. 55

Figure 23: Temperature data collected from the office, analysis lab and sampling lab

in June 2002. Temperature data is from the Murdoch MET station.................. 56

Figure 24: Mean monthly diurnal temperature variation and standard deviation of the

office, analysis lab and sampling lab versus outside temperature...................... 57

Figure 25: Temperature variation of the office, analysis lab, sampling lab and outside

for 21-28th November 2001. ............................................................................. 57

Figure 26: Temperature variation of the office, analysis lab, sampling lab and outside

2-9th June 2002. ............................................................................................... 58

Figure 27: Hourly temperature averages from Nov 2001 to Jan 2002 and June 2002 in

the outside, office, sampling lab and analysis lab. ............................................ 61

Figure 28: Results of predicted temperature using only heat flux through walls, floor

and roof versus measured temperature data over a summer and winter period. . 62

Figure 29: Heat flux and measured temperatures through the floor in winter and

summer............................................................................................................ 63

Figure 30: Heat flux and measured temperatures through the walls in winter and

summer............................................................................................................ 63

Figure 31: Heat flux and measured temperatures through the roof in winter and

summer............................................................................................................ 63

Figure 32: Soil temperature at depth in the soil (MET station) versus temperature

under the office floor (surface)......................................................................... 64

Figure 33: Hourly operational profile and schedule for weekdays and weekends. .... 65

Figure 34: Temperature difference between model predictions and actual temperature

in the office, analysis lab and sampling lab during a summer period................. 65

Figure 35: Discomfort degree hours as predicted by the model for June and

November. ....................................................................................................... 66

Figure 36: Diurnal temperature variation as given by data from ETC....................... 67

Figure 37: Diurnal temperature variation and maximum and minimum temperature in

November and June. ........................................................................................ 67

Figure 38: Hourly temperatures predicted by the model versus the measured data for

the Office in June and November. .................................................................... 68


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Figure 39: Sensitivity of model predictions to changes in wall thermal lag and thermal

decrement. ....................................................................................................... 70

Figure 40: Sensitivity of model predictions to changes in wall U-values and

admittance (W/m2K)………………………………………………………….. 70

Figure 41: Sensitivity of model predictions to changes in air infiltration rate and wind

sensitivity (air changes per hour) in winter. ..................................................... 71

Figure 42: Sensitivity of model predictions to changes in external temperature........ 72

Figure 43: Sensitivity of model predictions to changes in direct solar radiation (Wh).

........................................................................................................................ 72

Figure 44: Sensitivity of model predictions to changes in wind speed (km/h) and

diffuse solar radiation (Wh) by 20%................................................................. 73

Figure 45: Sensitivity of model predictions to changes in internal heat gains (W/m2).

........................................................................................................................ 73

Figure 46: Thermal zones of the Batavia household in plan view. ............................ 76

Figure 47: Long term average climate data for Perth (Marsh, 2000)......................... 77

Figure 48: The temperature distribution of the various thermal zones of the house.

The living zone is in bold................................................................................. 78

Figure 49: Hourly temperatures on the average coldest day. The living zone (orange)

and roof (red) are shown. ................................................................................. 79

Figure 50: Hourly temperatures on the average hottest day. The living zone (orange)

and roof (red) are shown. ................................................................................. 79

Figure 51: Fabric Gains (sQc+ sQs) (W).................................................................. 81

Figure 52: Ventilation Gains (sQv) (W). .................................................................. 81

Figure 53: Direct Solar Gains (sQs) (W). ................................................................. 81

Figure 54: Indirect Solar Gains (sQs) (W)................................................................ 81

Figure 55: Interzonal Gains (sQz) (W)..................................................................... 81

Figure 56: Thermal Comfort in Batavia household on the average coldest and hottest

day. Percentage dissatisfaction (PPD) from 0-100%........................................ 82

Figure 57: Discomfort degree hours of living area. .................................................. 83

Figure 58: Monthly Heating and Cooling loads with a mixed mode system. ............ 83

Figure 59: Thermal comfort in Batavia household with one person at home on

weekdays. ........................................................................................................ 84

Figure 60: Monthly heating and cooling loads with summer and winter settings. .... 85

Figure 61: Monthly heating and cooling loads with combined settings..................... 85


ix

Figure 62: Monthly heating and cooling loads with combined settings and heating all

night. ............................................................................................................... 86

Figure 63: Final base case thermal discomfort degree hours across the house........... 86

Figure 64: Batavia model with windows shown in yellow. Additional windows

simulated in North facing alcoves shown with arrows. ..................................... 88

Figure 65: The solar pergola as modelled over the summer and winter periods, with

and without roofing (red). ................................................................................ 89

Figure 66: Embodied Energy Components............................................................... 92

Figure 67: Thermal discomfort (degree Hours) of the living and lounge zone with

natural ventilation simulated with and without increased North facing window

space................................................................................................................ 94

Figure 68: Heating and cooling energy consumption (kWh) and approximate costs

with and without increased North facing window space. .................................. 94

Figure 69: Thermal discomfort (degree Hours) of the house with natural ventilation

simulated with increased concrete slab thickness.............................................. 95

Figure 70: Heating and cooling energy consumption (kWh) and approximate costs of

simulated with increased concrete slab thickness.............................................. 95


simulated with timber and tiled flooring........................................................... 95


simulated with timber and tiled flooring........................................................... 95


simulated with solar pergola............................................................................. 96


simulated with solar pergola............................................................................. 96


simulated with Al and timber window and doorframes and single and double-

glazing. ............................................................................................................ 97


simulated with Al and timber window and door frames and single and double

glazing. ............................................................................................................ 97

Figure 77: Thermal discomfort (degree Hours) of Bed 1 with natural ventilation

simulated with extended eaves and shading device........................................... 98


x

Figure 78: Thermal discomfort (degree hours) of the living and lounge zone with

natural ventilation simulated with extended eaves. ........................................... 99


simulated with extended eaves. ........................................................................ 99

Figure 80: Approximate heating cost (AUS$) and thermal discomfort (degree hours)

of the house with natural ventilation with different types of insulation. ............ 99


simulated with different external wall types. .................................................. 100


the living zone with a mixed mode system simulated with different external wall

types. ............................................................................................................. 100

Figure 83: Thermal Comfort in Batavia household at different orientations on the

average coldest day. Percentage dissatisfaction (PPD) from 0-100%. ............ 101

Figure 84: Thermal Comfort in Batavia household at different orientations on the

average hottest day. Percentage dissatisfaction (PPD) from 0-100%.............. 101


simulated with decreased ventilation. ............................................................. 102


with decreased ventilation.............................................................................. 102

Figure 87: Final Design Solution plan view. .......................................................... 105

Figure 88: Final Design Solution Perspective. Bed 1 new window measurements

found. ............................................................................................................ 105

Figure 89: Final Design Solution with South Facing Living Zone Door shown. ..... 105

Figure 90: Improved thermal comfort of all rooms and heating and cooling loads with

new design (bold) against the original design (stripes). .................................. 106

LLii ss tt oo ff TTaabb ll ee ssTable 1: Thermal conductivities of building materials at 0°C (Fisk, 1982). .............. 11

Table 2: The effect of adaptive behaviours on optimum comfort temperatures

(Oseland, 1998). .............................................................................................. 16

Table 3: End-use energy consumption in Australia (%) .(Australian Bureau of

Statistics, 2002) ............................................................................................... 18


xi

Table 4: Initial and recurring embodied energy as a percentage of operating energy

(Cole and Kernan, 1996) .................................................................................. 21

Table 5: Sources of embodied energy data in Australia (Australian Greenhouse

Office, 1999) ................................................................................................... 22

Table 6: Comparison of Published Embodied Energy Coefficients. Published lowest

and highest values are listed against the data supplied by Alcorn (1998). ......... 23

Table 7: Waste factors for different materials in the construction of buildings (Cole

and Wong, 1996; Adalberth, 1997). ................................................................. 24

Table 8: The average lifespan of common building materials (McCoubrie and A.,

1996; Adalberth, 1997). ................................................................................... 24

Table 9: Energy use in different modes of transportation - smallest from (Tillman et

al., 1991; Sperling and Shaheen, 1995; McCoubrie A., 1996; Miller, 1996). .... 25

Table 10: Energy use in installing and processing building components (Adalberth,

1997). .............................................................................................................. 25

Table 11: Internal heat gains per person according to activity rate (Marsh, 2002a).. 43

Table 12: Typical sensible heat gains from equipment (Marsh, 2002a). ................... 43

Table 13: Total areas and volume of the modelled thermal zones............................. 48

Table 14: Initial model parameter settings for the Murdoch Environmental

Technology Centre........................................................................................... 50

Table 15: Thermal Properties of building materials used to model the Murdoch

Environmental Technology Centre (Marsh, 2000)............................................ 51

Table 16: Calibrated internal heat loads. .................................................................. 64

Table 17: Sensitivity of predicted internal temperature outputs to changes in material

properties averaged over both summer and winter periods. Max and Min refer to

the average variation from the base case at the maximum and minimum range

values. ............................................................................................................. 69

Table 18: Summary of model sensitivity to changes in forcing climate data............. 71

Table 19: Base Case Batavia building material types. .............................................. 75

Table 20: Initial model settings for the Batavia project home................................... 76

Table 21: The total hours at each given temperature under natural ventilation.......... 78

Table 22: Embodied Energy Consumption............................................................... 92

Table 23: Embodied Energy Study results from Australia (Hill, 1978; Ballantyne,

1980; D'Cruz et al., 1990; Edwards et al., 1994; Pullen, 1995; Lawson, 1996). 93

Table 24: Alternative materials component embodied energy savings...................... 93


xii

Table 25: Summary of Scenario model findings..................................................... 102

Table 26: Ongoing, Embodied and Net Energy savings over 25yrs. ....................... 103

Table 27: Net Cost Savings to the homeowner over 7 years and pay back periods.. 104

Table 28: Total energy savings over 25 years......................................................... 107

LLii ss tt oo ff SSyymmbboo ll ssabs Absorptance

a Admittance factor

A Area of surface of interest (m2)

KT Clearness index

h Convective heat transfer coefficient (W/m2)

_ Declination (º)

d Decrement factor

_ Density (kg/m3)

_r Diffuse ground reflectance

Iso Diffuse solar flux (isotropic) (W/m2)

_ Diffusivity (m2/s)

IDN Direct normal flux (W/m2)

EL Dry respiration heat loss (W)

N Effective ventilation rate (air changes per hour)

e Embodied energy coefficient (MJ/kg or MJ/m3)

Ee Embodied energy use (J)

_ Emittance (dimensionless)

Ed Energy used in demolishing a building (J)

ev Evaporation rate (kg/h)

He Extraterrestrial solar radiation on a horizontal surface (W/m2)

fso Fraction of diffuse radiation passing through external shading

GER Gross Energy Requirement (J)

EC Heat loss by convection from the surface of a clothed body (W)

ER Heat loss by radiation from the surface of a clothed body (W)

Esw Heat loss due to sweating (W)

Ed Heat loss due to water vapour diffusion through the skin (W)


xiii

_c Indoor adaptive comfort temperature (ºC)

H Internal heat production (W)

_i Internal temperature (ºC)

Ere Latent heat loss due to respiration (W)

LON Longitude

Em Manufacturing energy use (J)

Met Metabolic Rate

Ag Net glazing area (m2)

_o Outside air temperature (ºC)

PMV Predicted Mean Vote

PPD Predicted Percentage of Dissatisfied People

PER Process Energy Requirement (J)

hr Radiative heat-transfer coefficient

q Rate of heat transfer (W)

RH Relative humidity

_ Replacement factor

R Resistance to heat transfer (m2°C/W)

_s Sol-air temperature (ºC)

_ Solar Altitude (º)

_ Solar Azmith (º)

Sc Solar constant (W/m2)

G Solar flux (W/m2)

sgf Solar gain factor

Ih Solar radiation incident on a horizontal surface (W/m2)

C Specific Heat (J / kgºC)

_ Stefan-Boltzmann constant (W/m2K4)

_ Surface azmith (º)

_ Temperature (K)

k Thermal conductivity (W/mºC)

_n Thermal neutrality (ºC)

_b Transmittance of glazing to beam radiation (dimensionless)

fb Transmittance passing through external shading (dimensionless)

Et Transport energy use (J)


xiv

fr Unshaded fraction of reflection

U U-value or overall heat-transfer coefficient (W/m2C)

F View factor

V Volume (m3)

_ Waste factor


1

11 II nn tt rr oo dd uu cc tt ii oo nnAs international concern has become centered on natural resource consumption and

carbon dioxide emissions, increased energy consumption around the globe has led to a

quest for more sustainable practices. The worldwide primary energy consumption of

buildings is close to 19 million barrels of oil per day (Santamouris, 2001b). With this

energy usage also accounting for 40% of materials, 25% of timber and 16% of

freshwater usage across the globe (Birtles, 1997), the potential for the built

environment to play a key role in reducing energy consumption is not to be

overlooked.

Energy usage is the highest in urban areas, with the average electricity consumption

for cities around the world with more than 1 million inhabitants being around

4500GWh per year (Santamouris, 2001a). Given that an increase in urban population of

1% leads to more than double the rate of energy consumption (Santamouris, 2001a) and

that Perth, Western Australia has an expected growth rate of 1.4% per year (Australian

Bureau of Statistics, 2002), it can be expected that energy demands will increase by at

around 2% per annum.

Non-renewable sources of energy, such as coal, petroleum and natural gas continue to

be the major sources of energy supply in Australia. As supplies become further

depleted, implementing efficiency standards across all sectors is as important as

moving towards renewable supplies of energy. The Australian building sector has the

potential to achieve both of the above. Firstly by implementing building standards

and codes that will maximize the potential for efficient energy use options and

secondly by making optimal use of natural heating and cooling processes.

The second major issue in energy consumption of building is the emission of

greenhouse gases under the global context of the Kyoto Protocol on climate change.

The Prime Minister of Australia announced in November 1997 that the building

sectors contribution to meeting targets form greenhouse gas emissions would be as

follows:


2

“The Commonwealth will work with the States, Territories and key

industry stakeholders to develop voluntary minimum energy

performance standards for new and substantially refurbished

commercial buildings on the basis of energy efficient benchmarks.

If after 12 months the government assesses that the voluntary

approach is not achieving acceptable progress towards higher

standards of energy efficiency for housing and commercial

buildings, we will work with the States and industry to implement

mandatory standards through amendment of the Building Code of

Australia.” (Australian Greenhouse Office, 1999)

Following this, the Chairman of the Australian Building Codes Board (ABCB), Mr

Peter Laver, announced in August 2002 that changes to the Building Code of

Australia (BCA) will be introduced in 1 January 2003 (ABCB, 2002). The revised

BCA will outline performance requirements, approved solutions, and means of

assessing alternative designs in order to reduce energy consumption and greenhouse

gas emissions.

Marsh (1995) makes the point that in the past while most large-scale projects use an

environmental consultant, smaller scale houses are usually designed without reference

to environmental principles. There exists a significant body of knowledge that can

now allow us to design effectively towards environmental goals rather than purely

aesthetic goals.

This project aims at defining steps towards reducing energy consumption of a project

home without compromising the economics or aesthetics of the building through the

application of solar passive design principles. It is believed that the project home

industry has the capacity to significantly reduce residential energy consumption, as

changes to a single design will not only lead to energy savings of one household but

several. An independent assessment of the industry’s capacity to meet stricter

building regulations and appropriate tools for evaluating energy efficient design in

Western Australia has been made in an attempt to expand the knowledge base from

which these designers will work in the future.


3

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22 .. 11 EE NN EE RR GG YY TT RR AA NN SS FF EE RR II NN BB UU II LL DD II NN GG SS

This section will outline physical principles related to energy transfer, in particular

heat transfer in the built environment. Firstly, the availability of solar energy will be

discussed and secondly the potential for this energy to be converted effectively to heat

energy through the mechanisms of convection, conduction and radiation will be

investigated.

2.1.1 SOLAR ENERGY

Energy conservation in buildings has in the past primarily referred to the reduction of

energy consumption regardless of whether the energy is renewable or non-renewable

(Dickinson and Cheremisinoff, 1980). As a consequence, the use of solar energy was not

considered as an energy-conserving device (Dickinson and Cheremisinoff, 1980).

However, the efficient usage of solar energy offers unique opportunities for the

conservation of energy in building practices.

SOLAR RADIATION AT THE EARTH’S SURFACE

An idealized model breaks incoming solar radiation into direct, diffuse, reflected and

infrared environmental radiation (Figure 1). Outside the atmosphere, light travels in a

direct path from the sun in what is called the _ degree cone, with radiation within this

cone being termed direct radiation (Burns, 1992). It is assumed that the majority of

incoming solar radiation is only diverted slightly from its original path upon entering

the atmosphere and hence remains within a 5-degree cone, called circumsolar

radiation. The sum of the direct radiation and circumsolar radiation is known as

direct normal or beam radiation. Diffuse radiation is radiation reflected by dust,

clouds and water vapour and environmental radiation is the emission of infrared light

from the heated air and landscape. The light reaching a building surface is usually

approximated as the sum of direct normal, diffuse and reflected radiation, where

reflected radiation from surfaces other than the ground are ignored.


4

Atmosphere

Earths Surface

Direct (1/2º Cone)

Circumsolar (5º Cone)Diffuse

Reflected

Atmosphere

Earths Surface

Direct (1/2º Cone)

Circumsolar (5º Cone)Diffuse

Reflected

Figure 1: Solar radiation components at the earths surface (Fisk, 1982).

The spectral distribution of solar radiation is defined relative to mass (m) of

atmosphere traversed, defined as 0 outside the atmosphere and 1 when the sun is

directly overhead (Burns, 1992). As the air mass increases, the spectral distribution

shifts towards longer wavelengths and the total energy decreases (Figure 2). The

spectral distribution of incoming solar radiation plays an important role when the

transmittance, absorptance and reflectance of a material are dependant on the

wavelength such as the case with metals (Burns, 1992). Measured solar energy at the

earth’s surface often does not account for the fact that a shift in wavelength occurs

and are usually calculated for m=1, which can translate to errors of up to 10% for

building materials which are highly dependant on wavelength.

Figure 2: Spectral distribution of solar radiation at the earths surface (m=1 and m=5) (Burns,

1992).


5

_

The directional distribution of solar radiation determines the amount of solar energy

available to a surface (Burns, 1992). The amount of solar energy falling on a surface

per unit area is called irradiance or solar flux (Kreith and Kreider, 1978). Insolation is

the solar irradiance received on a horizontal surface through direct normal and diffuse

radiation (Fisk, 1982). The extraterrestrial solar radiation on a horizontal surface (He)

is a function of the incoming angle (_), the midday of the month (N) and the solar

constant (Sc):

βπsin)]

365

2sin(0340.01[

NSH ce +=

( 1)

(Burns, 1992). The total radiation is then correlated with a clearness index (KT), which

accounts for weather variability to calculate the incident radiation on a horizontal

surface (Ih) as:

Ih = He KT ( 2)

Calculating radiative flux on surface other than the horizontal usually involves

assuming that the diffuse component is isotropic (Burns, 1992). Recent models have

attempted to incorporate a weighting factor between diffuse and direct normal

radiation in an attempt to rectify this (Puri et al., 1980).

The American Society of Heating Refrigerating and Air-Conditioning Engineers

(ASHRAE, 1981) define a co-ordinate system for solar analysis with the origin at the

center of the building surface (Figure 3). This coordinate system defines the surface

azimuth (_) as positive in a counter-clockwise direction from due north, the slope of

the surface is denoted by _ and the position is defined by the latitude, longitude and

local time (t).

Normal to vertical surface

N

Earth-sun line

y

S Surface Slopeb

f

Solar Altitude

Solar Azmith

Surface Azmith

v

Normal to vertical surface

N

Earth-sun line

y

S Surface Slopeb

f

Solar Altitude

Solar Azmith

Surface Azmith

v

Figure 3: Solar Angles for vertical and horizontal surfaces (Burns, 1992).


6

The position is given relative to the distance from the sun (d) and the day of the year

(N), which gives the declination, _, or the angle between the plane of the earths orbit

and the plane of the earth’s equator:

]365/)28sin[(45.23 N+=δ( 3)

It is also necessary to use a time scale in which reflects the position of the sun relative

to the surface of interest. The local solar time defines the time at which the sun is

directly overhead at solar noon. This time correction is normally not significant when

predicting the amount of incident energy but is important in determining the sunrise

and sunset times (Fisk, 1982). The local solar time is given by:

)2cos2172sin554cos4.15sin445()(240 DDDDLONLONtt ss ++−+−+= ( 4)

Where

LONs is the standard longitude at which the time zone is centred

LON is the longitude

D is equal to 2 N/365, a correction factor due the earth’s elliptical orbit

(ASHRAE, 1981).

DIRECT NORMAL OR BEAM RADIATION

The angle of incidence of beam radiation of a surface is given by _ (Figure 3)

(ASHRAE, 1981). The beam radiation entering a building (Qb) through a transparent

surface is given by:

bgDNbb AIfQ τθcos=( 5)

Where

fb is the transmittance passing through external shading

IDN is the direct normal flux

Ag is the net glazing area

_b is the transmittance of the glazing for beam radiation

The above equation can only be applied when there are no exterior reflections and can

be used for opaque surfaces by exchanging transmittance (_b) for absorptance (abs)

(Burns, 1992).


7

DIFFUSE RADIATION

Diffuse radiation may be of the same magnitude as beam radiation during the summer

months and for surfaces oriented away from the sun. The transmitted diffuse

radiation is given by (Burns, 1992):

dgisos

os AFIfQ τ=

( 6)

Where

fso is the fraction of diffuse radiation passing through external shading

Iso is the diffuse solar flux (isotropic)

Fis is the view factor from the surface to the sky or the percentage of diffuse

energy that is incident upon the surface in the absence of shading

_d is the transmittance of the glazing for diffuse radiation (Seigel and Howell,

1982).

GROUND REFLECTED RADIATION

Ground reflected radiation will dominate in situations where shading of beam and

diffuse radiation occurs and reflection is high, as is often the case in summer (Burns,

1992). The transmitted ground reflected radiation is given by:

dgirhrr AFIfQ τρ=( 7)

Where

_r is the diffuse ground reflectance

fr is the unshaded fraction of reflection

Ih is the total horizontal flux

Fir is the diffuse view factor between the surface and the ground (Burns,

1992).

The direction of ground reflected radiation is highly dependant on the surface and is

usually estimated as a constant (ASHRAE, 1981). The fractions of radiation that are

unobstructed by shading, fb, fso and fr are dependant on obstruction by trees and

overhangs (Burns, 1992).


8

SUN CHARTS

When analysing the solar energy available to a building the most important factor is

the determination of the direction of the incident rays. Sun charts were developed to

simplify the above calculations in order for this information to be more readily

available within an expectable range of error in the early stages of design (Burns,

1992). Sun charts define the position of the sun relative to the solar altitude angle (_)

and the solar azimuth angle (_) as given in Figure 3 (ASHRAE, 1981). The sun chart

calculated for Perth, Western Australia is given in Figure 4.

Figure 4: Sun chart for Perth, Western Australia (University of Oregan, 2001).

For a given latitude, the path of the sun is traced onto the sun chart given by the blue

lines, each of which represents two days, with the exception of June 21 and December

21. This is due to the fact that the suns path is identical on complimentary spring and

autumn days. The red lines represent the hour of the day at which the solar altitude is

0 degrees (Mazria, 1979). For example, at 10:00am on March 20th the solar azimuth is

52 degrees east of north and the solar altitude is 47 degrees. Practically, this means


9

that the surface will be shaded until 10am by an obstacle reaching a height of 47

degrees and spanning 52 degrees to the north (Mazria, 1979).

To calculate the specific area of shading caused by the surrounding landscape and

overhangs, Mazria (1979) has developed a shading calculator, which essentially plots

the surrounding landscape graphically on the sun chart. A solar radiation calculator,

which again graphically depicts the available sunlight throughout the day, can then be

used to calculate the incident solar flux, (Mazria, 1979). This value when multiplied by

the transmittance and the unshaded area fraction can then be used to calculate the

solar energy entering the building (Mazria, 1979).

The drawback of this technique is that it only accounts for incident beam radiation.

In summer months when ground reflected radiation and diffuse radiation become

important the error of this method is compounded (Burns, 1992). Secondly, the solar

radiation calculator does not take into account the clearness factor and assumes clear

days (Burns, 1992).

It is important to understand the underlying assumptions made in estimations of

incoming solar energy in order to determine which assumptions are particularly

relevant to the site in question. For the given site of Perth, Western Australia, hot, dry

summers mean that ground reflected radiation becomes extremely important.

However, the error of neglecting the clearness factor is small in comparison due to the

fact that Perth is dominated by clear days.


10

2.1.2 ENERGY TRANSFERS

Solar flux incident on a building surface is transferred to the building proper through

the mechanisms of conduction, convection and radiation. The role of air flow and

casual gains mechanisms in determining internal conditions are also discussed.

CONDUCTION

Thermal conduction is the transfer of energy through a material as heat from hotter,

more energised particles to cooler, less energized particles (Fisk, 1982). In a building,

conduction determines the transfer of heat from one boundary of a solid material to

another boundary, being reduced in magnitude and shifted in time due to the buildings

inertia (Clarke, 2001).

q(x,t) è(x,t)

0 l x

Figure 5: A homogeneous, isotrophic element.

If we consider a homogeneous, isotropic element with heat flux q and temperature _

(Figure 5), the Fourier Heat Conduction Equation in two dimensions in given by:

tx ∂∂=

∂∂ θθκ

2

2

( 8)

Where _ is the diffusivity defined as:

C

k

ρκ =

( 9)

Where

k is the thermal conductivity (W/m˚C)

_ is the density (kg/m3)

C is the specific heat (˚C).

These three properties are all dependant on time due to fluctuations in material

temperature and moisture. If the material is non-homogeneous or anisotropic these


11

properties may be dependant on position or direction. Typical thermal conductivities

of building materials are given in Table 1.

Table 1: Thermal conductivities of building materials at 0°°°°C (Fisk, 1982).

Material k (W/m°C)

Copper 386Aluminium 204

Steel 43Concrete 1.7

Face Bricks 1.3Glass 0.73Water 0.555

Hardwood 0.16Glass-wool Insulation 0.038

CONVECTION

Surface convection is the process by which heat flux is transferred between a surface

and the adjacent air layer (Clarke, 2001). When modelling a building, convective heat

flux is handled differently for internal and external air movement. External

convention is driven by the wind and hence considered as forced, whilst internal

fluxes can be either natural or forced by air conditioner or coolers. The rate of heat

transfer is given over the area of the flow surface (A) by:

xhAq

∂∂= θ

( 10)

Where h is the convective heat transfer coefficient (W/m2ºC). Usually, temporal

surface averaged convection coefficients are used in building simulation.

To calculate external forced convection flow, wind speed and direction data are

extrapolated to calculate the velocity profile of the fluid at different heights (Clarke,

2001). It is more difficult to estimate internal forced convection flows as they are

highly dependant on the operation and usage patterns of the equipment. However,

natural convection has been tackled by modellers and convection coefficients have

been derived as a function of temperature difference, surface roughness, direction of

heat flow and dimension heights.

RADIATION

Radiation heat transfer is the transfer between different bodies of heat energy by

infrared or visible light due to differences in temperature (Fisk, 1982). A blackbody is


12

a surface that emits and absorbs radiation perfectly. The rate at which it radiates

energy is given by:

4θδAq =( 11)

Where σ is the Stefan-Boltzmann constant (5.67 x 10-8 W/m2K4).

The fraction of blackbody radiation that a surface emits is called emittance, ε, and is

calculated by:

blackbody

actual

Aq

Aq

)/(

)/(=ε

( 12)

The fraction of available light that a surface absorbs is the absorptance, α, which is

equal to the emittance of the material for a given wavelength as given by Kirchhoff’s

law (Fisk, 1982). A good solar radiator should be capable of absorbing and utilising

solar energy but have a low emittance for infrared radiation or heat loss (Fisk, 1982).

Radiative heat transfer between two surfaces is given by:

xAhq r ∂

∂= θ( 13)

Where hr is the radiative heat-transfer coefficient (W/m2ºC). In most simplified

methods, radiative heat transfer coefficients are treated as combinations of convection

and longwave radiation (Clarke, 2001). However, the values used are often

questionable as these two processes both act to lower or raise surface temperatures.

Longwave radiation heat transfer between two surfaces is a function of surface

temperatures, emissivities, the extent to which the surfaces are in visual contact,

represented by the view factor, and the type of surface reflection (Clarke, 2001). This

type of heat exchange is particularly important when the temperature difference is

large, and solar passive buildings attempt to maximise this. One common method is

to use window glazings with low emissivity that increase the reflection of longwave

radiation and hence limit heat exchange.

Externally the exchange of longwave radiation between a building and the

surroundings can result in a substantial lowering of surface temperatures. To


13

accurately estimate this flux the air temperature, temperature of surrounding

buildings, ground temperature, and the view factors associated with each of these

components need to be considered.

Some portion of the direct or diffuse shortwave solar energy arriving on a surface may

pass through the fabric to contribute to internal heat fluxes (Clarke, 2001). For this

reason it is not uncommon for exposed surfaces to be 15-20ºC above ambient

temperatures. A simplified method of estimating fabric solar gain is the ‘sol-air’

temperature method, which defines an elevated ambient temperature (the sol-air

temperature) for use in conduction equations. The major drawback of this method is

that it fails to account for changes in solar flux over time unless the sol air

temperature is determined on the basis of time dependant shading and convention.

OVERALL HEAT-TRANSFER

As a demonstration of overall heat transfer processes within a building consider the

following scenario. Shortwave energy impinging on transparent surfaces is partially

reflected and partially absorbed within the material to raise its temperature. This rise

in temperature on the outermost surface will then drive heat conduction through the

material to establish a temperature change at the innermost surface. As a

consequence, this change will drive surface convection and longwave radiation heat

transfers. The component of the incident beam will strike an internal surface where a

proportion of the energy is reflected and a proportion is absorbed to give rise to

transient conduction where the flux is stored and lagged before being transferred back

to the outside or to another zone (Clarke, 2001).

In this process the thermal properties of interest are the absorptivity of opaque and

transparent materials and the transmissivity and reflectivity of transparent materials

(Clarke, 2001). As stated earlier these properties are dependant on the angle of

incidence of shortwave radiation and its spectral composition. These properties are

commonly averaged over the solar spectrum.

When considering a multi-layered construction a simplified index to represent the

total heat transfer through the above processes is called the U-value or total

transmittance given by:


14

∑=

+++=

N

i i

isosic k

xRRR

U

1

)()()(

1

( 14)

Where R is the combined radiative and convection resistance (m2°C/W) with

subscripts c, si and so refering to the innermost surface, the outermost surface and the

cavity respectively. N is the total number of layers, with x referring to the thickness

and k the conductivity of each layer. The overall heat-transfer is then estimated by:

q = UA∆_ ( 15)

This technique involves making a steady state assumption and hence the dynamic

aspects of material behaviour and spatial differences are ignored (Clarke, 2001).

However, it has still been deemed a useful tool and is often used to make first

estimates of heat transfer.

AIR FLOW

Within a building, airflow by infiltration, zone-coupled flows and mechanical

ventilation will also influence heat exchange (Clarke, 2001). Infiltration refers to

leakage of air from the outside and can be broken into natural ventilation through

windows and doors and leakage through cracks, vents and through building fabric.

The buoyancy forces due to density differences in the air drive zone-coupled flow

between two areas of a building. Mechanical ventilation is airflow driven by

conditioning systems.

Analytical solutions to air flow movement require the solution of the energy,

continuity (mass) and momentum (Navier-Stokes) equations (Clarke, 2001). The

contribution of air flow to internal conditions will be influenced by random

occurrences such as the opening and closing of doors, changes in wind speed and the

use of mechanical coolers which are often difficult to estimate.

CASUAL GAINS

Casual heat gains include heat gains from body heat, lighting and other electrical

equipment such as fridges. The contribution of casual heat gains to a building can be

significant (Clarke, 2001). Usually it is assumed that convective heat transfer from

these sources occurs instantaneously, spreading heat from the source around the room.


15

Radiative gains are usually distributed on the internal building surfaces according to

the distribution pattern of the said item.

2.1.3 HUMAN COMFORT

In the context of building design, human comfort is defined as the absence of any

form of thermal stress (Marsh, 2002a). Human comfort is a function of the

surrounding climatic conditions, namely the dry bulb temperature, mean radiant

temperature, relative humidity (RH) and air movement (Vel) as well as physiological

factors often defined in terms of metabolic rate (Met) and clothing level (Clo).

Comfort indices have been developed to account for all these factors and predict

human comfort levels, three of which will be considered; the simple Thermal

Neutrality model, the Adaptive model and the more complex Predicted Mean Vote.

THERMAL NEUTRALITY

Thermal Neutrality (_n) has been defined as the air temperature at which, on average,

a large sample of people would neither feel hot or cold (Marsh, 2002a) and is

correlated to dry bulb temperature (_av):

_n = 17.6 + 0.31 _av( 16)

To allow for variance in human response, the comfort zone is taken at ±2K about the

thermal neutrality temperature if the annual average temperature is used, or ±1.75K if

the mean monthly average is used (Marsh, 2002a).

ADAPTIVE COMFORT

Adaptive comfort models assume that people will change their behaviour, by either

taking off clothing or opening windows etc, upon reaching a state of thermal

discomfort (Table 2) (Oseland, 1998). Effectively this increases the range of thermal

comfort temperatures, especially in naturally ventilated buildings where occupants

have more control over the environment.

Humphreys & Nicol (1998) give equations for calculating the indoor comfort

temperature from outdoor monthly mean temperature for naturally ventilated

buildings and conditioned buildings respectively as follows:


16

_c = 11.9 + 0.534 _ave( 17)

_c = 23.9 + 0.295(_ave-22) exp([-(_ave-22)/33.941]_)( 18)

Table 2: The effect of adaptive behaviours on optimum comfort

temperatures (Oseland, 1998).

BEHAVIOUR EFFECT OFFSETJumper/Jacket on or off Changes Clo by ± 0.35 ± 2.2K

Tight fit/Loose fit clothing Changes Clo by ± 0.26 ± 1.7KCollar and tie on or off Changes Clo by ± 0.13 ± 0.8K

Office chair type Changes Clo by ± 0.05 ± 0.3KSeated or walking around Varies Met by ± 0.4 ± 3.4K

Stress level Varies Met by ± 0.3 ± 2.6KVigour of activity Varies Met by ± 0.1 ± 0.9KDifferent postures Varies Met by ± 10% ± 0.9K

Consume cold drink Varies Met by -0.12 + 0.9KConsume hot drink/food Varies Met by +0.12 - 0.9K

Operate desk fan Varies Vel by +2.0m/s + 2.8KOperate ceiling fan Varies Vel by +1.0m/s + 2.2K

Open window Varies Vel by +0.5m/s + 1.1K

The adaptive model has been varied to account for Australian conditions as a function

of both mean outdoor dry bulb temperature and the average indoor temperature (_i)

(Humphreys and Nicol, 1998):

_c = 9.22 + 0.48 _i + 0.14 _ave( 19)

PREDICTED MEAN VOTE

The Predicted Mean Vote (PMV) is a thermal scale from –3 (cold) to 3 (hot)

determined from a large sample of people asked to indicate their relative sensations on

the scale (Marsh, 2002a). The steady state model takes into account all environmental

and physiological factors affecting human comfort. As the PMV moves away from

neutral the Predicted Percentage of Dissatisfied people (PPD) is determined. Perfect

conditions are found at a PPD of 5%. PMV is recognised by the International

Standards Organisation (ISO, 1984) and is the most widely used thermal comfort

index.

PMV assumes that humans are exposed to constant conditions at a constant metabolic

rate (Marsh, 2002a). Conservation of energy under these conditions gives:


17

H - Ed - Esw- Ere - EL = ER + EC( 20)

Where

H is internal heat production

Ed is heat loss due to water vapour diffusion through the skin

Esw is heat loss due to sweating

Ere is latent heat loss due to respiration

EL is dry respiration heat loss

ER is heat loss by radiation from the surface of a clothed body

EC is heat loss by convection from the surface of a clothed body.

Each of the above heat loads can be substituted with empirical determination, except

for clothing surface temperature and the convective heat transfer coefficient, which

are functions of each other (Marsh, 2002a). By estimating an initial clothing

temperature and iterating, both of these values can be estimated.

For a body not at thermal equilibrium the thermal load (L) on the body is (Marsh,

2002a):

L = H - Ed - Esw - Ere - ER – EC

( 21)

Thermal strain or sensation (PMV) is then defined as an unknown function of the

thermal load and the metabolic rate. By correlating the thermal load with the

experimentally determined data it was found that the PMV is exponentially related to

the metabolic rate as:

PMV = exp(Met) * L ( 22)

The major limitation of this model is that skin temperature and evaporative heat loss

are prescribed as set values at neutral comfort levels.


18

22 .. 22 RR EE SS II DD EE NN TT II AA LL EE NN EE RR GG YY CC OO NN SS UU MM PP TT II OO NN

2.2.1 ONGOING ENERGY CONSUMPTION

The energy consumption of the residential sector can be divided into two components;

on-going energy consumption and embodied energy. The on-going energy

consumption of a household includes the energy used in heating and cooling and

electrical appliance use (Chen et al., 2001; Yohanis and Norton, 2002). Embodied energy

is the energy used in the construction of a household from producing and transporting

the materials through to the demolition and disposal energy costs (Yohanis and Norton,

2002).

Conventionally, energy usage of the residential sector has referred to on-going energy

consumption. This is primarily because on-going energy consumption is easy to

measure and administer, whilst calculation of embodied energy requires more in depth

analysis and processes. At present, processes for calculating embodied energy

consumption for households in Australia are under development (Australian

Greenhouse Office, 1999).

Table 3: End-use energy consumption in Australia (%) .(Australian Bureau of Statistics, 2002)

1998-99 (PJ) %Agriculture 70.1 1.4

Mining 264.5 5.4Manufacturing 1,177.0 24.2

Electricity generation 1,398.3 28.8Construction 50.3 1.0

Transport 1,231.2 25.3Commercial 210.5 4.3Residential 386.0 7.9

Other 70.3 1.4Total 4,858.3 100.0

In terms of on-going energy, 7.9% of reticulated energy consumption in Australia can

be attributed directly to the residential sector (Table 3) (Australian Bureau of Statistics,

2002). The average weekly-reticulated energy usage of residential houses in Western

Australia varies between those with reticulated gas from 350MJ in summer to 400MJ

in winter, and those with reticulated gas from 500MJ in summer and 700MJ in winter

(McLennan, 1988). In comparison with the national average, Western Australia


19

consumes less energy on both counts, with our energy consumption in the winter

months being significantly lower due to mild winter temperatures (McLennan, 1988).

Australia’s residential sector is primarily sourced by electricity, natural gas and wood

(Figure 6) (Australian Greenhouse Office, 1999). Of primary concern both in terms of

renewable resources and carbon dioxide emissions is the use of electricity from coal

and petroleum. In particular, as natural gas and wood are low greenhouse gas

emitters, 85% of greenhouse gas emissions can be attributed to electricity

consumption (Australian Greenhouse Office, 1999). Of the total energy consumption of

residential houses, 39 percent is used for space heating and cooling, followed by

electrical appliances, water heating and cooking (Figure 7).

3%14%

35%

48%

LPG

Wood

Natural Gas

Electricity

1%

4%

27%

30%

38%

Space Cooling

Cooking

Water Heating

Electrical Appliances andEquipmentSpace Heating

Figure 6: Residential Energy Fuels 1998(Australian Greenhouse Office, 1999)

.

Figure 7: Residential Energy Usage Share(Australian Greenhouse Office, 1999)

.

HEATING AND COOLING

Space heating and cooling accounted for 39% of total residential operational energy in

1998 (Australian Greenhouse Office, 1999), with the 3 main energy sources being

electricity, natural gas and wood. Due to the fact that a large proportion of this energy

is from less energy intensive energy sources than mains power in terms of carbon

dioxide emissions, heating and cooling energy usage only accounts for 15% of

residential greenhouse gas emissions (Australian Greenhouse Office, 1999).

National surveys have shown that 85% of households that have reticulated energy

heating use it during winter for an average of 6 days per week. In Western Australia,

the winter usage was averaged at 4.5 hours a day, 5.2 days a week (McLennan, 1988).


20

The energy consumption required for heating and cooling is a function of climate, the

thermal performance and design of the building, user behaviour and the energy

efficiency of heating and cooling appliances used (Australian Greenhouse Office, 1999).

4%10%

15%

15%

56%

LPG

Wood

Electricity Cooling

Electricity Heating

Natural Gas

3% 3%

3%

35%56%

Electricity Cooling

Electricity Heating

LPG

Wood

Natural Gas

Figure 8: Heating and cooling greenhouse gas

emissions 1998 (Australian Greenhouse Office,

1999).

Figure 9: Heating and cooling energy feuls

1998 (Australian Greenhouse Office, 1999).

APPLIANCES

A study conducted by the Australian Greenhouse Office (1999) found that 30 percent

of total energy use and 52 percent of greenhouse gas emissions from the residential

sector came from appliances selected by the residents and hence outside the influence

of the building sector.

The appliances that have the highest usage and hence consume the greatest amount of

energy are clothes dryers, ovens, dishwashers, cooktops and washing machines

(McLennan, 1988). Of these, the designer of a project home has the capacity to

influence decisions on the choice of dishwasher, oven and cooktop. Combined, these

appliances are used on average 225 minutes per week (McLennan, 1988), meaning that

significant energy savings could be made with the choice of energy efficient

appliances.

The Australian Greenhouse Office (2002) implements energy efficiency standards

ratings in the form of an Energy Rating label. The label is currently mandatory in

most states for all refrigerators, freezers, clothes washers, clothes dryers, dishwashers

and air-conditioners (less than 7.5 kilowatts output cooling capacity) (Australian

Greenhouse Office, 2002). The label constitutes a star rating based on a scale of zero to

six which gives a measure of the appliance’s energy efficiency per unit energy used


21

and the comparative energy consumption which gives an estimate of how much

energy the appliance will use in one year (Australian Greenhouse Office, 2002).

2.2.2 EMBODIED ENERGY

Embodied energy is the energy involved in the construction of a building. This

includes the energy involved in the acquisition of natural resources, and all processes

from the manufacture of the building material through to transport of the products

(Australian Greenhouse Office, 2001). Embodied energy can be divided into two

components; initial embodied energy is the energy put into production and recurring

embodied energy is the energy used in maintenance, repair and renovation (Chen et

al., 2001).

Another important distinction often made in embodied literature is between the gross

and process energy requirements. The Gross Energy Requirement (GER) of a

household will include energy inputs such as lighting in the manufacturing process,

transporting the staff to the site and the embodied energy of infrastructure such as

roads (Australian Greenhouse Office, 2001) while the Process Energy Requirement

(PER) is a measure of the energy related only to the manufacture of a material

(Australian Greenhouse Office, 2001). This generally accounts for 50-80% of GER.

Research has shown that contrary to popular belief, the embodied energy of a building

is comparable to and may exceed the on-going energy consumption of a building

(Table 4) (Australian Greenhouse Office, 2001). It has been proposed that embodied

energy use could account for up to 40% of the life-cycle energy used in residential

buildings (Cole and Wong, 1996) and that embodied energy can be as much as 50%

of its operational energy over 25 years in new well-insulated buildings (Atkinson et

al., 1996). Treloar and Tucker (1994) estimated that the embodied energy of

construction materials in Australia accounts for 19.5% of total energy use.

Table 4: Initial and recurring embodied energy as a percentage of operating energy (Cole and

Kernan, 1996)

Building Life Initial embodied energy (%) Initial + recurring embodied energy (%)25 67 10550 34 82

100 17 71


22

The most important principle in reducing the energy use by buildings is to design

buildings for long life whilst keeping the design flexible (Australian Greenhouse Office,

2001). The CSIRO found that the average household contains 1000GJ of embodied

energy, which is equivalent to 15 years of operational energy use (Australian

Greenhouse Office, 2001).

DATA AVAILABILITY

The embodied energy of a single material is dependant on several factors. The

efficiency of the manufacturing process, the fuels used in the manufacturing, distances

materials are transported and the amount of recycled product used all contribute to the

variability in the ways materials can be assessed (Australian Greenhouse Office, 2001,

October 10-last update).

There are three main methods used to carry out an embodied energy analysis; process

analysis, input-output analysis; and hybrid analysis (Australian Greenhouse Office,

1999). Process analysis involves examining the inputs to the processes of a product.

Due to the fact that the energy inputs get increasingly more complex as one considers

indirect energy inputs into the main process, this technique may be significantly

incomplete. Input-output analysis uses economic statistics to account for almost all

energy inputs, both direct and indirect, to the main process (Treloar, 1998). This

method is more accurate but still requires making inherent assumptions that may lead

to errors. Hybrid analysis is the combination of the previous methods with the

intention to reduce error (Bullard et al., 1978; Treloar, 1997). Hybrid analysis based

on input-output tables as developed by Treloar (1997) is the most comprehensive

method available.

Table 5: Sources of embodied energy data in Australia (Australian Greenhouse Office, 1999)

SourceHill 1978 Now considered old, process analysisD’Cruz et al. 1990 Used data from old sources and international sourcesTucker et al. 1993 International process data and Australian input-output data.Treloar 1996 Based on Tucker et al. (1993) with minor improvementsPullen 1995 Similar to Treloar (1996) and Tucker et al. (1993)Lawson 1996 Process data onlyTucker et al 1996 Similar to 1993 with more comprehensiveTreloar 1998 Similar to 1996 with 1992-3 input-output data hydbrid analysis


23

There are several publications that calculate embodied energy data for Australia

(Table 5) (Australian Greenhouse Office, 1999). Each method has drawbacks either

in the methodology or the number of materials and products investigated. The study

completed by Treloar (1998) is currently under development to become the most

comprehensive listing of embodied energy data, in the form of embodied energy

coefficients. At present it has been deemed that this database needs to be refined to

improve the reliability of the following components; direct energy in construction,

processes which combine basic materials, services such as banking and areas where

non-greenhouse gas emissions are modelled (Australian Greenhouse Office, 1999).

However, at present this list of embodied energy coefficients is not publicly available.

As a result, the most comprehensive database of embodied energy coefficients

available for general use and applicable to Australia was prepared by Alcorn (1998) in

New Zealand. This investigation used the recommended hybrid input-output analysis

method (Alcorn, 1998). A comparison between this data and other published overseas

data is given in Table 6. The variability between data is evident and must be

considered in any analysis.

Table 6: Comparison of Published Embodied Energy Coefficients. Published lowest and highest

values are listed against the data supplied by Alcorn (1998).

Material MJ/kg High LowAluminium, Vigin 191 241* 170^

Steel, Virgin 32.0 45.0* 10.4*

Zinc 51 51^

Timber, softwood, kiln dried, dressed 2.5 6.1 4.0^

Cement 6.9 9.4* 7.0^

Clay brick 2.5 9.3* 1.7^

Plaster, gypsum 4.5 3.4^

Concrete ready mix 17.5 Mpa 1.0 2.3^ 0.54#

Ceramic Tiles 2.5 6.3+

Glass 15.9 15.0^ 12.7Insulation, fibreglass 30.3 30.3* 18.3#

*(The American Institute of Architects, 1994)+(Franklin Associates Ltd, 1991)

^(Lawson, 1994)#(Sheltair Scientific Ltd, 1991)

MODELS FOR EMBODIED ENERGY ANALYSIS

Chen, Burnett and Chau (2001) developed a model for embodied energy analysis.

The total embodied energy is given as the sum of the energy used in manufacturing

(Em) and transporting (Et) building materials and production energy (Ep).


24

The energy use in the manufacture of materials takes into account the waste of

materials during building production and the replacement of materials in the lifetime

of the building by waste and replacement factors (_ and _ respectively) (Table 7).

The replacement factor is given by the ratio of the building lifespan to the average

lifespan of the given material (Table 8). To take into account the fact that technology

improvements will most probably lead to less recurring embodied energy, an annual

decrement rate may be added to the equation (Chen et al., 2001). For a given mass (m)

kg of each material the manufacturing energy is:

meEm µλ )1( += ( 23)

Where e is the embodied energy coefficient required for the manufacture of each

material (MJ/kg).

Table 7: Waste factors for different materials in the construction of buildings (Cole and Wong,

1996; Adalberth, 1997).

Material Waste factor Materials Waste factorAluminium 0.025 Polystyrene 0.05

Coatings (paint and laquers) 0.05 Polythene 0.05Concrete (reinforced) 0.025 Polyvinyl chloride (PVC) 0.05

Concrete (plain) 0.025 Steel 0.05Copper 0.025 Tiles and clinkers 0.025Glass 0 Timber (planed) 0.025

Gypsum wallboard 0.05 Timber (roughsaw) 0.025Mineral wool 0.05 Timber (shingles and shavings) 0.025

Table 8: The average lifespan of common building materials (McCoubrie and A., 1996;

Adalberth, 1997).

Building component Replacement factor Building component Replacement factorStructural (beams etc) 1.0 Plastic carpeting 2.4

External and interior walls 1.0 Ceiling finishes 2.0Flooring 1.0 Floor finishes 3.0

Windows and doors 1.3 Painting and wall papering 5.0Walls and roofing tiles 1.3 Others 1.2

The transportation energy includes the energy required for demolition (ed) as well as

transportation use. Difficulties in assessing the lifespan of a building and assessing

the form of demolition in the future make the cost of demolition difficult to calculate

(Yohanis and Norton, 2002). The average energy used in transportation (et) of each

material is a function of the ratio of imported materials to the total materials and the


25

transportation distance. Energy use for different modes of transportation are

presenting in Table 9, where it was assumed that local transport costs were negligible

in comparison to transportation from overseas. The energy used in various forms of

transportation has been estimated by a number of authors, but assuming that

technology advances will improve the energy efficiency of transportation vessels the

values quoted are the lowest amongst those found (Tillman et al., 1991; Sperling and

Shaheen, 1995; McCoubrie A., 1996; Miller, 1996). Consequently, the transportation

energy for a given material of mass m (kg) is given by:

)()1( dtt eemE ++= µλ( 24)

Table 9: Energy use in different modes of transportation - smallest from (Tillman et al., 1991;

Sperling and Shaheen, 1995; McCoubrie A., 1996; Miller, 1996).

Mode of transport Energy use (MJ/kgm)Deep-sea transport 0.216

Coastal vessel 0.468Truck 2.275

Class railroads 0.275

The energy required for the production processes per unit mass (ep) can be found in

Table 10. The total energy for each material is then:

pp meE =( 25)

Table 10: Energy use in installing and processing building components (Adalberth, 1997).

Type of Process Energy UseDrying of standard concrete 0.158 MJ/kg

Drying concrete element 0.900 MJ/kgLighting of construction objects 0.0072 MJ/kgHeating of construction objects 93.6 MJ/m2

Heating of sheds 50.4 MJ/m2


26

22 .. 33 TT AA KK II NN GG SS TT EE PP SS TT OO RR EE DD UU CC EE EE NN EE RR GG YY

CC OO NN SS UU MM PP TT II OO NN

2.3.1 REDUCING HEATING AND COOLING NEEDS

SOLAR PASSIVE DESIGN

Solar Passive Design (SPD) involves using the natural heating and cooling reagents

surrounding a building so that the building regulates its own temperature without the

need for mechanical energy inputs such as fans and pumps (Fisk, 1982). They are

“designs that consist of architectural features, components, and/or assemblages

thereof that make use of the natural transfer of solar-generated thermal energy for the

purposes of water heating, space heating and/or space cooling” (Dickinson and

Cheremisinoff, 1980).

A totally passive system will only rely on convection, conduction and radiation to

maintain a comfortable temperature range within the house (Fisk, 1982). A hybrid

system is one that has limited help from mechanical inputs such as fans to assist with

air circulation (Fisk, 1982).

There are two distinct type of SPD. The most common is a direct-gain system

whereby heat is gained directly through exposure to sunlight through large north

facing windows. The second type of system is the indirect-gain system where heat is

stored in the thermal mass of the house (the walls and floor) to be released into the

building at a later stage (Fisk, 1982).

The following sections will briefly describe commonly used solar passive design

techniques, which aim at maximising the energy efficiency of the building. The

methods range from changing the type of building materials used to adding shading

and insulation materials.

Thermal Mass


27

The thermal mass of a building stores heat during the day and releases heat slowly

during the night, helping to regulate the internal temperature. This process can work

conversely in the summer months (Fisk, 1982). The most commonly used materials to

provide thermal mass include concrete, adobe, stone, brick and contained water (Fisk,

1982).

Building site

The site and orientation of a building will have a significant impact on its exposure to

solar flux and seasonal winds (Holtz, 1982). The seasonal movement of the sun in

Perth, Western Australia is shown in Figure 10. The orientation of a household

should aim to maximise direct solar gains in summer and minimise them in winter

(Sustainable Energy Development Office, 2002). The majority of summer heat gain

is through the roof and east and west facing windows, whilst the north facing

windows will receive the most heat gain in winter. As a consequence it is best

practise to face all living areas to the north allowing maximum winter heating.

Figure 10: Seasonal Sun movement in Perth, Western Australia (Sustainable Energy

Development Office, 2002).

In the design of a house, the local seasonal wind pattern should be investigated. In

terms of maximum ventilation during the summer months in Perth, access to cool

summer winds from the southwest should be maximised through the use of windows

and appropriate landscaping (Sustainable Energy Development Office, 2002). Hot

north east summer winds should also be minimised.


28


29

Building Form

The shape of a building can have a profound influence on its energy performance

(Holtz, 1982). To maximise energy efficiency of residential buildings in Perth it is

recommended that north and south facing walls are roughly 1.5-2.0 times the length of

east west facing walls (Sustainable Energy Development Office, 2002).

Insulation

The need for heating and cooling may be limited through the use of wall, roof and

floor insulation (Holtz, 1982). In particular, due to the high conductivity of the

ceiling and roof, heat loss and gain can be significantly reduced through the use of

insulation. Insulation can be broken into two types; bulk insulation which works by

trapping pockets of air in the insulating material and reflective insulation which works

to maximise the reflection of light and heat (Sustainable Energy Development Office,

2002). The resistivity of common insulation materials is often quoted as the R-value,

with a higher R-value indicating greater resistivity to heat transfer (Figure 11).

Figure 11: R values or resistivity of common insulating materials (Sustainable Energy



30

Infiltration

Reducing natural and unwanted infiltration can also significantly improve the energy

efficiency of a house. An insulated home can still lose up to 25% of winter warmth

and 20% of summer heat from air leaks and draughts (Sustainable Energy

Development Office, 2002). Ensuring that windows and doors are well sealed and

choosing materials with low infiltration capacities are recommended solutions.

Glazing Area, Location and Type

The location, area and type of windows and skylights influences solar gains but must

also consider views, privacy and daylighting (Holtz, 1982). Solar passive design of

windows means maximising direct solar gain and minimising heat loss in winter. A

balance must also be struck to allow adequate ventilation from cross breezes.

Suggested rules of thumb include making a third to half of the building’s north face

glass, to minimise west and east facing windows and south facing windows should be

large enough to allow for cross ventilation without excessive heat loss in winter

(Sustainable Energy Development Office, 2002).

Double glazed windows consist of two panes of glass separated by 10mm of air.

These windows have low emissivity and hence reflect heat and light waves,

effectively retaining heat in winter and minimising heat gains in summer (Fisk, 1982).

However, double glazed windows will still allow significant solar gains under direct

sun and it is recommended that they be used in conjunction with shading devices

(Sustainable Energy Development Office, 2002).

Shading

Windows can be shaded in summer through the use of fixed overhangs, eaves or solar

pergolas and in winter through internal coverings such as blinds or curtains (Holtz,

1982). The recommended eave overhang for North facing windows is roughly equal

to 0.7 times the distance from the eaves overhang to the bottom of the window to

shade the windows from September until March (Sustainable Energy Development


31

Office, 2002). A solar pergola is designed to allow winter sun penetration whilst

blocking summer sun penetration. This is achieved through the use of angled louvres

as shown in Figure 12. East and west facing windows will only be fully protected

from the sun in summer through the use of vertical screening.

Figure 12: Solar pergola in winter and summer (Sustainable Energy Development Office, 2002).

Interior Spatial Arrangement

Where possible, building layout should be used to maximise the benefit from buffer

zones such as garages, utilities and storage rooms (Holtz, 1982). Inter-zonal airflow

can then be used to maximise energy efficiency. By grouping rooms with similar

usages together, unconditioned rooms can be placed together and the energy

consumption of the house reduced. Placing living areas to the north of the building

allows for maximum solar gains in winter and locating bedrooms on the south side of

the house ensures these rooms are cool at night.

2.3.2 REDUCING EMBODIED ENERGY

ALTERNATIVE BUILDING MATERIALS

The two main parameters that have a significant effect on the total embodied energy

of a house are the energy consumed in the manufacture of the materials and the

energy to transport the materials (Kreijger, 1987).


32

Traditionally, buildings were constructed from local materials. However, today’s

built environment has become increasingly internationalised, with building materials

being taken from multiple origins. When compared with typical concrete use, it was

demonstrated in a study in southern France the utilisation of in-situ local resources

decreased energy use by up to 215% and reduced transportation energy usages by up

to 453% (Morel et al., 2000). A UK study also showed that using local timber can

reduce embodied energy inputs by at least 70 times in comparison to imported timber

from tropical regions (Harris, 1999).

Morel et al. (2002) suggest that three steps be taken in order to ensure the

appropriateness of materials before design of a building. This involves firstly

establishing an inventory of materials available locally, then selecting the materials,

and finally designing the building form. Using local materials will significantly

reduce the energy required to transport the materials, and hence the embodied energy.

By selecting alternative building materials that have low embodied energy in

manufacturing, the embodied energy of a house can also be significantly decrease.

The use of recycled products, such as recycled aluminium frames, brick or concrete is

one method of achieving this. Another method is to replace metals with high

embodied energy, such as steel and aluminium, with local timber. Replacing double

brick walls with brick veneer will also significantly reduce the component embodied

energy but must be balanced with the appropriate thermal mass required for solar

passive design.

22 .. 44 QQ UU AA NN TT II FF YY II NN GG EE NN EE RR GG YY CC OO NN SS UU MM PP TT II OO NN

2.4.1 LIFE CYCLE ASSESSMENT (LCA)

Creating a balance between embodied energy reduction and on-going energy

reduction through the use of solar passive design is the central aim of life cycle

assessment (LCA). The LCA of a whole household involves examining the total

environmental impact of all materials from their abstraction to their use in a building

(Yohanis and Norton, 2002). In terms of energy consumption,. this involves the


33

assessment of the total embodied and on-going energy consumption of the house over

its lifetime.

The environmental impacts that are generally considered when undertaking LCA

include greenhouse emissions, waste generation, energy use, water use and resource

depletion (Yohanis and Norton, 2002). International standards defining accepted

LCA methodologies and protocols can be found in ISO 14040 Environmental

Management – LCA Principles and framework (Yohanis and Norton, 2002).

2.4.2 MODELLING

Although a range of modelling tools have been developed to determine various

elements of the environmental impact of buildings, no single tool that is capable of

assessing the total environmental impact of a project exists (Australian Greenhouse

Office, 2001). Whilst both the Australian government and other international

institutes have recognised the need for sustainable practises, the necessary design

tools to support decision makers have not been developed. It has been suggested that

if LCA is to become universal practise, the development of “robust and credible LCA

software with reliable data on a wide range of construction materials” is necessary

(Dowling, 2002).

In order to quantify on-going energy savings, the challenge to develop numerical

models that predict and evaluate the thermal performance of a building is now being

met. Dikinson et al. (1980) points out that although passive solar designs are

conceptually and constructurally simple, analysing, predicting and evaluating, their

thermal performance is relatively complex. Whilst traditionally, modelling tools have

been used by architects to evaluate the building’s engineering performance at the final

stages of design, design tools that aid the design process are now being developed to

ensure that buildings meet sustainability requirements (Marsh, 2002b).

The types of thermal models available can be divided into three broad categories of

steady state, response methods and finite difference and finite element numerical

methods.


34

STEADY STATE

Steady state models use U-values to calculate an overall building loss coefficient, and

then multiply that by monthly or annual temperature figures to estimate total heat

losses and gains (Marsh, 2002a). The downfall of these models is that they fail to

incorporate the dynamics of direct solar gains, casual gains, long-wave radiation

exchange or plant operation. This is especially important in climates with a large

diurnal temperature range.

RESPONSE FUNCTION METHOD

q(x,t) è(x,t)

0 l x

Figure 13: A homogeneous isotrophic element.

The Fourier heat flux equation (26) can be solved analytically by the application of

Laplace transforms through a three stage process (Clarke, 2001). The equation is

transformed and then solved by algebraic manipulation. Finally, the inverse Laplace

transform is applied to find the solution in the original time domain. In applying the

inverse transform two distinct modelling methods have developed, the time-domain

response function and the frequency domain response function. The time-domain

method calculates the response of multi-layered constructions to temperature time

series, whilst the frequency domain method calculates the response to periodic

excitations of differing frequencies.

tx ∂∂

Κ=

∂∂ θθ 1

2

2

( 26)

The major assumption underlying the frequency domain response function method is

that the weather time series can be represented as a series of periodic cycles (Clarke,

2001). The weather time series f(t) is broken into a series of sine and cosine functions

defined by the harmonic frequency (1/L) and time (t):


35

)/2cos()/2sin()(11

LmtbLmtaatFk

mm

k

mmo ππ ∑∑

==

++= ( 27)

The fundamental harmonic is usually 24hours, with other harmonics having smaller

periods. Each harmonic is processed separately and using the principle of

superposition the system response is obtained by the sum of these with respect to the

mean condition.

The limitation of this method is that some energy transfer may be crudely estimated,

such as longwave radiation and casual gains whose square profile will be clipped with

a sinusoidal approximation. The method also has limitations when considering the

interaction of coupled processes such as HVAC systems, which do not lend

themselves to the principle of superposition (Clarke, 2001). The means and swings

technique (Danter, 1960) defines three response factors that determine the response

time of each process through a phase angle.

The Chartered Institute of Building Services Engineers (CIBSE) has adopted this

technique and developed from Danter’s (1960) ‘means and swings’ technique what is

now known as the admittance method (Loudon, 1968). The admittance method

allows for the estimation of flux transfer under steady cyclic conditions where the

external temperature variations are repeated over time (Loudon, 1968). The 24-hour

period harmonic is used to determine the response factor, which is then applied to the

actual temperature of the building. The major assumption of this method being that

the internal temperature of any building will always tend towards the local 24-hour

mean outdoor temperature (Marsh, 2002a).

FINITE DIFFERENCE AND FINITE ELEMENT METHOD

Finite difference and finite element models, whilst computationally intensive, involve

a higher degree of accuracy than previously defined methods. These methods

subdividing either each material into multiple equidistant layers or surfaces into

segments in a 3D grid (Marsh, 2002a).


36

The distinct advantage of these techniques over the response method is that their

generality allows the coupling of heat flux domains (Clarke, 2001). This is

particularly important when modelling the interaction of heating and cooling systems

with other heat flow processes. However, these models are more difficult to validate

than the response function method (Clarke, 2001). The response function method is

the outcome of many years of accumulated research and development, whilst the

finite difference and finite element methods are still under investigation and require

further refining before simple applications can be made.

MODEL REVIEWS

A range of modelling and rating tools for evaluating thermal performance of houses

have been developed for Australian climates and conditions. The following are a

selection of tools intended for use in the conceptual and design phase of residential

houses both nationally and internationally.

Australian Models

House Energy Rating Schemes and Related Models

House Energy Rating Schemes (HERS) are currently being introduced around

Australia (Australian Greenhouse Office, 2001). HERS aims to encourage improved

building envelope design. These tools rate building thermal performance using

predicted heating and cooling energy demands, but do not take into account the

embodied energy or water heating energy consumption. At present NABERS

(National Australian Building Environmental Rating Scheme), based on a culmination

of the models below, is being developed and is hoped to be introduced in conjunction

with the amended Building Code of Australia in January 2003 (ABCB, 2002).

Most rating systems (NatHERS, FirstRate, QuickRate, BERS, Q Rate and

ACTHERS) are based on CheeNATH developed by the CSIRO (Australian

Greenhouse Office, 2001). The CheeNATH engine is based on the response function

method and hence encompasses the limitations of this theory (Marsh, 2002a). It also

is limited in the number of zones it can analyse.


37

NatHERS (Nationwide House Energy Rating Software) is currently the most

commonly used HERS and has been widely tested and calibrated to give consistent

results for most climatic zones (Australian Greenhouse Office, 2001). It is a software

tool developed to simulate the operational energy use in the home. The scheme is

useful for making fast comparisons between building designs and displays results

graphically. Following a simulation, NatHERS outputs a report giving the energy

requirements of the house and a star rating out of 5. The model can simulate

mechanical heating and cooling or natural ventilation temperatures.

NatHERS requires the user to input material and dimensions of each building element

via tables. Climate forcing data is selected from 27 climatic zones determined by the

user inputted postcode (Environment Australia, 2000). NatHERS does not allow the

user to change internal heat, thermostat settings, schedules for heating and cooling or

energy requirements for heating and cooling (Environment Australia, 2000.)

BERS (Building Energy Rating Scheme) developed by Solar Logic gives the most

accurate results in tropical climates and simulates operational energy in the home

(Australian Greenhouse Office, 2001). This scheme allows the user to select whether

mechanical cooling will be used and can also assess a number of natural ventilation

options. It also uses data entry on a drawing interface whereas other schemes require

numerical inputs of all data. BERS provides a comfort index based on temperature,

air speed, relative humidity and acclimatisation for natural ventilation settings. Qrate

has been developed along the same line to give a faster, simpler rating for a range of

users.

FirstRate, Qrate, ACTHERS and Quick Rate are correlation programs which do not

simulate operational energy they assume that cooling will be used above thermal

comfort levels which can be misleading when mechanical cooling is not used

(Australian Greenhouse Office, 2001). The Sustainable Energy Authority Victoria

developed FirstRate and the faster QuickRate.

The Windows Energy Rating Scheme (WERS) rates the energy impact of residential

windows anywhere in Australia (Australian Greenhouse Office, 2001).


38

ECOTECT

Dr Andrew Marsh from the School of Architecture at the University of Western

Australia developed ECOTECT (Marsh, 2002a). ECOTECT uses a CAD type user

interface so that the designer can input the basic building geometry and materials and

is a professional tool, which requires substantial skill on the part of the operator

(Environment Australia, 2000). The model has a comprehensive materials library and

climatic data from cities around the world. The CIBSE Admittance Method is used to

determine internal temperatures and heat loads combined with shading and over-

shadowing pre-calculations (Marsh, 2002a). In addition to thermal analysis, the

model also has shadow, lighting acoustic and cost analysis functions.

The advantages of this model are in its flexibility placing no restrictions on building

geometry or the number of thermal zones that can be simultaneously analysed (Marsh,

2002a). ECOTECT has also been designed to output files to DOE2, CheeNATH and

LCAid and can calculate lifecycle embodied energy and greenhouse emissions given

user inputted values.

LCAid is a design tool developed by Andrew Marsh to be used in integration with

ECOTECT as a Dynamic Data Exchange (DDE) and Bousted databases to allow LCA

information to be more accessible to engineers and architects (Environment Australia,

2000). It outputs the following eco-indicators of alternative designs: life cycle

greenhouse gas emissions, life cycle embodied energy, ozone depletion,

nutriphication, heavy metals, acidification, summer/winter smog, carcinogens, solid

wastes, water consumption and primary fuels (Environment Australia, 2000).

International Models

ESP-II

ESP-II is a US model comprising five computer programs that estimate the energy

consumption of a building. The model accounts for site location, building structure

and the type of conditioning system installed (Marsh, 2002a). It enables a designer to

investigate many alternatives and make energy comparisons quickly and effectively

for a very wide range of building configurations and air conditioning systems using

measured climatic data (Marsh, 2002a).


39

TAS

TAS is a UK CAD based model and is reputed as a powerful design tool in the

optimisation of a building’s environmental and energy performance (Marsh, 2002a).

The model’s performance has been validated against the International Energy Agency

Data and contains comprehensive materials, schedules and climate databases

(Environmental Design Solutions Limited, 2002).

TAS performs dynamic building simulation with integrated natural and forced airflow

and can be coupled with a conditioning systems simulator (Environmental Design

Solutions Limited, 2002). The model outputs plant sizing and total energy demand.

The main difference between the dynamic procedure used and the CIBSE steady state

and admittance procedures is the ability to account for variations in weather over a

number of days.

EnergyPlus

EnergyPlus, the culmination of two models formally known as DOE2 and BLAST,

was developed by the US Department of Energy and is based on ASHRAE

methodology. The model is designed for air-conditioned commercial, institutional

and residential buildings (Environment Australia, 2000). The model uses a

description of the building layout, conditioning system and utility rates and weather

data to perform an hourly simulation of the building and to estimate energy bills

(Environment Australia, 2000).

BLAST (Building Loads Analysis and System Thermodynamics) is based on the

fundamental heat balance method and is the industry standard for heating and cooling

load calculations (Marsh, 2002a). BLAST output may be utilized in conjunction with

the LCCID (Life Cycle Cost in Design) program to perform an economic analysis of

the building (Marsh, 2002a).

Building Design Advisor

“The Building Design Advisor (BDA) is a computer program that supports the

integrated use of multiple building simulation and analysis tools through a single,


40

object-based representation of building components and systems” (Papamichael,

2002). In essence, the software aims at simplifying the input process for users to

increase the accessibility of analysis tools such as EnergyPlus. The program uses a

CAD based graphical user interface to three separate daylighting, energy and thermal

analysis tools. The model also has a library of alternative materials.

ESP-r

ESP-r is a dynamic thermal simulation tool developed in Scotland at the University of

Glasgow (Marsh, 2002a). The model has the capacity to assess energy use, air flows

and the role of conditioning and heating systems. It allows researchers and designers

to assess the manner in which weather patterns, occupant interactions, design

parameter changes and control systems affect energy requirements and environmental

states (Marsh, 2002a). ESP-r is designed for use with Unix systems.


41

33 MM oo dd ee ll BB aa cc kk gg rr oo uu nn ddAs the only whole building assessment model designed in Australia, ECOTECT was

selected as a tool for predicting the thermal performance of the project home. This

section outlines the theory behind the ECOTECT model.

One of the main motivations behind ECOTECT was the need for a design tool that

enables architects to factor environmental impacts into the design process without

extensive data input (Marsh and Carruthers, 1995). The model’s intended use is by

architects or designers in the early stages of building design (Marsh, 2002b).

33 .. 11 MM OO DD EE LL AA LL GG OO RR II TT HH MM SS

ECOTECT is a three dimensional model that uses a frequency domain response

function integrative modelling method, commonly referred to as the admittance

method, to determine internal temperatures and heat loads (Clarke, 2001).

3.1.1 STEADY STATE HEAT BALANCE

The heat entering or leaving a building at steady state is estimated by the sum of the

following heat gains; conduction (Qc), ventilation (Qv), solar (Qs), internal (Qi) and

evaporative (Qe) (Marsh, 2002a):

Qc + Qv + Qs + Qi + Qe = 0(28)

So-called “conduction gains” are the combined conductive, convective and radiative

gains as given by the U-value and the difference in temperature between inside and

outside (__):

Qc=UA __ ( 29)

Ventilation gains via both natural ventilation and infiltration are a function of the

effective ventilation rate (N) or the number of air changes per hour and the volume of

the thermal zone (V):


42

Qv=0.33NV __ ( 30)

Ventilation gains are varied by the user through the use of an infiltration rate which

ranges from 0.1 for well sealed buildings to 1.0 and wind sensitivity, which

determines the sensitivity of the zone to external wind speed (Marsh, 2002a). These

values act in addition to the effect of windows and doors, which is calculated

separately.

Solar gains can be broken in to direct and indirect components. Direct solar radiation

is given by the mean total solar irradiance impinging on the windows (G) by the sunlit

area of glass (A) and the mean solar gain factor (sgf) (Marsh, 2002a):

Qs = GAsgf(31)

Where the solar gain factor is a value between 0 and 1 dependant on the angle of solar

incidence and the window and blind properties that are used to determine the portion

of solar flux that penetrates the boundary (Clarke, 2001).

The indirect solar gains through opaque building materials are estimated using the sol-

air temperature of the external surface (_s):

_s= _o +GabsRso ( 32)

Where abs is the surface absorption of the material (0-1), a function of the material

colour and type and Rso is the outside air film resistance. The excess gains above

conduction are then given as:

Qs = UA (GabsRso)(33)

Where A is the total area of opaque surfaces (Marsh, 2002a).

The mean internal gains are the sum of instantaneous casual gains from people (Table

11) and sensible heat gains from lighting and equipment (Table 12)). Schedules of

occupancy and equipment use determine the heat load at any hour of the day and the

mean internal heat gain is given by the instantaneous gain (W) multiplied by the hours

of application divided by 24.


43

Table 11: Internal heat gains per person according to activity rate (Marsh, 2002a).

Activity Rate (W)Sedentary 70Walking 80

Exercising 100Strenuous 150

Table 12: Typical sensible heat gains from equipment (Marsh, 2002a).

Type W/m2

Lighting 20Equipment 40

TOTAL 60

Evaporative heat losses are a function of the latent heat of water and the evaporation

rate (ev):

Qe = 666.66ev (34)

The steady state heat balance equation can then be solved to give the mean internal

temperature (_i).

3.1.2 ADMITTANCE METHOD

The Admittance Method then calculates the dynamic swing in internal temperature by

using the theory of superposition to add the swing in heat gains from each component.

The swing in effective solar heat gains is:

_Qs= sgfaA (Gp - G) ( 35)

Where sgfa is the alternating solar gain factor and Gp is the peak intensity of solar

radiation.

The swing in indirect solar gains is:

_Qs=dAU(_s’- _s) ( 36)

Where d is the decrement factor, _s’ is the sol-air temperature at the time of the peak

less the time lag and _s is the mean sol-air temperature. The decrement response

factor is the ratio of the cyclic flux to the steady state flux and is applied to

fluctuations about the mean external temperature or flux impinging on opaque

surfaces (Clarke, 2001):


44

d=q(L,t)/U_(0,t) ( 37)

The factor gives the related fluctuation within the building at a point later in time

dependant on the time lag (Figure 14).

time

0-1

0-1

x l 0

q(l,t)

No change in frequency implied

q(0, t+? t)

Figure 14: The effect of the decrement factor and time lag (Clarke, 2001).

The swing in internal gains is given by:

_Qi=Qi’-Qi ( 38)

Where Qi’ is the internal gains at the peak hour.

The swing in ventilation and conduction gains is given by:

_Qc=(AU+0.33NV) __o ( 39)

Where __o is the change in external temperature.

The total swing in heat gains (_Qt) is then calculated to give the swing in internal

temperatures (__i) by:

_Qt= (UAa+ 0.33NV) __i ( 40)

Where a is the admittance response factor, the amount of energy entering a surface for

each degree of temperature swing. This factor gives the temperature swing about the

mean due to the cyclic heat load on the building. The swing in internal temperature is

then applied to the steady state internal temperature for each time step to calculate the

predicted environmental temperatures.


45

33 .. 22 GG RR AA PP HH II CC AA LL UU SS EE RR II NN TT EE RR FF AA CC EE (( GG UU II ))

ECOTECT’s Graphical User Interface (GUI) is designed to make data entry simple

and flexible. The major components include a 3D drawing interface, a materials

library, a schedule editor and a weather data manipulator.

3.2.1 3D DRAWING INTERFACE

A 3D CAD like drawing interface allows the user to enter and edit the building layout

and structure. Each object entered is assigned to a thermal zone, which represents a

shared air space and is also indexed against a material type (Marsh, 2002a). The

program defines objects as a particular building element from the following:

Void – the null default, which does not have mass or restrict air flow

Roof – defined as a plane

Floor – defines the boundaries of zones and used to calculate the floor area

Ceiling – defines the upper boundaries of zones

Wall – defines the vertical boundaries of zones. Can be any shape or angle.

Internal Partition – fully internal wall objects

Window – either individual or child objects

Panel – an area of different material within another object

Door – can be transparent or semi-transparent

Point – single node points used to calculate the response of an area

Line – can be a cable or measuring device

3.2.2 MATERIALS LIBRARY

The ECOTECT materials library is a comprehensive reference list of common

building material types which can be selected and assigned to objects in the model.

The library contains reference for multi-layered materials, such as double brick walls

with internal plaster, but also allows the user to specify different combinations.

Given the width, density, specific heat, conductivity and hatch of specific materials,

ECOTECT calculates the U-value, admittance, solar absorption, transparency and

thermal decrement for a complete building component. For example, given the


46

specific thermal properties of each component in a double brick wall ECOTECT will

compute the combined thermal properties for the complete wall. The only exception

to this is that ECOTECT requires the user to input the thermal lag of the building

component separately.

3.2.3 SCHEDULE EDITOR

Internal sensible heat gains are determined through the use of both schedules and

static loads. The user may define schedules for occupancy and equipment use through

the use of the schedule editor. The editor contains many standard schedules including

standard weekends and public holidays for different countries and states.

The user defines the maximum occupancy of the building and the maximum sensible

heat loads from other sources in the zone properties dialog box. The schedule editor

will then allow the user to define the percentage of the maximum use throughout the

day for standard weekdays, weekends and public holidays. An example of a schedule

is shown in Figure 15.

%

10

20

30

40

50

60

70

80

90

0 2 4 6 8 10 12 14 16 1 8 2 0 22 24

H ourly Ope rational P rofile

Figure 15: Schedule editor.

3.2.4 ECOTECT WEATHER TOOL

ECOTECT simulates a building’s response to external temperature changes and solar

loads through the application of climatic data as forcing functions. The model

contains compiled weather data for 35 geographical locations, including Perth,

Western Australia. However, when not available the Weather Tool allows the user to

convert standard weather files from a variety of formats for use in ECOTECT. The

tool converts hourly temperature, relative humidity, solar radiation, wind and


47

cloudiness data over a minimum one year perioid to averages for use in the model.

Climatic data is then graphically displayed for confirmation. The weather tool also

allows editing of the data and can search for a range of maxima and minima

conditions.

33 .. 33 TT HH EE RR MM AA LL AA NN AA LL YY SS II SS

The “thermal analysis” function of ECOTECT generates internal temperature and

fluxes for a given thermal zone. ECOTECT simulates heat fluxes and temperature

and presents the results graphically. The heat gains calculated are conduction,

ventilation, direct and indirect solar radiation and internal gains.

ECOTECT allows each thermal zone to be regulated under 5 heating and cooling

regimes for user defined time schedules; natural ventilation, mixed mode, full air

conditioning, heating only and cooling only. The natural ventilation scenario assumes

that the user will open and close windows and doors when the external temperature

conditions are more favourable than internal condition, whilst the mixed mode setting

combines the use of natural ventilation with mechanical heating and cooling devices.

In other words, the model assumes that when internal conditions fall outside the

specified thermal comfort range the occupants will firstly open the windows and

doors if beneficial, or else use mechanical means. The model will only calculate the

energy use for appliances between user specified on and off times.

Heating and cooling loads are calculated by reversing the procedure for internal

temperature prediction to compute the plant capacity required to maintain specified

temperature conditions (Clarke, 2001). As this method of calculating heating and

cooling loads does not include any information about the system, this calculation

should be considered as a space load rather than an energy load (Marsh, 2002a).


48

44 MM oo dd ee ll VV aa ll ii dd aa tt ii oo nnThe ability of ECOTECT to simulate the thermal response of a building was validated

by comparing simulated results with data from an instrumented building, the Murdoch

Environmental Technology Centre. The Centre consists of three freestanding

buildings designed with passive solar principles in mind. The buildings do not use

heating or cooling devices but rely on thermal mass in the rammed earth walls and

exposed concrete floors to regulate the temperature. The Centre also utilises external

shading devices to regulate heat transfer via direct radiation in summer. The thermal

performance of the three buildings is currently being monitored by thermistors, which

record internal temperatures. The Centre is also conveniently located 50m from the

Murdoch meteorological station.

44 .. 11 MM OO DD EE LL CC OO NN FF II GG UU RR AA TT II OO NN

4.1.1 BUILDING LAYOUT AND MATERIALS

Architectural plans of the Murdoch Environmental Technology Centre (Appendix A)

were used to configure the model in ECOTECT. This model had been used in the

design stages of the Centre to give preliminary predictions of the thermal behaviour of

the building and formed the basis for the modelling performed in this project.

The model of the Environmental Technology Centre was divided into 5 thermal

zones, each thermal zone representing a self-contained air space; office, analysis lab,

sampling lab, kitchen and hall (Figure 16). All buildings have vaulted ceilings so the

roof space was defined as the vertical limit of each thermal zone (Figure 17). This

required dividing the roof of the office building into three sections corresponding to

the office, hall and kitchen areas. External shading devices were defined as “outside”

and were used for shading calculations. A grid of mesh size 1500mm x 1500mm x

1500mm was used, with the Centre facing due north.

Table 13: Total areas and volume of the modelled thermal zones.

Office Analysis Lab Sampling LabTotal Area 269.35 m_ 193.23 m_ 214.76 m_Floor Area 46.79 m_ 45.90 m_ 45.90 m_


49

Volume 168.84 m_ 70.94 m_ 127.72 m_

Figure 16: Model Grid used for Murdoch Environmental Technology Centre. Five different thermal

zones were defined; the kitchen, hall, office, analysis lab and sampling lab. Remaining infrastructure

KITCHENOFFICE

HALL

ANALYSIS

LAB

SAMPLING

LAB

EXTERNAL

SHADING

ANALYSIS LAB SAMPLING LAB OFFICE


50

was defined as external shading.

The building was modelled using the natural ventilation setting. The thermal comfort

range for was set to 18-26˚C when the room was occupied. The office was modelled

with a maximum occupancy of 5 people between the hours of 9am to 5pm Monday to

Friday and zero occupancy on weekends and public holidays. The Analysis and

Sampling Lab were modelled with the same schedule for a maximum occupancy of 2

people each. The air infiltration and wind sensitivity of the building were both

initiated at a value of 0.1 (Table 15).

Table 14: Initial model parameter settings for the Murdoch Environmental Technology Centre.

Parameter SettingComfort band 18-26˚CMaximum number of people Office: 5; Labs: 2Occupancy Schedule 100%: 9:00-17:00 weekdays

0% : weekendsActivity Rate Sedentary (70W / person)Sensible Heat Gains 50 W/m2

Infiltration rate 0.25 (Air changes per hour)Wind Sensitivity 0.25 (Air changes per hour)

The model was configured to calculate the internal temperature of each thermal zones

for the period of 1st July 2001 to 1st July 2002.

Figure 17: Office building with the roof divided into the office (red), hall (cyan) and kitchen

(yellow) thermal zones.

Parameters representing the thermal properties of building materials were compiled

from the ECOTECT material library and major components were crosschecked

against reputed references (Australian Greenhouse Office, 2001; Clarke, 2001) (Table


51

15). The thermal lag of previously undefined components such as rammed earth walls

were obtained from published data (Australian Greenhouse Office, 2001). A

completed list of material components and definitions can be found in Appendix B.

Table 15: Thermal Properties of building materials used to model the Murdoch Environmental

Technology Centre (Marsh, 2000).

ComponentU-value

(W/m2K)Admittance

(W/m2K)

SolarAbsorption

(0-1)

Transparency(0-1)

ThermalDecrement

(0-1)

Thermal Lag(hr)

Concrete floor 3 5.2 1 0 0.7 5Glass SlidingDoor

5.356 5.36 1 0.95 0.34 0.39

Roller Door 5.55 5.57 0.966 0.95 1 0.39Wooden Door 2.36 3.9 1 0 1 0.4Partitions 0.853 4.4 1 0 0.44 7.7Zinc alumRoof

0.13 1.01 0 0 1.02 2

Rammed EarthWalls

2.86 5.56 1 0 0.22 10.3*

Windows 5.46 6 0.9 0.95Refractive

Index = 1.52Alt Solar gain

= 0.47

* (Australian Greenhouse Office, 2001)

4.1.2 FORCING DATA

Climate data from the Murdoch Meteorological Station for the year July 2001 to June

2002 was used to force the model. The station is 50m from the study site. Hourly

values of air temperature, wind speed and direction, solar radiation, relative humidity

and rainfall were used in the model (Figure 20). The daily maximum, minimum and

mean temperature and the monthly average solar radiation were calculated for

analysis (Figure 18 and Figure 19).

The study site is at a longitude of -32˚ and latitude of 155.8˚, Perth, Western

Australia. The local terrain was defined as suburban to account for shading from

adjacent buildings.


52

5

10

15

20

25

30

35

J F M A M J J A S O N D

C

Outside Temperature

Maximum

Minimum

Mean

Figure 18: Maximum, minimum and mean monthly air temperatures at Murdoch MET station

from June 2001 – June 2002.

0

100

200

300

400


W/m2

Average Solar Radiation

Figure 19: Monthly average solar radiation at Murdoch MET station from June 2001- June 2002.


53

Dry Bulb Temperature

0

10

20

30

40

J A S O N D J F M A M J J

Deg

rees

C

Total Solar Radiation

0

500

1000

1500

2000

J A S O N D J F M A M J

W/m

2

Relative Humidity

0

20

40

60

80

100

J J S N D F A M J S

Rainfall

0

5

10

15

20

25


mm

Evaporation

0

0.5

1

1.5

2


mm

Figure 20: Climate data at Murdoch from July 2001 – June 2002.


54

4.1.3 VALIDATION DATA

ECOTECT was validated against data from the Environmental Technology Centre’s

Office, Sampling Lab and Analysis Lab. The temperature data were also

independently analysed to determine the thermal comfort of the buildings before

modelling. The thermistor data from the office space were used to calculate the

relative contribution of the roof, walls and floor to internal thermal loads via

conduction.

Internal temperature data were recorded by thermistors in the Office, Analysis Lab

and Sampling Lab over the period of November 2001 to February 2002 (Figure 21)

and for the month of June 2002 (Figure 23). Thermistors were placed at sitting height

to best replicate human comfort levels in the buildings and temperatures were

measured at 10-minute intervals. In addition, thermistors were placed under the

flooring, atop of the concrete slab, under the solar collector and in the roof duct of the

Office to determine heat fluxes through each element (Figure 22).

Office Temp

0

10

20

30

40

O N D J F

Deg

rees

C

Analysis Lab Temp

0

10

20

30

40

O N D J F

Deg

rees

C

Sampling Lab Temp

0

10

20

30

40

O N D J F

Deg

rees

C

Outside Temperature

0

10

20

30

40

O N D J F

Deg

rees

C

Figure 21: Temperature data collected from the office, analysis lab and sampling lab over the

period Oct. 2001 – Feb. 2002. Temperature data is from the Murdoch MET station.


55

Office Temp

0

10

20

30

40

O N D J F

De

gre

es

C

Roof Duct Temp

0

20

40

60

80

O N D J F

Deg

rees

C

Solar Collector Temp

0

20

40

60

O N D J F

Deg

rees

C

Under Flooring 1

0

10

20

30

O N D J F

Deg

rees

C

Concrete Floor 1

0

10

20

30

O N D J F

Deg

rees

C

Concrete Floor 2

0

10

20

30

40

O N D J F

Deg

rees

C

Figure 22: Temperature readings from thermistors placed around the office space.


56

Off ice

0

510

15

2025

1-Jun 11-Jun 21-Jun 1-Jul

Analysis Lab

0

10

20

30


Sampling Lab

0

5

10

15

20

25


Outside

0

5

10

15

20

25


Figure 23: Temperature data collected from the office, analysis lab and sampling lab in June

2002. Temperature data is from the Murdoch MET station.

Following the removal of outliers from the internal temperature data (_i) the daily

maximum (_max) and minimum (_min) were extracted to calculate diurnal variation in

temperature (__i). From these values monthly mean and standard deviation in diurnal

temperature variation were then calculated (Figure 24).

minmax θθθ −=∆ i (41)


57

0

5

10

15

20

Oct Nov Dec Jan June

C

Sampling

Analysis

Office

Outside

Figure 24: Mean monthly diurnal temperature variation and standard deviation of the office,

analysis lab and sampling lab versus outside temperature.

In order to validate the model on a smaller time-scale, a one-week period in

November (21st-28th) (Figure 25) and a week in June (2nd-9th) (Figure 26) were chosen

to represent summer and winter conditions. Although not the hottest period,

November was used due to limitations of consistent data for all instrumented rooms

being unavailable at other periods.

0

510

15

20

2530

35

40

21-Nov 22-Nov 23-Nov 24-Nov 25-Nov 26-Nov 27-Nov 28-Nov 29-Nov

Deg

rees

C

Outside Air Temp

Sampling Lab Temp

Analysis Lab Temp

Office Temp

Figure 25: Temperature variation of the office, analysis lab, sampling lab and outside for 21-28th

November 2001.

0

5

10

15

20

25

30

2-Jun 3-Jun 4-Jun 5-Jun 6-Jun 7-Jun 8-Jun 9-Jun 10-Jun

Deg

rees

C Outside

Sampling Lab

Analysis Lab

Office


58

Figure 26: Temperature variation of the office, analysis lab, sampling lab and outside 2-9th June

2002.

THERMAL COMFORT

The thermal comfort range for humans was defined as 18-26ºC (Loudon, 1968).

Using the internal temperature data of each room (_) the degree hours that the

buildings were hotter (thot) or cooler (tcool) than this given range were calculated for

each month by the following relationships for each time step:

thot = (_ - 26)*(_t) ( 42)

tcold = (18-_)*(_t) ( 43)

Where _t is the time between measurements. Given that the building is generally only

occupied between the hours of 9am and 5pm these calculations were repeated to

determine comfort levels during times of use.

HEAT LOADS

Thermistor data from the Office building were analysed to determine the contribution

of each building component to internal heat loads. The contribution of the walls, roof

and floor (Qw, Qr and Qf) were used to estimate the internal temperature (_in). The

predicted internal temperature was then compared to the collected data to determine

the importance of these three components. The combined U-values (Uw, Ur and Uf)

and exposed surface areas (Aw, Ar and Af ) were used together with the equation at

time t:

Qw, t + Qf, t + Qr , t = Cp V(_in, t – _in, t-1)/(_t) (44)

Where Cp is 1.23kJ/m3K (Clarke, 2001) and the heat flux through each component is

given by:

Qw, t = UwAw (_out, t – _in, t) ( 45)

Qr, t = Ur Ar (_out, t – _in, t)(46)

Qf, t = Uf Af (_ground, t – _in, t)(47)


59

Where the external temperature of the concrete slab is measured by the thermistors

under the flooring (_ground). Hence Equation 44 can be rearranged to calculate the

internal temperature:

ffrrwwp

tinptgroundfftoutrrtoutwwtin

AUAUAUtVC

CAUAUAU

+++∆

+++= −1,,,,

,

θθθθθ

(48)

The predicted internal temperature was then plotted against the actual internal

temperature and analysed for discrepancies.

4.1.4 CALIBRATION

The model was calibrated by comparing simulated internal temperatures with

measured data for the period December 10th-11th. The model was calibrated by

adjusting the following parameters to minimise the difference between simulated and

observed internal temperatures:

Internal heat gains

Schedule of living

Wind sensitivity and air infiltration.

4.1.5 VALIDATION

ECOTECT was validated against data for the periods November 21st-28th and June

2nd-9th. The validity of ECOTECT to replicate internal conditions was match against

a list of criteria. The criteria were the models ability to replicate minimum and

maximum temperatures, diurnal temperature variations and to predict the levels of

thermal comfort under both summer and winter forcing conditions. Areas of

weakness were identified and improvements were recommended.

4.1.6 SENSITIVITY ANALYSIS

A sensitivity analysis was carried out to determine the sensitivity of the model to the

following parameters:


60

Material thermal characteristics: U-value, admittance, thermal decrement,

thermal lag, transparency and solar absorption.

Climate forcing data: air temperature, direct radiation, indirect radiation, wind

speed, relative humidity and cloudiness.

Internal heat gains and living schedules.

The sensitivity of the model was defined by the maximum variation from the mean.

For example the model was run with a U-value for the walls of 0 and 20, and the

variation from the base case of 2.7 was calculated.


61

44 .. 22 RR EE SS UU LL TT SS AA NN DD DD II SS CC UU SS SS II OO NN

4.2.1 DATA ANALYSIS RESULTS

Data Analysis was used to calculate heat loads and thermal comfort of the

Environmental Technology Centre. These results were then compared to model

predictions.

THERMAL COMFORT

People can comfortably operate in environments of 18-26˚C [Loudon, 1968 #58].

The ability of the building materials and design of the Environmental Technology

Centre to regulate internal temperatures to within this range is shown in Figure 27.

On average, during both the summer and winter periods both the Sampling and

Analysis Labs effectively regulate the temperature, throughout the day to less than

26˚C and greater than 18˚C. Whilst both buildings perform similarly during the

summer period, the Analysis Lab evidently has larger heat gains through the winter.

In contrast to this, the Office experiences large diurnal fluctuations largely driven by

external temperature changes that leads to the room being too hot in summer during

the day and too cool in winter during the night.

0 5 10 15 20 2510

15

20

25

30

35

HOUR

TEMP (C)

outside sampling laboff ice analysis lab

TOO COLD

TOO HOT

COMFORT ZONE

0 5 10 15 20 2510

15

20

25

30

35

HOUR

TEMP (C)

outside sampling laboff ice analysis laboff ice model

TOO HOT

COMFORT ZONE

TOO COLD

Figure 27: Hourly temperature averages from Nov 2001 to Jan 2002 and June 2002 in the

outside, office, sampling lab and analysis lab.


62

HEAT LOADS

The relative contribution of thermal conduction and radiation through the walls, floor

and ceiling to internal temperatures was shown by comparing the measured data to the

predicted internal temperatures using only heat inputs from these sources. In summer

(Dec 10th- Dec 14th) the predicted internal temperature replicated the actual

temperature of the office (Figure 28). During the winter (Jun 1st- Jun 9th), although

the predicted temperature mimics the patterns in internal gains, the predicted

temperature is consistently 1-2˚C lower.

During the summer the major differences, such as early morning peaks, may be

attributed to internal sources such people and office equipment. However, during the

winter other factors, such as direct solar radiation through windows appear to be more

important.

10

15

20

25

30

35

40

12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00

C

Predic ted

Measured

5

10

15

20

25

0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00

C

Predicted

Measured

Figure 28: Results of predicted temperature using only heat flux through walls, floor and roof

versus measured temperature data over a summer and winter period.

The relative contribution of the roof, floor and walls is shown in Figure 29-Figure 31.

In general, the relatively high resistivity of the insulated roof to heat transfer means

that heat transfer into and out of the room primarily occurs through the walls and

floor. The walls contribute the most during both the summer and winter periods.

During summer, at midday, the temperature outside is hotter than inside hence the

heat flux is through the walls and into the room. Once the room heats up heat flows


63

out through the walls when the external temperature falls below internal conditions.

The roof follows the same pattern but as the resistivity of the insulated roof is much

larger, the contribution to the heat flux is relatively small. The temperature under the

concrete floor stays relatively constant at a temperature of 23.4˚C (standard deviation

2.8). Hence before he room heats up the heat flows into the room from the concrete

slab. Once the room heats up the slab absorbs heat from the room, cooling it down.

-0.5

-0.3

-0.1

0.1

0.3

0.5

-15

-10

-5

0

5

10

15

20

25

Q floor

Outside Temp

Office Temp

C

positive Q

negative Q

W

11:2719:1710:4719:17

-0.5

-0.3

-0.1

0.1

0.3

0.5

-15

-5

5

15

25

Q floor

Outside Temp

Office Temp

C

positive Q

negative Q

W

10:20 16:4011:50 18:00

Figure 29: Heat flux and measured temperatures through the floor in winter and summer.

-0.5

-0.3

-0.1

0.1

0.3

0.5

-15

-10

-5

0

5

10

15

20

25

Q walls

Outside Temp

Office Temp

C

positive Q

negative Q

W

8:5715.:578:3716.37

-0.5

-0.3

-0.1

0.1

0.3

0.5

-15

-5

5

15

25

Q floor

Outside

Office Temp

Cpositive Q

negative Q

W

7:40 16:10 7:10

Figure 30: Heat flux and measured temperatures through the walls in winter and summer.

-0.5

-0.3

-0.1

0.1

0.3

0.5

-15

-10

-5

0

5

10

15

20

25

Q walls

Outside Temp

Office Temp

C

positive Q

negative Q

W

8:5715.:578:3716.37

-0.5

-0.3

-0.1

0.1

0.3

0.5

-15

-5

5

15

25

Q roof

Outside

Office Temp

C

positive Q

negative Q

W

7:40 16:10 7:10

Figure 31: Heat flux and measured temperatures through the roof in winter and summer.

During winter, daytime outside temperatures outside are warmer than internal

temperatures and hence the net heat flux is into the room through the walls and roof

until just past midday. During the night the temperature outside drops and as the

office stays relatively warm the net heat flux is out of the building. The temperature

x100 x100

x100

x100

x100

x100


64

under the concrete slab is 17.46˚C (standard deviation 0.7) and hence there is heat

flow into the room during the night when the room is cooler than this and heat flow

out of the room during the peak temperatures during the day.

Meteorological data of the soil temperatures at depth were compared with the

temperatures under the concrete slab of the office building (Figure 32). The

temperatures are roughly equal to that found at a soil depth of 1000mm. However, the

diurnal variations are more similar to that found at 500mm depth.

20

25

30

35

10/12/01 0:00 10/12/01 12:00 11/12/01 0:00 11/12/01 12:00 12/12/01 0:00

C

Measured SoilSurface125mm

250mm

500mm

1000mm

Figure 32: Soil temperature at depth in the soil (MET station) versus temperature under the

office floor (surface).

4.2.2 CALIBRATION

The model was calibrated by adjusting the internal loads and air change rates to

minimise the difference between simulated and measured internal temperatures for the

period Dec 10th – 11th . The adjustment of internal loads, a static quantity, was

achieved through two methods. These were, altering the magnitude of internal loads

through occupancy, office equipment, and refrigerators and secondly, by adjusting the

schedule as to when these loads occur (Table 13). The wind sensitivity and air

infiltration rates were also calibrated to 0.1 air changes per hour. Thermal lags were

also adjusted, however, their initial settings gave the best calibration.

Table 16: Calibrated internal heat loads.

Office Analysis Lab Sampling LabSchedule Occupancy and Sensible Heat Sensible Heat Sensible HeatMaximum Occupancy 5 0 0Activity Walking (80W) - -Sensible Heat Gains 0-60 W/m2 40-65 W/m2 40-65 W/m2

Max Total Heat Gains 68.5 W/m2 65 W/m2 65 W/m2


65

%

10

20

30

40

50

60

70

80

90

0 2 4 6 8 10 12 14 16 18 20 22 24

H ourly Operationa l Profile%

10

20

30

40

50

60

70

80

90

0 2 4 6 8 10 12 14 16 1 8 2 0 22 24

H ourly Ope rational P rofile

Figure 33: Hourly operational profile and schedule for weekdays and weekends.

The final calibration results are shown in Figure 34.

Office

-4

-2

0

2

4

0:00 0:00 0:00 0:00

del

T

10

15

20

25

30

35

0:00 12:00 0:00

model

actual

Analysis Lab

-1

-0.5

0

0.5

1

0:00 12:00 0:00 12:00

del

T

23

24

25

26

27

28

0:00 12:00 0:00 12:00

model

actual

Sampling Lab

-2

-1

0

1

2

12:00 0:00 12:00 0:00 12:00 0:00

del

T

2223

2425

2627

28

12/10/010:00

12/10/0112:00

12/11/010:00

12/11/0112:00

12/12/010:00

model

actual

Figure 34: Temperature difference between model predictions and actual temperature in the

office, analysis lab and sampling lab during a summer period.


66

4.2.3 VALIDATION

The validity of the predicted internal temperatures was assessed in terms of thermal

comfort, diurnal variation and maximum and minimum predicted temperatures for the

periods 2-9th June and 21-28th November. When compared to the levels calculated

from the measured data the predicted thermal comfort of each building is as expected.

The office experiences large diurnal variations and hence both over heats and over

cools in both summer and winter (Figure 35). The office is more effective at

regulating temperatures during the winter but suffer from over heating during the

middle of the day in summer. One would expect that this problem would worsen in

the months of January and February. In contrast, the model predicts effectively that

the Analysis Lab, the warmer of the two will experience occasional over-heating in

November and the Sampling Lab will experience occasional over cooling in winter.

-100

-50

0

50

100

150

200

250

300

Office

Sampli

ng

Analys

is

Office

Sampli

ng

Analys

is

Deg

ree

Hou

rs

Too Hot

Too Cool

JUNE NOV

Figure 35: Discomfort degree hours as predicted by the model for June and November.

The predicted diurnal temperature range was within the expected error range for the

Office (Figure 36). However, model predictions were slightly high in June and low in

November. This suggests that internal loads vary between the summer and winter

months. Possible explanations for this include increased heat emissions in summer

from people or variations in the usage patterns of the office over the seasons. The

model does not take into account long wave radiation reflected from external paved

surfaces or buildings. This may be significant in summer [Fisk, 1982 #26] and as the

calibration was done against a summer period the model may be overestimating

winter temperatures.


67

0

5

10

15

20

Nov June

C

Sampling

Analysis

Of fice

Model Off ice

Outside

Figure 36: Diurnal temperature variation as given by data from ETC.

The maximum and minimum predicted temperatures follow the expected trend over

the calibrated periods of 2-9th June and 21-28th November (Figure 37). The error from

the measured values was only 3% or a variance of ±0.6ºC.

20 22 24 26 28 3010

15

20

25

30

35

day

C

Sampling LabAnalysis LabOffice Office Model

2 4 6 8 1010

15

20

25

30

35

day

C

Sampling LabAnalysis LabOffice Office Model

Figure 37: Diurnal temperature variation and maximum and minimum temperature in

November and June.

On inspection, the predicted hourly temperatures for the month of June and November

match the measured data with some exceptions (Figure 38). The main difference

between the predicted and measured data is an inability to replicate temperatures on

the days of the 9th June, a Sunday and the 5th June, a Wednesday. It appears that the

given internal gains are applicable on the majority of days but can lead to under and

over estimates of temperature on days of increased or decreased activity. It is possible

that a conference or meeting was held in the building on the Sunday and that staff

were out of office on the Wednesday.


68

1012141618202224

6/2/020:00

6/3/020:00

6/4/020:00

6/5/020:00

6/6/020:00

6/7/020:00

6/8/020:00

6/9/020:00

6/10/020:00

6/11/020:00

Cmodel

actual

10

15

20

25

30

35

11/21/010:00

11/22/010:00

11/23/010:00

11/24/010:00

11/25/010:00

11/26/010:00

11/27/010:00

11/28/010:00

11/29/010:00

model

actual

Figure 38: Hourly temperatures predicted by the model versus the measured data for the Office

in June and November.

Other major differences between the observed and model data can be attributed to the

patchiness of the external temperature data. As the model is extremely sensitive to

external temperature values, jumps between hourly values will be reflected in the

amplified in the predicted temperatures. The model was designed to run with

averaged temperature data over a number of years, meaning that hourly data will run

in smooth curves. In order to validate the model over the specified period, long term

averages could not be used and hence lead to the forcing data containing occasional

jumps between hours. To validate ECOTECT on a smaller scale it is suggested that

the model be varied to allow for the use of forcing data at smaller time steps.

However, despite these differences the mean error of the estimated temperatures is

still less than 2%, indicating that the model is replicating internal conditions to a

reasonable degree of accuracy.

4.2.4 MODEL SENSITIVITY

The sensitivity of the predicted room temperature in the Office to changes in material

properties is summarised in Table 17. The sensitivty analysis was conducted over the


69

validated summer and winter periods. The shown plots are indicative of the changes

seen in both seasons unless indicated otherwise.

Given that the three major components contributing to heat transfer are the walls,

floor and ceiling, the analysis was focused on these areas. It was found that the model

was particularly sensitive to changes in wall and roof material properties, with

variations of up to 3ºC in the predicted temperatures. In contrast, the sensitivity of the

model predictions to changes in the floor properties was insignificant. This may be

attributed to the fact that the external forcing temperature under the concrete slab has

a smaller diurnal variation when compared to the outside temperature, which drives

heat flux through the roof and walls.

In terms of material properties, the predicted temperatures were insensitive to changes

in thermal lag, solar absorption and transparency. Large variance was, however, seen

with changes in thermal decrement and to a lesser degree U-value and Admittance.

Table 17: Sensitivity of predicted internal temperature outputs to changes in material properties

averaged over both summer and winter periods. Max and Min refer to the average variation

from the base case at the maximum and minimum range values.

CurrentValue

Tested Range Max (ºC) Min (ºC)

U-value (W/m2K) Walls 2.86 0-5 -0.2 0.5Floor 3.0 0-5 -0.0 +0.0Roof 0.13 0-5 -1.2 +0.0

Admittance (W/m2K) Walls 5.56 0-20 -0.1 +0.1Floor 5.2 0-20 -0.1 +0.0Roof 1.01 0-20 -0.1 +0.0

Thermal Lag (hr) Walls 10.3 2-13 -0.0 +0.0Floor 5 0-15 -0.0 +0.0Roof 2 0-10 -0.0 +0.0

Thermal Decrement Walls 0.22 0-1 -0.1 -2.9Solar Absorption Walls 1 0-1 -0.0 +0.0Transparency Walls 0 0-1 -0.0 +0.0Wind Sensitivity Office 0.1 0-1 -0.5 +0.1Air infiltration Office 0.1 0-1 -0.6 +0.1

The thermal decrement factor is defined as the ratio of cyclic flux transmission to the

steady state flux transmission [Clarke, 2001 #44]. A larger thermal decrement will

enhance the effective contribution of heat input due to structural gain of that

component. Increasing the thermal decrement increase the heat inputs from the given

component. In Figure 39 with an increase in thermal decrement a large peak 10 hours


70

after the external peak temperature can be seen. In this instance the contribution of

both heat gain and heat loss is increased and hence the resulting decrease in predicted

temperature. A longer thermal lag will mean that peak heat transfer will occur later.

Ideally, thermal lag will be designed to ensure maximum heat transfer occurs when

external temperatures are at a minimum. Hence, the model performed as expected,

demonstrating that a thermal lag of 13 hours will lead to a smaller diurnal temperature

range, than a small thermal lag time (Figure 39).

20

20.5

21

21.5

22

22.5

23

23.5

24

0:00 12:00 0:00

2 hours

10.3 hours

13 hours

14

16

18

20

22

24

0:00 12:00 0:00

0.5

0

1

Figure 39: Sensitivity of model predictions to changes in wall thermal lag and thermal

decrement.

An increase in U-values and Admittance, or a decrease in resistivity, leads to greater

heater transfer through the given building component. When heat transfer serves to

balance external temperature variations due a long thermal lag time, such as in the

rammed earth walls, an increase in U-value or Admittance should lead to lower peaks

and troughs in the predicted internal temperatures (Figure 40).

20

20.5

21

21.5

22

22.5

23

23.5

24

0:00 12:00 0:00

0 W/m2K

2.86 W/m2K

5 W/m2K

20

20.5

21

21.5

22

22.5

23

23.5

0:00 12:00 0:00

0 W/m2K

5.56 W/m2K

20 W/m2K

Figure 40: Sensitivity of model predictions to changes in wall U-values and admittance (W/m2K).

The model predictions were also sensitive to changes in the air infiltration rate and

wind sensitivity (Figure 41). Both of these parameters indicate the number of air


71

changes per hour due to air infiltration through vents, brickwork and other openings,

in addition to the effect of opening and closing doors and windows [Marsh, 2002

#19]. An increase in the infiltration rate resulted in the expected decrease in internal

temperatures in winter and increase in summer.

16

18

20

22

24

26

0:00 12:00 0:00

0

1

0.1

16

18

20

22

24

26

0:00 12:00 0:00

0

1

0.1

Figure 41: Sensitivity of model predictions to changes in air infiltration rate and wind sensitivity

(air changes per hour) in winter.

Changing the individual climate parameters indicated that model predictions are only

sensitive to the outside temperature, direct solar radiation and wind speed (Figure 42,

Figure 43 and Figure 44). Other parameters such as diffuse solar radiation, humidity

and cloudiness had an insignificant impact on model results (Table 18).

The model is particularly sensitive to changes in external temperature. A change of 1

degree will lead to a corresponding change of 1 degree in internal temperature.

Previous data analysis found that the major contributing factors to internal

temperature changes were structural gains through the walls, floor and roof, modelled

by temperature differences. Direct solar radiation only contributes to heat flux

through windows and hence would expect that the model be primarily sensitive to

changes in temperature. An increase in wind speed leads to a resultant decrease in

temperature as expected.

Table 18: Summary of model sensitivity to changes in forcing climate data.

Summer Winter Summer Winter±20% ±5ºC and 200Wh

Outside Temperature (˚C) ±3.5 ±1.8 ±5.0 ±5.0Direct Solar (Wh) -0.5 ±0.0 -0.3 -0.1Diffuse Solar (Wh) ±0.0 ±0.0Wind Speed (km/h) -0.3 -0.1Relative humidity ±0.0 ±0.0Cloudiness ±0.0 ±0.0


72

SUMMER

10

15

20

25

30

0:00 12:00 0:00

C+5

Base

-5

10

15

20

25

30

0:00 12:00 0:00

C

+20%

Base

-20%

WINTER

10

15

20

25

30

0:00 12:00 0:00

C

+5

Base

-5

10

15

20

25

30

0:00 12:00 0:00

C

+20%

Base

-20%

Figure 42: Sensitivity of model predictions to changes in external temperature.

SUMMER

14

16

18

20

22

24

26

28

0:00 12:00 0:00

+200

Base

-200

14

16

18

20

22

24

26

28

0:00 12:00 0:00

+20%

Base

-20%

WINTER

14

16

18

20

22

24

26

28

0:00 12:00 0:00

+200

Base

-200

14

16

18

20

22

24

26

28

0:00 12:00 0:00

+20%

Base

-20%

Figure 43: Sensitivity of model predictions to changes in direct solar radiation (Wh).


73

SUMMER WINTER

14

16

18

20

22

24

26

28

0:00 12:00 0:00

Base

Wind

Diffuse Solar

14

16

18

20

22

24

26

28

0:00 12:00 0:00

Base

Wind

Diffuse Solar

Figure 44: Sensitivity of model predictions to changes in wind speed (km/h) and diffuse solar

radiation (Wh) by 20%.

Model sensitivity to internal heat gains was particularly high (Figure 45). It must be

noted that in future modelling attempts it is important to accurately input internal heat

gains and schedules in order to predict internal temperatures, in fact, after temperature

this is the second most important parameter.

14

16

18

20

22

24

26

28

0:00 12:00 0:00

C

20%

Base

-20%

Figure 45: Sensitivity of model predictions to changes in internal heat gains (W/m2).

44 .. 33 CC OO NN CC LL UU SS II OO NN

Despite the fact that ECOTECT was not designed for validation against hourly data,

the performance of the model to replicate data and hence make predictions can be

considered adequate. The model accurately predicted level of thermal comfort, daily

maxima and minima and diurnal variation to within an acceptable degree of error.

Change ± 20% ± 10 W/m2

Error (˚C) ± 0.8 ± 0.3


74

Given that the model is designed for use with long-term averaged climate data in

order to predict the thermal comfort of new housing designs, the capacity for the

model to indicate effective design changes is evident.

However, the sensitivity of the model to both internal heat gains and external

temperature data must be recognised by the user before effective results may be seen.

Given that changing internal heat gains through both static sensible heat and user

occupancy can vary so much from day to day, it is important that in order to

accurately model the internal temperature of the house these variables must be

designed to best reflect the daily usage patterns of the house. This may pose a

problem to the designer, who may not be able to accurately predict the usage patterns

of a household, but it is suggested that consideration be taken of major appliances

such as fridges and major alterations in usage patterns from weekend to weekday. If

these assumptions are clearly stated and increased thermal performance is evaluated

through comparisons with a base case the model can be a useful tool.

Secondly, it is important that accurate, long-term averaged climate data be used to

force the model. ECOTECT come with averaged data for various cities around the

world, including Perth. It is recommended that this data be used to make predictions.

The validation of ECOTECT involved the utilisation of material properties as defined

in the ECOTECT Material Library. Although this data was varied, in particular the

thermal lag, during calibration efforts, it was found that the given values accurately

predicted internal temperatures and did not need to be altered. This finding means

that this library can be used with confidence in the following application exercise.


75

55 MM oo dd ee ll AA pp pp ll ii cc aa tt ii oo nnECOTECT was used to quantify the energy efficiency of a Dale Alcock project home,

Batavia, by predicting the ongoing energy costs due to heating and cooling of the

household. This was combined with embodied energy calculations to assess the

houses total energy consumption. Several energy-efficient scenarios, such as

changing the wall type and increasing window sizes, were then assessed for

effectiveness using ECOTECT. Using a combination of these approaches an energy-

efficient design, both in terms of embodied energy and on-going energy is proposed

and the savings quantified.

55 .. 11 MM OO DD EE LL CC OO NN FF II GG UU RR AA TT II OO NN

5.1.1 BUILDING LAYOUT AND MATERIALS

Full architectural plans for the Batavia project home can be found in Appendix C.

Material data from the ECOTECT material library was used where available and from

the manufacturer otherwise. The house was modelled initially using the materials in

Table 19, further materials were investigated to model thermally efficient scenarios.

A full materials listing and their thermal properties can be found in Appendix D.

Table 19: Base Case Batavia building material types.

TypeCeiling Suspended plaster with R2 glass fibre insulation battsWalls Double brick exterior walls, single brick interiorRoof Colourbond

Outer Doors and Windows Single glazed, aluminium framedFloors 85mm suspended concrete floor covered with carpet, tiles and

timber flooring

The Batavia model was divided into 10 thermal zones; 3 bedrooms, living, lounge,

hall, utilities, garage, ensuite and roof, with a grid size of 3000 x 3000 x 3000mm

(Figure 46). The living zone includes the open living kitchen, dining and family

rooms. The utilities (toilet, bathroom and laundry) were also grouped together as a

buffer for heat exchange, where the temperature of each room is relatively

unimportant.


76

Figure 46: Thermal zones of the Batavia household in plan view.

Model parameters were defined as in Table 20 with the major assumption being that

the house will only be occupied from 5pm to 9am Monday to Friday and throughout

the weekends. The maximum occupancy of the house at these times was assumed to

be 4 people.

The house was modelled initially using the natural ventilation setting to determine the

expected thermal comfort levels without the use of a heating and or cooling system.

Following this, the model was configured with a mixed mode heating and cooling

system in the living area to determine the expected on-going energy consumption. It

was assumed that heating and cooling appliances would only be used from 5pm to

11pm Monday to Friday and from 8am to 11pm on weekends. It was assumed that a

mixed mode system would be used with an efficiency of 95%.

Table 20: Initial model settings for the Batavia project home.

Parameter SettingComfort band 18-26˚C

Maximum number of people 4Occupancy Schedule 17:00 – 9:00 weeknights

0:00 – 24:00 weekendsActivity Rate Walking (80W / person)

Sensible Heat Gains 60 W/m2

Infiltration rate 0.5 (% Air change per hour)Wind Sensitivity 0.25 (% Air change per hour)

Heating system efficiency 95%Heating / cooling regime 17:00 – 23:00 weekdays

8:00 – 23:00 weekendsCost of electricity 12.75c /kWh

E


77

5.1.2 FORCING DATA

In order to make predictions for likely weather conditions in Perth, the hourly climate

data was based on long-term averages for the Perth Metropolitan region. The relevant

parameters were air temperature, relative humidity, wind speed and direction, direct

and diffuse solar radiation, cloudiness and rainfall (Figure 47).

J F M A M J J A S O N D0 0

20 100

40 200

60 300

80 400

100 500

0

10

20

30

40

50

0

2

4

6

8

10

12

8

4

0 DAYLT

IRRAD

TEMP

CLIMATE SUMMARY

9am

Wind

3pmWind

J F M A M J J A S O N D0k

2k

4k

6k

8k DEGREE HOURS (Heating, Cooling and Solar)

H

C

S

NAME: PerthLOCATION: Western AustraliaDESIGN SKY: Not AvailableALTITUDE: 33.0 m© A.J.Marsh '00

LATITUDE: -32.0°LONGITUDE: 115.8°TIMEZONE: +8.0 hrs

Figure 47: Long term average climate data for Perth (Marsh, 2000).

5.1.3 SENSITIVITY ANALYSIS

A sensitivity analysis was conducted to evaluate the robustness of the assumed

occupancy and heating / cooling schedules. The thermal comfort range, initially set to

18-26ºC was also varied to analyse its effect on predictions.


78

55 .. 22 BB AA SS EE CC AA SS EE RR EE SS UU LL TT SS

5.2.1 DIURNAL VARIATION

The house is predicted to be within thermal comfort ranges 57.7% of the time under

natural ventilation (Table 21). The design suffers from being too cold rather than

over-heating. The temperature distribution of all zones is skewed towards being too

cold, with as many hours being spent with temperatures being between 12-16ºC as

between 20-24ºC (Figure 48). All thermal zones appear to follow the same pattern.

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 460

320

640

960

1280

Hrs

Outside Temp.

Temperature Distribution Location: Perth, Western Australia

Figure 48: The temperature distribution of the various

thermal zones of the house. The living zone is in bold.

Temperature Hours Percentage 8°C 29 0.3% 10°C 197 2.2% 12°C 774 8.8% 14°C 1353 15.4% 16°C 1142 13.0% 18°C 1385 15.8% 20°C 1529 17.5% 22°C 1145 13.1% 24°C 684 7.8% 26°C 308 3.5% 28°C 126 1.4% 30°C 70 0.8% 32°C 13 0.1% 34°C 5 0.1% ------- ------- --------

COMFORT 5051 57.7%

Table 21: The total hours at each given

temperature under natural ventilation.

On the long-term average hottest and coolest days the diurnal variation in temperature

of the living zone is 4.6ºC and 2.5ºC (Figure 49 and Figure 50). In both cases the

building structure appears to be able to regulate the temperature during the day more

effectively than during the night. This is particular evident when compared to the

predicted temperatures of the roof zone, which basically mimics the outside

temperature. Despite this the house still remains uncomfortable for the residents

throughout both days, implying that heating and cooling devices would be used.


79

Figure 49: Hourly temperatures on the average coldest day. The living zone (orange) and roof

(red) are shown.

Figure 50: Hourly temperatures on the average hottest day. The living zone (orange) and roof

(red) are shown.


80

5.2.2 HEAT GAINS

In order to analyse inefficiencies in the design it is important to understand the major

heat flux drivers. Heat gains via fabric or structural gains, ventilation gains, indirect

solar gains, direct solar gains and inter-zonal gains in the living area were analysed

(Figure 51-55).

Fabric gains are highest during the afternoon in the hottest months and lowest over-

night during the coldest months. Ideally, the building should be designed to minimise

heat loss at night during winter and to heat gain during the day during summer. This

could be achieved by increasing the area of exposed thermal mass with a long thermal

time lag so that absorbed heat is released late at night rather than during the day.

Ventilation heat gains similarly resulting in daytime gains in summer and losses at

night in winter. Ventilation gains occur through windows, doors as well as cracks in

the building structure and vents. Due to the large area of glass windows and doors in

the living thermal zone, it is not surprising that these values are high. Heat gains and

losses through ventilation are actually double the magnitude of fabric gains in summer

and 1.5 times the magnitude of fabric losses in winter. This means that it may be a

more effective measure to ensure the building is airtight rather than increase the

thermal mass exposure through increased window space. A balance will have to be

struck between increasing thermal mass without further exacerbating ventilation

problems.

The building design effectively maximises the use of both direct and indirect solar

radiation during the winter months. This would imply that the household is orientated

correctly to make maximum used to North facing windows, whilst not overheating

due to over-exposure during summer.

The living thermal zone is adjacent to the ensuite, lounge, and bed 2 and 3, but the

largest contributing zone to interzonal heat fluxes is from the roof. It appears that the

insulation in this household is working effectively as heat fluxes into the room occur

at night in winter and heat flux out of the room during summer peak temperatures.


81

2

4

6

8

1 0

1 2

1 4

1 6

1 8

2 0

2 2

H r


W a t t s

1 6 0 0

1 2 8 0

9 6 0

6 4 0

3 2 0

0

- 3 2 0

- 6 4 0

- 9 6 0

- 1 2 8 0

- 1 6 0 0

F a b r i c G a i n s - s Q c + s Q sL o c a t i o n : P e r t h , W e s t e r n A u s t r a l i a

Figure 51: Fabric Gains (sQc+ sQs) (W).

2 4 6 8

10 12 14 16 18 20 22 Hr


Watts2400192014409604800

-480-960-1440-1920-2400

Ventilation Gains - sQv Location: Perth, Western Australia

Figure 52: Ventilation Gains (sQv) (W).

2 4 6 8

10 12 14 16 18 20 22 Hr


Watts1700136010206803400

-340-680

-1020-1360-1700

Direct Solar Gains - sQg Location: Perth, Western Australia

Figure 53: Direct Solar Gains (sQs) (W).

2 4 6 8

10 12 14 16 18 20 22 Hr


Watts60483624120

-12-24-36-48-60

Indirect Solar Gains - sQs Location: Perth, Western Australia

Figure 54: Indirect Solar Gains (sQs) (W).

2

4

6

8

1 0

1 2

1 4

1 6

1 8

2 0

2 2

H r


W a t t s

1 3 0 0

1 0 4 0

7 8 0

5 2 0

2 6 0

0

- 2 6 0

- 5 2 0

- 7 8 0

-1 0 4 0

-1 3 0 0

I n t e r - z o n a l G a i n s - s Q z L o c a t i o n : P e r t h , W e s t e r n A u s t r a l i a

Figure 55: Interzonal Gains (sQz) (W).


82

5.2.3 THERMAL COMFORT

The spatial distribution of thermal comfort in the building varies greatly throughout

the building (Figure 56). During cold external temperature conditions the living

thermal zone is the most comfortable in the house. The most uncomfortable areas are

the ensuite and Bed 1. The remainder of the house is comfortable 50-60% of the time.

During summer, the living area is the most uncomfortable place in the house. This

implies that the large glass exposure in this area helps the house in winter but

increases heat gains in summer. The most comfortable area is Bed 3. The lounge

area also benefits in comparison to Bed 1 from shading provided by the verandah.

PPD 100+

90 -100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0-10

PPD 100+

90 -100 80 -90 70 -80 60 -70 50 -60 40 -50 30 -40 20 -30 10 -20 0 -10

Figure 56: Thermal Comfort in Batavia household on the average coldest and hottest day.

Percentage dissatisfaction (PPD) from 0-100%.


83

The temporal distribution of thermal comfort levels demonstrates that the building

suffers from being too cold through the winter period (Figure 57). On inspection, it

appears that the on-going energy costs of heating the house in winter are expected to

be larger than the cooling costs in summer. However, when the heating and cooling

loads are calculated the summer loads are more significant (Figure 58). The model

also predicts that the house will require cooling during the in winter and that the total

electricity cost of heating and cooling is $272 per year.

These results are largely a function of the heating and cooling regime. This regime is

constant over the entire year and is turned on at night Monday to Friday and during

the day on weekends. On all days the system is turned off at 11pm. As extreme

temperatures occur during the day in summer and during the night in winter, the

seasonal heating and cooling requirements are different. As the model only heats the

house until 10pm the required heating load is lower than that for cooling in summer,

which occurs during the day when people are home on the weekend. The follow

section will assess the models sensitivity to these settings and address these issues.

Dhrs

-1200

-1000

-800

-600

-400

-200

0

200

400

600


Too Hot Too Cool

Figure 57: Discomfort degree hours of living area.

kWh

-600

-500

-400

-300

-200

-100

0

100

200

300


Heating Cooling

Figure 58: Monthly Heating and Cooling loads with a mixed mode system.


84

5.2.4 SENSITIVITY

Thermal comfort levels are changed by user occupancy regimes. At present, the

model assumes zero occupancy during the day from Monday to Friday. For this

reason, levels of thermal discomfort may be underestimated in the summer months

where peak temperatures are experience during the day, if the household were

occupied. For this reason the model was also run with one person present in the

building during the weekday daylight hours (Figure 59).

Dhrs

-3000

-2500

-2000

-1500

-1000

-500

0

500

1000

1500


Too Hot Too Cool

Figure 59: Thermal comfort in Batavia household with one person at home on weekdays.

The resulting thermal comfort levels result in an increase in summer and winter

discomfort by over 150% percent. This degree of change indicates the importance of

clarifying assumptions. In order to effectively monitor the effect of energy efficient

design solutions to the house at all time it was decided to model the house under the

worst-case scenario, with one person present in the house at all times.

Changing the occupancy schedule does not have any effect on the heating and cooling

loads generated by the model. ECOTECT does not have any capacity to set heating

and cooling systems seasonally and hence to solve the dichotomy between winter and

summer requirements the model was run separately over winter and summer to

determine the effect changing the operation regime has on results. Summer operation

(October to April) was set from 9am to 6pm whilst winter operation (May to

September) was set from 6pm to 10pm (Figure 60).


85

kWh

-700

-600

-500

-400

-300

-200

-100

0

100

200

300


Heating Cooling

kWh

-200

-150

-100

-50

0

50

100

150

200


Heating Cooling

Figure 60: Monthly heating and cooling loads with summer and winter settings.

Dhrs

-700

-600

-500

-400

-300

-200

-100

0

100

200


Heating Cooling

Figure 61: Monthly heating and cooling loads with combined settings.

The resultant heating and cooling loads amount to an electricity cost of $266 per year.

This is decrease in comparison to the previous estimate of $272 is mainly due to

decreased the removal of cooling costs in winter.

The assumption that heating at night would stop at 11pm was also tested. Heating and

cooling loads with regimes set to overnight resulted in a two-fold increase in cost to

$544 per year. Given that the average user spends close to $700 per year on

electricity (Dashlooty, 2001) and that 39% (Australian Greenhouse Office, 1999) of

this is due to heating and cooling costs, it is reasonable to assume that this value is an

over estimate of actual costs and the original assumption was considered more

realistic for the purposes of this study. Given that heating and cooling loads and

related prices are likely to vary highly depending on the user the following analysis

will use a comparative cost analysis to limit this error.


86

Sensitivity of the predicted heating and cooling loads to the system efficiency was

also tested. Decreasing the efficiency to 85% led to a 40c rise in electricity costs,

whilst decreasing to 50% led to a 45c rise in costs. The expected efficiency of heating

appliances is between 50 and 95% (Sustainable Energy Development Office, 2002).

The insensitivity of the model to these values to this degree is unexpected and

indicates that these algorithms need further development.

Dhrs

-800

-600

-400

-200

0

200

400

600

800

1000


Heating Cooling

Figure 62: Monthly heating and cooling loads with combined settings and heating all night.

02000400060008000

1000012000

Bed 1 Bed 2 Bed 3 Ensuite Lounge Living

Deg

Hou

rs

TOO HOT TOO COOL TOTAL

Figure 63: Final base case thermal discomfort degree hours across the house.

55 .. 33 AA SS SS EE SS SS II NN GG EE NN EE RR GG YY EE FF FF II CC II EE NN CC YY

5.3.1 METHODOLOGY

The energy consumption of a household can be divided into embodied and on-going

energy costs. The embodied energy cost of the Batavia household was calculated

from the materials inventory (Alcock, 2002), whilst the on-going energy costs were

estimated using ECOTECT.


87

EMBODIED ENERGY

Embodied Energy Coefficients (MJ/kg or MJ/m3) for manufacturing were sourced

from the Centre for Building Performance Research, Victoria University of

Wellington the most comprehensive list of publicly available data (Alcorn, 1998).

The complete listing of materials, their quantities and embodied energy coefficients

are listed in Appendix E. The total quantity of each material was calculated in kg or

m3 and multiplied by its embodied energy coefficient (e) to give the total embodied

energy of each component. These values were summed and divided by the floor area

to give the embodied energy of manufacturing for the total household in GJ/m2. To

account for the wastage of materials on site and the replacement of materials

throughout the lifetime of a building, waste and replacement factors were also

incorporated (Appendix F).

The affect of using alternative materials with lower embodied energy was then

investigated. These alternatives were:

Window frames: replacing aluminium frames with recycled aluminium or

timber.

Structural beams: replacing timber with steel.

Wall Structure: replacing double brick exterior walls with reverse brick

veneer, brick veneer (timber and steel) or concrete and brick layers.

Roofing: replacing Colourbond roof with clay tiling.

Flooring: Increasing the thickness of the concrete slab.

SCENARIOS

The thermal advantages and disadvantages of changes to the household in terms of

on-going energy savings were then investigated by running the model for a series of

scenarios. In each scenario one parameter was changed whilst holding others constant

and the thermal performance of the house under the natural ventilation and mixed

mode settings was investigated. The complete list of alternative materials

investigated and their thermal properties is shown in Appendix D.


88

Initial analysis of the building layout identified several areas with potential to improve

the thermal performance of the building. These areas and the modelling scenario used

to quantify their savings are outlined below.

Thermal Mass

Firstly it was identified that increasing the amount of exposed North facing thermal

mass would help even out temperature extremes in the house. Improvements in the

design of the house that may achieve this include increasing the area of North facing

windows, increasing the thickness of the concrete slab and changing the timber and

carpeted flooring to exposed concrete or tiled surfaces. A further improvement would

be to replace the North facing covered pergola area with a solar pergola, which could

best utilise maximum solar penetration through the windows in the winter whilst

maintaining shading during summer.

Four scenarios were run to test these hypotheses:

Scenario 1: Increase north facing windows

Two full sized windows were placed in the North facing alcoves of the living and

lounge thermal zones (Figure 64). The window types were kept consistent with the

rest of the house, ie single glazed and aluminium framed.

Figure 64: Batavia model with windows shown in yellow. Additional windows simulated in North

facing alcoves shown with arrows.

Scenario 2: Increase concrete slab thickness

The thickness of the concrete slab was increase from 85mm to 100mm and the

material properties of the slab were updated accordingly.


89

Scenario 3: Improving flooring

Timber flooring in the living area and carpeted flooring in the lounge area was

replaced with tiles.

Scenario 4: Solar pergola

The solar pergola was simulated by maintaining the existing roof structure over the

summer period but by removing the roofing over the pergola during winter (Figure

65).

Figure 65: The solar pergola as modelled over the summer and winter periods, with and without

roofing (red).

Heat Loss and gains through windows

Heat loss through the aluminium, single glazed windows was also identified as a

possible issue. The affect of replacing the window frames with timber with has much

lower conductivity and utilising double glazed windows were identified as areas with

potential benefits for minimum cost offset. Increasing the eaves over north facing

windows and improving shading devices around the house would minimise the

potential for over heating in summer.

Three scenarios were investigated:

Scenario 5: Window Frames

All of the existing window frames were replaced with timber.

Scenario 6: Double Glazing


90

All existing windows were changed to double-glazed windows.

Scenario 7: Improved Shading

Increased eaves on the northern side of the building and shading device use over Bed

1 were investigated.

Improved Insulation

The house is currently insulated with Bradford Gold Batts with an R2 rating. The

potential for increased insulation (R3.5) was also identified as an area for potential

savings. Wool and Cellulose insulation have lower embodied energy and hence were

also identified as alternative materials.

Scenario 7: Improved Insulation

Current insulation over the main area of the house (everywhere except the garage)

was updated to a resistivity of 3.5 W/m2K.

Wall types

The current Batavia project home is built with standard double brick walls. The

potential for cost savings in terms of initial outlay and embodied energy is great

through the replacement of one layer of bricks with concrete bricks or masonry. The

greatest thermal advantage was expected to be through the replacement of the double

brick exterior walls with insulated reverse brick veneer.

In all four scenarios were run to test the thermal benefits of changing wall types:

Scenario 8: Reverse Brick Veneer

All exterior walls were replaced with timber clad masonry.

Scenario 9: Insulated Reverse Brick Veneer

All exterior walls were replaced with timber clad masonry with an R1.5 layer of

insulation.

Scenario 10: Standard Brick Veneer

All exterior walls were replaced with a standard brick veneer.


91

Scenario 11: Concrete and Brick Double Brick

All exterior walls were replaced with a concrete and brick double-layered walls.

Orientation

It was proposed that the house is orientated correctly with the living area facing due

north and minimal use of east facing windows.

Scenario 12: Orientation

This conjecture was investigated by rotating the house through 360˚C.

Ventilation Gains

Ventilation gains through gaps and vents were found to be a major cause of over-

heating in summer and cooling in winter. Purpose built designs, which minimize

infiltration, can limit this effect.

Scenario 13: Ventilation

Wind sensitivity and air infiltration rates were reduced to 0.1 air changes per hour.

Combined Design

Finally, a combined design utilizing a combination of the above scenarios was

proposed and modeled. Total on-going energy savings and improvements in thermal

comfort were quantified by simulating the model under the mixed mode and natural

ventilation settings.

LIFE CYCLE ASSESSMENT

The cost benefits of the proposed combined design in terms of both embodied energy

and on-going energy consumption was calculated on two time scales. Firstly over the

lifetime of the building, an expected 25 years and secondly over an owners expected

occupancy of 7 years. The second of these values relates to a cost benefit for the

buyer of the household whilst the first pertains to larger community benefits of

reduced energy consumption. A discount factor of 6% was used to calculate the Net

Present Value (NPV) cost to the owner.


92

5.3.2 RESULTS AND DISCUSSION

EMBODIED ENERGY

21%

35%

18%

6%

14%

6%

CONCRETE SLAB

WALLS

FRAMES

WINDOW FRAMES

ROOF

*OTHERS

21%

35%

18%

6%

14%

6%

CONCRETE SLAB

WALLS

FRAMES

WINDOW FRAMES

ROOF

*OTHERS

Figure 66: Embodied Energy Components.

Table 22: Embodied Energy Consumption.

Component % GJWALLS 36.5 233

CONCRETE SLAB 14.6 94FRAMES 18.8 120

WINDOW FRAMES 6.1 39ROOF 15.1 96

*OTHERS 6.1 56PLASTERBOARD* 2.0 13

GLASS* 0.7 5TILES* 0.2 1

INSULATION* 3.2 20PAVING* 2.8 18

TOTAL 97.2639

3.01GJ/m2

With ReplacementAnd Waste

666 GJ3.10GJ/m2

The total embodied energy of the Batavia house was calculated as 3.10 GJ/m2 of

flooring or 666GJ in total (Table 22). This value is low compared to other

investigations around Australia, which range from 3.5 – 5.9 GJ/m2 (Table 23). This is

due to the low embodied energy in the concrete slab of the Batavia house, which is


93

85mm in comparison to the other houses, which are thicker. The Batavia house is

also built out of lower embodied energy timber frames as opposed to steel.

Table 23: Embodied Energy Study results from Australia (Hill, 1978; Ballantyne, 1980; D'Cruz

et al., 1990; Edwards et al., 1994; Pullen, 1995; Lawson, 1996).

AUTHOR TOTAL GJ/m2

Ballantyne 5.9Lawson 4.9D'Cruz 4 to 5

Hill 3.5Edwards 3.7 to 5.5Pullen 4.2

The major contributor to embodied energy consumption of the house was the double

clay brick walls, which made up 35% of the whole (Figure 66). Second to this came

the concrete slab, followed by the frames and the steel roof cladding. Other minor

components were plasterboard, insulation, paving, glass and tiles. Detailed

calculations and results can be found in Appendix E and F.

Several options were investigated to determine the potential savings through reduced

embodied energy (Table 24). The largest savings were found through the replacement

of double brick walls with brick veneer or reverse brick veneer construction.

Concrete roof tiling, suspended timber floors and timber or recycled aluminium

frames also reduced the embodied energy of the design. Recycled aluminium window

frames constituted a greater saving than timber frames, however are not commercially

available in Perth.

Table 24: Alternative materials component embodied energy savings.

ELEMENT Component TOTAL GJ GJ

Floors Concrete slab 93.6 666 Suspended timber 41.3 613

Walls Double Brick 233.31 666 Brick Veneer (timber) 148.4* 543 Brick Veneer (steel) 148.9* 543 Timber cladding and frame 107.6* 502

Window frames Aluminium 39.1 666 Timber 26.2 653

Recycled Aluminium 6.15 633Roof coverings Concrete Tile 61.2 630.7

Steel Sheeting 96.5 666

*(Ballantyne, 1980)


94

ON-GOING ENERGY CONSUMPTION

Scenario Results

Scenario 1: Increase north facing windows

Increasing the area of North facing windows will increase the direct solar radiation

heat loads over the winter period when the house is cooler. However, it was shown

that the relative heat gains were largely offset by an increase in heat gains in summer

and losses in winter through the comparatively low resistivity windows (Figure 67

and Figure 68). Ventilation gains and direct and indirect solar gains also increase

with increased window space increasing over-heating in summer and increase heat

loss through ventilation in winter.

0

2000

4000

6000

8000

10000

12000

Loun

ge

Wind

ows

Living

Wind

ows

Deg

Hou

rs


0200400600800

1000

12001400160018002000

Living Window s

$267 $306

kWh

HEATING COOLING

Figure 67: Thermal discomfort (degree Hours)

of the living and lounge zone with natural

ventilation simulated with and without

increased North facing window space.

Figure 68: Heating and cooling energy

consumption (kWh) and approximate costs with

and without increased North facing window

space.

Scenario 2: Increase concrete slab thickness

Increasing the concrete slab thickness aims at increasing the thermal mass of the

household. In the living area, an increased amount of exposed thermal mass is

expected to both cool the room in summer and heat the room during winter nights due

to the thermal lag time of the flooring. Although the increased concrete does improve

winter conditions significantly, the living area becomes hotter in summer (Figure 69-

70). As a result the balance between heating and cooling needs changes and the

decrease in heating loads is offset by an increase in cooling loads, resulting in a net


95

decrease of the predicted annual power costs by $11. An increase in thermal mass

will result in more heat retention by the concrete flooring to be released into the room

at night. This results in summer temperatures being constantly higher than with a

thinner concrete slab.

0

5000

10000

15000

20000

25000

30000

35000

40000

85mm 100mm

Deg

Hou

rs


0

200

400

600

800

1000

1200

1400

1600

1800

2000

85mm$266

100mm$255

kWh

HEATING COOLING


of the house with natural ventilation simulated

with increased concrete slab thickness.


consumption (kWh) and approximate costs of

simulated with increased concrete slab

thickness.

Scenario 3: Improve flooring

0

5000

10000

15000

20000

25000

30000

35000

40000

Timber Tiled

Deg

Hou

rs


0

500

1000

1500

2000

2500

Timber Tiled

$266.90 $316.42

kWh

HEATING COOLING

Figure 71: Thermal discomfort (degree Hours) of

the house with natural ventilation simulated with

timber and tiled flooring.


consumption (kWh) and approximate costs

simulated with timber and tiled flooring.


96

At present the living and lounge areas have timber flooring. This does not make the

best use of the concrete floor mass as thermal mass to balance heat loads. Were the

flooring changed to tiling it is expected that an increase in exposed thermal mass

would decrease heating loads in winter. However, once again it is evident that gains

made in winter heating are lost in increases in summer cooling loads (Figure 71-72).

The benefits of changing the flooring are outweighed by the increase in heat loads in

summer and it appears that increasing the concrete slab thickness is a more effective

option. The relative cost of increasing the concrete slab thickness and tiling may

mean that tiling a portion of the room is a feasible option. Increasing the shading on

the northern side may alleviate the need for increased cooling but it also must be

considered that these savings must offset the increase of embodied energy.

Scenario 4: Solar pergola

Given that the house design suffers from being too cold, increasing direct and indirect

solar radiation heat gains by the replacement of the pergola roof with transparent

cladding was investigated. However, the removal of the lower ceiling in this area

means that the area would be prone to heat losses through conduction. The increase

solar exposure may also result in over-heating in summer. Modelling results showed

that the heat losses through the removal of the ceiling outweigh the benefits of

increased direct solar radiation gains (Figure 73-74).

0

2000

4000

6000

8000

10000

12000

Living

Solar P

ergo

la

Loun

ge

Solar P

ergo

la

Deg

Hou

rs


0

200

400

600

800

1000

1200

1400

1600

1800

Living Solar Pergola

$267 $280

kWh

HEATING COOLING

Figure 73: Thermal discomfort (degree Hours) of the

house with natural ventilation simulated with solar

pergola.


consumption (kWh) and approximate

costs simulated with solar pergola.


97

Scenario 5-6: Windows

The thermal conductivity of aluminium is much higher than that of timber. Hence it

is expected that greater heat losses and gains will occur with Aluminium frames over

timber. This is evident when comparing the relative heating and cooling loads of

aluminium and timber frames when all windows have double-glazing (Figure 75-76).

Both the heating and cooling loads decrease with timber frames. However, the

relative benefit is not as significant as when single glazing is used. This suggests that

heat loads through the window itself are significantly larger than through the frame

with the less resistive glass.

0

5000

10000

15000

20000

25000

30000

35000

40000

Al /Single

Timber/Double

Timber/Single

Al /Double

Deg

Hou

rs


0

200

400

600

800

1000

1200

1400

1600

1800

Al /Single

Timber/Double

Timber/Single

Al /Double

$267 $217 $269 $223

kWh

HEATING COOLING



with Al and timber window and doorframes

and single and double-glazing.


consumption (kWh) and approximate costs

simulated with Al and timber window and door

frames and single and double glazing.

This is also reflected in the fact that double-glazing is more effective than changing

the window frames at reducing thermal comfort and heat loads. The most thermally

efficient option is to change all windows to double-glazed, timber frames with an

annual saving of $50. With a little ingenuity placing double-glazing on the most

important windows should achieve the same results at least cost.


98

Scenario 7: Improved shading

TOO HOT

1350

1355

1360

1365

1370

1375

1380

1385

None

Eaves

Shadin

g

Deg

Hou

rs

TOO HOT TOO COOL

2300

2305

2310

2315

2320

None

Eaves

Shading

De

g H

ou

rs

TOO COOL TOTAL

3655366036653670367536803685369036953700

None

Eaves

Shadin

g

Deg

Hou

rs

TOTAL

Figure 77: Thermal discomfort (degree Hours) of Bed 1 with natural ventilation simulated with

extended eaves and shading device.

Increasing shading over Bed 1 aims to decrease solar gains during summer. However,

a balance must be struck with increased shading in summer and over cooling in

winter. Two scenarios were investigated to compare the relative benefits; increasing

the eave overhang and placing a solar pergola-shading device above the window. It

was found that the relative decrease in summer heat loads was significantly larger

with the shading device (Figure 77). It was also found that the relative decrease in

temperature in the winter is minimised through the use of shading in comparison to

eaves. Permanent eaves allow no solar radiation penetration throughout the year and

the benefits of using a shading device design to maximise winter sun is clear.

Increasing the eave over hang on the north facing side of the building was seen as a

relatively cost effective solution to decreasing solar radiation heat gains through these

windows during summer. Increasing the eave overhang by 750mm lead to an increase

in summer thermal comfort with a relatively small increase in winter heating loads

(Figure 78-79). The net annual saving by implementing this method was $17 and

132kWh.


99

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Living Eaves Lounge Eaves

Deg

Hou

rs


0

200

400

600

800

1000

1200

1400

1600

1800

Living Eaves

$267 $250

kWh

HEATING COOLING

Figure 78: Thermal discomfort (degree hours)

of the living and lounge zone with natural

ventilation simulated with extended eaves.



simulated with extended eaves.

Scenario 8: Improved insulation

The effectiveness of the current insulation (R2.5 Bradford Gold Batts) is evident in

terms of both thermal comfort and heating and cooling loads (Figure 80). The saving

to the homeowner is predicted to be $190 per annum. Increasing the insulation

resistivity has relatively little cost savings. This indicates that the insulated building

is dominates by heat loss and gain through the windows and infiltration losses and

gains.

05000

10000

15000200002500030000

350004000045000

R2.5

None

Woo

l

Cellulo

seR3.

5

Deg

Hou

rs


0

500

1000

1500

2000

2500

R2.

5

Non

e

Woo

l

Cel

lulo

se

R3.

5

$267 $457 $267 $267 $265

kWh

HEATING COOLING

Figure 80: Approximate heating cost (AUS$) and thermal discomfort (degree hours) of the house

with natural ventilation with different types of insulation.


100

Scenario 9-11: Wall Materials

05000

10000150002000025000300003500040000

Double

Bric

k

Brick /

Concr

ete

Rever

se B

. Ven

eer

Insu

lated

R.B

.V.

Brick V

enee

r

Deg

Hou

rs TOO HOT TOO COOL TOTAL

0200400600800

10001200140016001800

Dou

ble

Bric

k

Bric

k

/Con

cret

e

Rev

erse

B. V

enee

r

Insu

late

dR

.B.V

.

Bric

k

Ven

eer

$267 $214 $266 $266 $265

kWh

HEATING COOLING



with different external wall types.



the living zone with a mixed mode system

simulated with different external wall types.

Different wall types with varying conductivity, thermal mass and lower embodied

energy content were tested to determine their effectiveness in the building design

(Figure 81-82). The increased thermal mass of clay and concrete double brick walls

had significant advantages over the other options. The thermal mass significantly

reduced the heating and cooling costs of the design to an estimated $214, a saving of

$53 per annum. All other wall types had increased thermal efficiency compared to

double clay brick with insulated reverse brick veneer also result in a significant

increase in thermal comfort of the household.


101

Scenario 12: Orientation

PPD 100+

90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0-10

PPD 100+

90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0-10

PPD 100+

90 -100 80 -90 70 -80 60 -70 50 -60 40 -50 30 -40 20 -30 10 -20 0 -10

PPD 100+

90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10 -20 0-10

Figure 83: Thermal Comfort in Batavia household at different orientations on the average

coldest day. Percentage dissatisfaction (PPD) from 0-100%.

PPD 100+

90 -100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0 -10

PPD 100+

90 -100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10 -20 0-10

PPD 100+

90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0-10

PPD 100+

90 -100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0-10

Figure 84: Thermal Comfort in Batavia household at different orientations on the average hottest

day. Percentage dissatisfaction (PPD) from 0-100%.

The optimal orientation for the house was found to be 2º from North. The increase in

discomfort for the home user during winter and summer periods over different

orientations is shown in Figure 83-84.


102

Scenario 13:Ventilation

0

5000

10000

15000

20000

25000

30000

35000

40000

All DecreasedVentilation

Deg

Hou

rs


0200400600800

10001200140016001800

All DecreasedVentilation

$267 $240

kWh

HEATING COOLING



with decreased ventilation.


consumption (kWh) and approximate costs with

decreased ventilation.

Decreasing infiltration through building gaps and vents significantly reduces the heat

and cooling requirements for minimum cost (Figure 85-86). The annual savings are

$27 and 215kWh.

Summary

A summary of the above findings is found in Table 25. These options were then

evaluated to determine the best overall combined design solution.

Table 25: Summary of Scenario model findings.

Scenario Best Option kWh AnnualCost

kWhSaved

CostSaving

1 Do not increase North facingwindows

2093 $267 0 0

2 Increase Slab (100mm) 2002 $255 91 $123 Timber Flooring 2093 $267 0 04 No Solar Pergola 2093 $267 0 0

5-6 Timber Framed / Double GlazedWindows

1701 $217 392 $50

7 Solar Pergola on Bed 1 3669ºh - 19ºh -7 North Facing Eaves 1961 $250 132 $178 R2.5 Insulation 2093 $267 0 09 Brick Concrete Walls 1678 $214 415 $5312 2º Orientation 2093 $267 0 013 Decrease Ventilation 1879 $240 215 $27


103

LIFE CYCLE ASSESSMENT RESULTS AND DISCUSSION

The proposed improvements were analysed in terms of cost and total energy savings.

The total energy savings included the ongoing energy savings as predicted by the

model and embodied energy savings. The results of this assessment are shown in

Table 26. Details of the individual cost calculations can be found in Appendix G.

The largest total energy savings were found through changing the interior walls to

concrete brick, which has significant embodied energy savings. Also significantly

large savings were through the replacement of windows with timber frames and

double-glazing. Ventilation decreases, increasing the North facing eaves and

decreasing window sizes were all found to have positive energy savings. Increasing

the thickness of the concrete slab did not have sufficient ongoing energy saving to

balance the increase consumption of embodied energy. The bedroom solar pergola

also amount to a net loss due to the fact that heating and cooling was not simulated in

the bedroom.

Table 26: Ongoing, Embodied and Net Energy savings over 25yrs.

Savings Ongoing EnergyEmbodied

EnergyNet

kWh / yr kWh/ 7yrs kWh/25yrs GJ GJ GJ

Increase Slab (100mm) 91 637 2275 8.19 -10 -1.81

Timber Framed / Double GlazedWindows

392 2744 9800 35.28 12.93 48.21

North Facing Eaves 132 924 3300 11.88 -2.4 9.48Brick Concrete Walls 415 2905 10375 37.35 53 90.35Decrease Ventilation 215 1505 5375 19.35 0 19.35

Bed 1 Solar Pergola 0 0 0 0 -0.01 -0.01

Decrease Window Size 169 1183 4225 15.21 -8.4 6.81

The cost of each design was weighed against the cost savings from decreased heating

and cooling consumption to determine the economic benefits of each design option

(Table 27). A net benefit to the homeowner and project homebuilder was found

through the use of smaller window sizes. The initial outlay of double-glazing on the

windows was partial offset by the saving of using timber frames to result in a payback

period of 8 years. Increasing the north facing eaves had a slightly longer payback

period of 14 years due to high initial outlay costs. Replacing the interior layer of


104

external walls with concrete block, whilst reaping significant energy savings was the

most costly design option resulting in a 16-year pay back period. Decreasing

ventilation through the use of doorstoppers had a payback period of 21 years, three

times the expected user occupancy of the house. Increasing the concrete slab was

deemed economically unfeasible with a payback period of 46 years.

Table 27: Net Cost Savings to the homeowner over 7 years and pay back periods.

Savings Saving Design Cost Cost to userPay back

period

(1 year) (7 years) (25 years) (7 years) (years)

Increase Slab (100mm) $12 $70 $157 $552 $482 46

Timber Framed / DoubleGlazed Windows

$50 $293 $656 $381 $88 8

North Facing Eaves $17 $100 $223 $230 $130 14

Brick Concrete Walls $53 $311 $695 $856 $545 16

Decrease Ventilation $27 $157 $354 $560 $403 21

Bed 1 Solar Pergola $0 $0 $0 $38 $38 n/a

Decrease Window Size $22 $126 $283 -$447 -$573 -21

Based on these calculations a final design was proposed and modelled to determine

the on-going energy savings. The house was modelled with the following changes:

Orientation 2ºN

All window frames replaced with timber

All windows double-glazed except ensuite window, kitchen south facing

windows and lounge east facing windows.

Decreased ventilation losses by placing seals on all major windows and door.

Decreased window sizes in Bed 1 (Figure 88) and living sliding door (Figure

89)

Increase eaves by 750mm (Figure 87)

Solar pergola on Bed 1 (Figure 87)

Improved flooring in Bed 1 and 3 (concrete)

All north, east and west facing external double brick walls replaced with clay

and concrete double brick.


105

Figure 87: Final Design Solution plan view.

Figure 88: Final Design Solution Perspective. Bed 1 new window measurements found.

Figure 89: Final Design Solution with South Facing Living Zone Door shown.

Solar Pergola

Increased eaves


106

The estimated ongoing heating and cooling energy consumption of the house was

1300kWh per year. With the assumption that this power is supplied by mains

electricity at the base rate of 12.75c/kWh the total annual expected cost of heating and

cooling the house is reduced to $164, a saving of over $100 for the homeowner

(Figure 90). Assuming that consuming 1kWh of electricity from Western Power's

south west electricity grid emits approximately 0.99 kg of carbon dioxide (Sustainable

Energy Development Office, 2002), the main greenhouse gas, this a saving of

0.8tonnes of carbon dioxide emissions per year per household. Given that a present

Australians consume 26.7tonnes of greenhouses gases per capita per year, this is a 1%

reduction in greenhouse gas emissions.

In terms of thermal comfort, the house is dramatically more comfortable, with the

ensuite and living thermal zones benefiting most from the changes (Figure 90).

Generally, the number of hours where the house is too cold was reduced more

dramatically than the number of hot hours. This is not reflected in the heating and

cooling requirement because it was assumed that heating would be turned off over-

night. As such, there is an equivalent reduction in heating and cooling loads.

0

2000

4000

6000

8000

10000

12000

Bed 1 Bed 2 Bed 3 Ensuite Lounge Living

Deg

Hou

rs


0200400600800

10001200140016001800

Original New

$267 $164

kWh

HEATING COOLING

Figure 90: Improved thermal comfort of all rooms and heating and cooling loads with new design

(bold) against the original design (stripes).

With the assumption heating and cooling constitutes 39% of the total ongoing-power

consumption of a household (Australian Greenhouse Office, 1999) the total expected

on-going energy consumption was 114MWh over a 25 year lifespan of the house

(Table 28). When combined with embodied energy savings, the total energy saving is

35.1MWh or 126GJ per house. If Dale Alcock homes builds 4 houses with the new


107

design every year for the next 25 years, a sum total of 100 houses the total energy

savings are equivalent to the power produced by the Wellington Dam Hydroelectric

Scheme in two years (Sinclair Knight Merz, 2002). The amount of carbon dioxide

gas emission abated amounts to 2.5Mt of CO2 or the equivalent emissions of all the

100,000 cars in one year (Australian Greenhouse Office, 2002). The percentage

reductions in total energy use is 11%, with heating and cooling consumption reduced

by 38% and embodied energy reduced by 8%.

Table 28: Total energy savings over 25 years.

Embodied

EnergyOngoing Energy Total Energy

Heating/Cooling

Total

GJ MWh GJ MWh GJ MWh GJ MWh Mt CO2

Original 639.6 177.7 188.4 52.3 483.0 134.2 1122.6 311.8 0.309

New Design 584.5 162.4 117.0 32.5 411.6 114.3 996.1 276.7 0.274

Savings per house 55.1 15.3 71.4 19.8 71.4 19.8 126.5 35.1 0.035

Savings per 100 houses 12.7MJ 2.6GWh 2.5Mt

Economically, the design has is at minimum cost for both the homeowner and project

homebuilder. The increased capital outlay of double-glazing and increased shading is

offset by the use of timber frames and concrete bricks to result in a net initial outlay

cost increase of only $1618. However, over the expected ownership lifetime of the

house (7 years) the net present value cost to the homeowner is $1032. However, over

the lifetime of the house (25 years) the net present value cost to society is only $306.

The proposed alterations to the given project home aimed at reducing the total energy

consumption at minimum cost to the project homebuilder and homeowner. Given this

constraint design options were limited to those that were economically feasible and

acceptable to the client. Initial outlay costs may be reduced further if the project

homebuilder can negotiate lower prices, particularly for concrete blocks and seals.

The calculated heating and cooling energy consumptions are to be used to give an

indication of the potential savings possible through the use of passive solar design.

Given the limitations of the model to accurately predict the particular efficiency of the


108

heating appliance and user patterns in the household the estimates given are to be used

as a guide under the given assumptions and it should be noted that variation from

these values might be significant.

Given that embodied energy constitutes 57% of the total energy of the household the

potential for further reductions in this area are great. Maximising the use of recycled

products is one such option. Another alterative to timber frames would be to invest in

recycled aluminium frames, which have significantly lower embodied energy. Other

options include resource materials manufactured in the eastern states, such as

Bradford Gold Batts to local manufacturers and using local products such as replacing

Oregon timber beams with local hardwood.

66 CC oo nn cc ll uu ss ii oo nn ss aa nn dd RR ee cc oo mm mm ee nn dd aa tt ii oo nn ss

ECOTECT was found capable of simulation internal temperature to within 2% of

accuracy. The model has the potential to be of use to building industry developers in

the development of sustainable housing. ECOTECT has the potential to output data

to programs such as NATHERS and NABERS and hence could serve as an accurate

indicator of a buildings environmental performance.

However, the limitations of the model must always be kept in context and the

assumptions under which predictions are made must be made clear. The models

sensitivity to internal heat gains and material parameters means that the modeller must

religiously enter these values to minimise errors. The model only crudely predicts

expected heating and cooling loads and must be finetuned to account for convection

and efficiency if more accurate results are required. It is suggested that savings of

potential designs be quantified in comparative terms and that output results are

considered merely as indicators.

The total energy consumption of the investigated project home was made up of 57%

embodied energy and 17% heating and cooling energy consumption of a 25 year

lifetime. The reduction of energy usage of any household must encompass changes to


109

reduce both embodied and on-going energy costs. Through the use of investigative

modelling the predicted on-going energy costs through heating and cooling have the

potential to be reduced by 38% through the application of passive solar design

principles. As the designs chosen were also evaluated in terms of lower embodied

energy costs this resulted in a total energy reduction of 11%.

The recommended changes to the building structure and materials are at minimum

cost to the project homebuilder and homeowner, with a net present value cost of only

$1032. The potential for the project home industry to make cost effective changes to

housing design whilst significantly reducing the environmental impact is great. Given

that homes are designed for multiple lots the potential benefits are also multiplied and

the need for the project homebuilder to encompass energy efficient design techniques

in an effort to reduced energy consumption and greenhouse gas emission is evident.

66 .. 11 RR EE CC OO MM MM EE NN DD AA TT II OO NN SS FF OO RR TT HH EE PP RR OO GG RR AA MM MM EE RR

Heating and Cooling Loads

The calculation of heating and cooling loads in the model is currently an estimate cost

based on internal temperature changes only. It is recommended that for more accurate

results the model be expanded to incorporate the efficiency of the system, its

interaction with air infiltration and convection processes.

At present heating and cooling functions can only be turned on and off at the same

time throughout the year. It is recommended that these stationary times be in a

schedule format so that the user may specify different winter and summer regimes.

This will overcome the in accuracies implied where the model currently predicts that

the user will heat in winter and cool in summer.

Thermal comfort

At present ECOTECT only allocates a given hour as comfortable or uncomfortable if

the room is occupied. Given that the model is sensitive to internal heat gains this may

lead to inaccuracies when trying to predict the thermal comfort of rooms, which may


110

not be in continuous use. It is recommended that thermal comfort of rooms with low

occupancy be calculated without the need for added internal heat gains.

Internal gains

As the model is particularly sensitive to internal sensible heat gains, errors may be

minimised by adding typical heat gain components such as lighting and refrigerators

as options under this setting. The user could then choose the suitable combination of

sources, whilst leaving the option of manually entering values. For the novice user of

ECOTECT this would minimise modelling errors.

Thermal Lag

It is recommended that the thermal lag function be calculated automatically from the

material properties. This will reduce user input errors.

Validation

For ease of model validation it is recommended that the model be adjusted to allow

for data outputs for longer than one day. It is also suggested that the model be

validated over smaller time steps.

66 .. 22 RR EE CC OO MM MM EE NN DD AA TT II OO NN SS FF OO RR TT HH EE MM OO DD EE LL LL EE RR

Internal Gains

Where possible it is recommended that the modeller try to match expected internal

gains through the use of the occupancy schedule and heat gains settings. The user

should be aware of the potential error involved in not including these settings.

Thermal Comfort

It is important that the modeller recognised the effect of occupancy on thermal

comfort calculations, for a room must be occupied to be comfortable or

uncomfortable. Note, however that occupancy does not affect heating and cooling

loads which is calculated from the HVAC on/off settings.

Heating and Cooling Loads

The modeller must be aware of how the heating and cooling loads will vary with

usage time. The dichotomy between summer and winter timing requirements may


111

often mean that although the house may be cold for a greater number of hours the

cooling requirements in summer are greater because users will use cool all day but

switch of heating at night. Efforts to mimic usage patterns must be used and the effect

of changing the on/off times of the system must be recognised.

U-values and Material Properties

It is recommended that where not available in the material library, the modeller gains

reputable thermal properties for the material in question from other sources. The

model is sensitive to changes in U-values and inaccuracies in one material will be

reflected in the results. At present the model does not automatically calculate thermal

lag time so it is recommended that these values be checked also.

66 .. 33 RR EE CC OO MM MM EE NN DD AA TT II OO NN SS FF OO RR TT HH EE HH OO MM EE OO WW NN EE RR

Use of Greenpower

The energy savings calculated in this project assumed that all power was from the

mains electricity grid. Reductions in carbon dioxide gas emissions will be enhance

through the use of greenpower options. It is recommended that the home owner

install ducted reverse cycle air conditioners or ducted natural gas heaters which have

the lowest running costs and greenhouse gases emissions (Sustainable Energy


Window Dressings

It is recommended that the homeowner invest in window dressings to reduce direct

heat gains in summer and heat loss in winter. In particular, Bedrooms 1 and 3 would

benefit from the use of curtains to retain heat.

Appliances

It is recommended that homeowners invest in 5 or 6 star rating appliances.

Ventilation

Draught excluders installed on the bottom of all external door and windows will

significantly reduce ventilation losses and gains.


112

Landscaping

Landscaping should be design to best maximise heat gains in winter and provide

shading in winter (Sustainable Energy Development Office, 2002). It is

recommended that shading on the west and southeast end of the house be maximised

and that low shrubbery be placed to the north of the house. Unshaded paving to the

north, east and west of the house should be avoided to minimise long wave reflection

gains. Landscaping should also be designed to maximise the benefits of summer

south westerlies.

Lighting

It is recommended that the home owner use fluorescent lights to minimise on-going

energy costs (Sustainable Energy Development Office, 2002).

66 .. 44 RR EE CC OO MM MM EE NN DD AA TT II OO NN SS FF OO RR TT HH EE PP RR OO JJ EE CC TT

HH OO MM EE BB UU II LL DD EE RR

Orientation

It is recommended that the Batavia household is only suitable for land lots that allow

the house to be orientated with the living areas facing north.

Windows

Alternative options to double-glazing that were not investigated in this report included

tinted and reflected films. Were timber considered undesirable, recycled aluminium

frames offer significant embodied energy reductions.

Insulation

The use of insulation in all project homebuilding designs is strongly encouraged.

Materials

The sourcing of material should be, where possible, from local sources to decrease

embodied energy inputs. This includes the use of local timber, or replacing Oregon

beams and sourcing goods manufactured in Western Australia rather than in the


113

eastern states. It is also recommended that alternative recycled materials, such as

recycled aluminium frames be investigated.

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121

AA pp pp ee nn dd ii cc ee ss

AA PP PP EE NN DD II XX AA :: EE NN VV II RR OO NN MM EE NN TT AA LL TT EE CC HH NN OO LL OO GG YY

CC EE NN TT RR EE FF LL OO OO RR PP LL AA NN


122

AA PP PP EE NN DD II XX BB :: TT HH EE RR MM AA LL PP RR OO PP EE RR TT II EE SS OO FF BB UU II LL DD II NN GG

MM AA TT EE RR II AA LL SS (( EE NN VV II RR OO NN MM EE NN TT AA LL TT EE CC HH NN OO LL OO GG YY

CC EE NN TT RR EE )) ..APPENDIX B: THERMAL PROPERTIES OF BUILDING MATERIALS IN ENVIRONMENTAL TECHNOLOGY CENTRE

Concrete floor 3 5.2 1 0 0.7 5Glass Sliding Door

5.356 5.36 1 0.95 0.34 0.39

Roller Door 5.55 5.57 0.966 0.95 1 0.39Wooden Door 2.36 3.9 1 0 1 0.4Partitions 0.853 4.4 1 0 0.44 7.7Zincalum Roof 0.13 1.01 0 0 1.02 2Rammed Earth Walls

2.86 5.56 1 0 0.22 10.5

Windows 5.46 6 0.9 0.95Refractive Index = 1.52

Alt Solar gain = 0.47

Inside Outside Inside Outside Inside Outside

Concrete floor 0.77 0.89 0.89 0 0 0 0Glass Sliding Door

7.54 0.86 0.86 0.13 0.13 0 0

Roller Door 7.54 0.86 0.86 0.13 0.13 0 0Wooden Door 1.65 0.85 0.85 0.038 0.038 0 0Partitions 0 0.89 0.89 0 0 0 0Zincalum RoofRammed Earth Walls

0.9 0.9 0 0 0 0

Windows 5.52 0.86 0.86 0.13 0.13 0 0

ROOF

Width (mm) Density (kg/m3)Specific

Heat (J/kgC)Conductivity

(W/mC)Hatch

Zinc Metal Deck (Foil)

16 1400 900 80 75

Air gap 150 1.3 1004 0.025 15

Insulation - Glass Fibre Quilt

50 12 840 0.04 45

Plaster Board 10 1250 1088 0.431 85

OUTSIDE

INSIDE

Component

Thermal Decrement

(0-1)

Thermal Lag (hr)

Admittance (W/m2K)

Greenhouse Gas Emmision

(kg)

Emissivity Specularity Roughness

Component U-value (W/m2K)Solar

Absorption (0-1)

Transparency (0-1)

INSIDE

OUTSIDE


123

AAPPPPEENNDDIIXX CC:: BBAATTAAVVIIAA FFLLOOOORR PPLLAANN


124

AA PP PP EE NN DD II XX DD :: TT HH EE RR MM AA LL PP RR OO PP EE RR TT II EE SS OO FF BB UU II LL DD II NN GG

MM AA TT EE RR II AA LL SS (( BB AA TT AA VV II AA ))

APPENDIX D: THERMAL PROPERTIES OF BUILDING MATERIALS IN BATAVIA HOUSEHOLD

CEILING

Plaster, R2 Insulation

0.5 0.9 0.44 0 0.32 0.7

Plaster, R3.5 Insulation

0.09 1.05 0.44 0 1 0.7

Plaster, Cellulose 0.12 1.26 0.44 0 0.88 0.7

Plaster, Wool Insulation

0.11 1.36 0.44 0 0.83 0.7

Plaster, Joists 5.16 4.96 0.17 0 0.98 0.3

PLASTER R2 INSULATION

Components Width (mm)Density (kg/m3)

Specific Heat (J/kgC)

Conductivity (W/mC)

Hatch

OUTSIDE

Air gap 150 1.3 1004 0.025 15Insulation - R2 Glass Fibre Quilt

50 12 840 0.04 45

Insulation - R3.5 Glass Fibre Quilt

185 12 840 0.04 45

Cellulose Insulation 105 43 1380 0.042 45Wool Insulation - resin bonded

100 99 1000 0.036 45INSIDE

Plaster Board 10 1250 1088 0.431 85

DOORS AND WINDOWS

Heavy Weight

Light Weight

Hollow Core Plywood Door

2.98 0.65 0.244 0 0.98 0.4

Soild Core Pine Door

2.31 3.54 0.19 0 0.98 0.4

Single Glazed Al Framed Window

5 5 0.94 0.92 1.74 0.47 0.64

Double Glazed Al Framed Window

2.7 2.8 0.81 0.92 1.74 0.42 0.56

Double Glazed Timber Framed Window

2.9 2.9 0.81 0.92 1.74 0.34 0.43

Single Glazed Timber Framed Window

5.1 5 0.94 0.92 1.74 0.47 0.64

Type Component Width (mm) Density (kg/m3)Specific Heat

(J/kgC)Conductivity

(W/mC)Hatch

DOUBLE GLAZED Hollow Core Plywood Door

Plywood 3 530 1400 0.14 85

Air Gap 34 1.3 1004 0.25 15Plywood 3 530 1400 0.14 85

Soild Core Pine Door

Wood Pine 40 550 2301 0.343 91

Single Glazed Window

Standard Glass 6 2300 836.8 1.046 75

Double Glazed Window

Standard Glass 6 2300 836.8 1.046 75

Air Gap 30 1.3 1004 0.25 15Standard Glass 6 2300 836.8 1.046 75

Refractive IndexAlt Solar Gain

Transparency (0-1)

Thermal Decrement (0-1)

Thermal Lag (hr)

Type U-value (W/m2K)Admittance

(W/m2K)

Solar Absorption (0-

1)

Transparency (0-1)


Thermal Lag (hr)


(W/m2K)


1)

INSIDE

OUTSIDE

OUTSIDE

INSIDE


125

FLOORS

Suspended Concrete Floor

85 3 5.2 0.322 0 0.7 4

100 2.24 5.28 0.322 0 0.33 4.2

Suspended Concrete Floor Carpet

85 2.56 4.2 0.9 0 0.7 4

100 0.35 1.74 0.9 0 0.09 4.2

Suspended Concrete Floor Tiles

85 2.9 5.21 0.475 0 0.69 4.1

100 0.4 4.6 0.475 0 0.09 4.1

Suspended Concrete Floor Timber

85 2.16 2 0.87 0 0.97 0.7

100 0.39 3.73 0.87 0 0.13 0.7


(J/kgC)Conductivity

(W/mC)Hatch

CARPETED FLOORSuspended Concrete Floor

Concrete 85 3800 656.9 0.753 35

Carpet Underlay 5 160 1732 0.045 95Carpet 15 240 732 0.055 79

Tiles Screed 5 2000 656.9 0.753 119Ceramic Tiles 10 1900 656.9 0.309 79

Timber Flooring 10 650 1200 0.14 115

ROOF

Colourbond 5.55 5.56 0 0 1 0


(J/kgC)Conductivity

(W/mC)Hatch

ColourbondZinc Metal Deck (Foil)

16 1400 900 80 75

WALLS

DOUBLE BRICK

Double Brick 1.78 4.59 0.39 0 0.37 8

Brick Concrete Block 1.6 4.3 0.58 0 0.31 8

Single Brick 2.62 4.38 0.32 0 0.7 5Brick Cavity Concrete Block

1.36 3.49 0.39 0 0.41 8

Solid Double Brick 1.95 4.55 0.39 0 0.39 8 BRICK TIMBER FRAMETimber Clad Insulated Masonary

0.44 4.94 0.39 0 0.37 8

Timber Clad Masonary

0.3 4.96 0.39 0 0.35 6

Brick Timber Frame 0.3 1 0.39 0 0.55 6


(J/kgC)Conductivity

(W/mC)Hatch

Transparency (0-1)


Thermal Lag (hr)


(W/m2K)


1)

Transparency (0-1)


Thermal Lag (hr)


(W/m2K)Solar

Absorption (0-

Type Thickness (mm)U-value

(W/m2K)

Admittance (W/m2K)

Solar Absorption (0-1)

Transparency (0-1)Thermal

Decrement (0-Thermal Lag (hr)

OUTSIDE

INSIDE

WALLS

DOUBLE BRICK

Double Brick 1.78 4.59 0.39 0 0.37 8

Brick Concrete Block 1.6 4.3 0.58 0 0.31 8

Single Brick 2.62 4.38 0.32 0 0.7 5Brick Cavity Concrete Block

1.36 3.49 0.39 0 0.41 8

Solid Double Brick 1.95 4.55 0.39 0 0.39 8 BRICK TIMBER FRAMETimber Clad Insulated Masonary

0.44 4.94 0.39 0 0.37 8

Timber Clad Masonary

0.3 4.96 0.39 0 0.35 6

Brick Timber Frame 0.3 1 0.39 0 0.55 6


(J/kgC)Conductivity

(W/mC)Hatch

Double Brick Brick Masonary 110 2000 836.8 0.711 25 INSULATED TIMBER Air Gap 50 1.3 1004 0.025 5 CLAD MASONARYBrick Masonary 110 2000 836.8 0.711 25Plaster 10 1250 1088 0.431 85

Brick Concrete Block Concrete Cinder 220 1600 656.9 0.335 35

Timber Clad Insulated Masonary

Wood Pine 15 550 2301 0.343 95

Fibre Quilt 75 12 80 0.04 45Brick Masonary 110 2000 836.8 0.711 25Plaster 10 1250 1088 0.431 85


(W/m2K)


1)

Transparency (0-1)


Thermal Lag (hr)

OUTS

DE

OUTSIDE

INSIDE

OUTSIDE

NS

DE


126

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EE MM BB OO DD II EE DD EE NN EE RR GG YY CC OO EE FF FF II CC II EE NN TT SS

Building Component Material Quantity UnitTotal (kg or m3) Cost

MJ/kg or MJ/m3 Energy (MJ) % Total

CONCRETE SLAB 85mm thick 23.4 m3 23.4 4,609.80$ 3890.0 91026.0 14.2Timber Flooring kiln dried, dressed 0.7 m3 0.7 1380.0 941.7 0.1Carpet nylon 1.1 m3 1.1 1480.0 1611.9 0.3

WALLSFace Brick Cored 17.8 m3 17.8 3,697.65$ 5170.0 92069.5 14.4

Solid 0.5 m3 0.5 130.68$ 5170.0 2624.4 0.4Internal Brick Fastwall 18.8 m3 18.8 3,850.08$ 5170.0 97111.8 15.2

Cored 1.1 m3 1.1 197.57$ 5170.0 5436.0 0.9Sand Yellow 3.0 loads 30.0 289.00$ 232.0 6960.0 1.1

Plaster 1.0 loads 10.0 123.00$ 232.0 2320.0 0.4Cement Grey Cement 50.0 bags 892.5 187.00$ 7.8 6961.5 1.1

Lite cement 96.0 bags 1713.6 443.00$ 7.8 13366.1 2.1Plaster Wall 401.5 m2 2.0 6460.0 6460.0 1.0

FRAMESSteel Structural 68.7 m 0.2 1,090.69$ 274570.0 52840.9 8.3Ceiling Beams 14.6 m 1.2 137.65$ 1550.0 1808.0 0.3Roof timber hardwood* 765.0 m 2.1 1,327.19$ 1550.0 3266.3 0.5

pine* 1246.6 m 5.2 2,456.55$ 1380.0 7225.3 1.1Oregan Glulam 26.1 m 0.5 752.15$ 2530.0 1267.8 0.2Steel 42.4 m 0.2 772.08$ 274570.0 53540.8 8.4Steel fascia 54.0 m 0.0 139.32$ 274570.0 5.3 0.0

WINDOW FRAMES Al 1.0 unit 0.1 6,752.00$ 588600.0 39098.4 6.1

ROOF Hardiflex (fibrecement) 0.1 m3 0.1 91.43$ 13550.0 1009.7 0.2Metal Deck 0.3 m3 0.3 7,600.00$ 274570.0 95455.0 14.9Weatherboard 0.0 m3 0.0 22.32$ 21300.0 26.8 0.0

OTHERSIsulation Gold Bats 187.4 m2 19.7 800.20$ 970.0 19086.7 3.0

R2 Batts 10.9 m2 1.1 72.70$ 970.0 1105.1 0.2Ceiling Plasterboard 2.2 m3 2.2 3,639.92$ 5890.0 12818.9 2.0Skylights Vented 0.0 m2 0.0 320.00$ 375450.0 135.2 0.0

Windows 56.9 m2 0.1 40600.0 4624.1 0.7Tiles Walls 26.8 m2 0.1 655.88$ 5250.0 702.7 0.1

grout 21.0 kg 21.0 52.50$ 0.0 0.0 0.0Floor (bathroom) 12.3 m2 0.1 300.38$ 5250.0 321.8 0.1

Brick paving Bluestone (65mm) 2000.0 bricks 3.4 724.00$ 5170.0 17622.5 2.8Flyscreens Al 1.0 unit 0.0 475.00$ 588600.0 0.0 0.0Joinery Steel Frames 10.0 unit 0.0 322.48$ 273180.0 0.0 0.0

Door frames 3.0 unit 0.0 112.17$ 273180.0 0.0 0.0Total 638850.2


127

AA PP PP EE NN DD II XX FF :: TT OO TT AA LL EE MM BB OO DD II EE DD EE NN EE RR GG YY WW II TT HH

RR EE PP LL AA CC EE MM EE NN TT AA NN DD WW AA SS TT EE FF AA CC TT OO RR SS (( BB AA TT AA VV II AA ))

Building Component Material GJ Wastefactor*Replacement factor* GJ With Replacement

CONCRETE SLAB 85mm thick 91.0 0.025 1 93.3Timber floor boards 0.9 0.05 1.3 1.3Carpet 1.6 0.05 1.3 2.2

WALLSFace Brick Cored 92.1 0.025 1 94.4

Solid 2.6 0.025 1 2.7Internal Brick Fastwall 97.1 0.025 1 99.5

Cored 5.4 0.025 1 5.6Sand Yellow 7.0 0.025 1 7.1

Plaster 2.3 0.025 1 2.4Cement Grey Cement 7.0 0.025 1 7.1

Lite cement 13.4 0.025 1 13.7Plaster Wall 6.5 0.025 1 6.6FRAMESSteel Structural 52.8 0.05 1 55.5Ceiling Beams 1.8 0.05 1 1.9Roof timber hardwood* 3.3 0.025 1 3.3

pine* 7.2 0.025 1 7.4Oregan Glulam 1.3 0.025 1 1.3Steel 53.5 0.05 1 56.2Steel fascia 0.0 0.05 0.0

WINDOW FRAMES Al 39.1 0.025 1 40.1

ROOF Hardiflex (fibrecement) 1.0 0.05 1.3 1.4Metal Deck 95.5 0.05 1 100.2Weatherboard 0.0 0.025 1.3 0.0

OTHERS 0.0 0.025 1 0.0Isulation Gold Bats 19.1 0 1.2 22.9

R2 Batts 1.1 0 1.2 1.3Ceiling Plasterboard 12.8 0.025 1 13.1Skylights Vented 0.1 0 1.3 0.2

Windows 4.6 0 1.3 6.0Tiles Walls 0.7 0.025 1.3 0.9

grout 0.0 0.025 1.3 0.0Floor (bathroom) 0.3 0.025 1 0.3

Brick paving Bluestone (65mm) 17.6 0.025 1 18.1Flyscreens Al 0.0 0.025 1.3 0.0Joinery Steel Frames 0.0 0.05 1 0.0

Door frames 0.0 0.025 1 0.0

TOTAL (GJ) 638.9 666.2* Chen et al 2001 TOTAL (GJ/m2) 3.0 3.1


128

AA PP PP EE NN DD II XX GG :: CC OO SS TT CC AA LL CC UU LL AA TT II OO NN SS

CONCRETE SLAB SOLAR PERGOLAOriginal cost (85mm) * 4,690.00$ Cost of pine* 1.97$ /mNew cost (100mm) 5,241.76$ Need 1810 mmCost increase 551.76$ by 1000 mm

Total Length 19.29 mTotal Cost 38.02$

WINDOWSWall Constituents* 22.21$ per m2

1.8 GJ/m2Windows* 118.57$ per m2

0.1 GJ/m2MJ m2 MJ/m2

Face Brick Cored 92070 234.3 392.9Solid 2624 6.7 392.9

Internal Brick Fastwall 97112 208.7 465.3Cored 5436 11.7 465.3

Sand Yellow 6960 401.5 17.3Plaster 2320 401.5 5.8

Cement Grey Cement 6962 401.5 17.3Lite cement 13366 401.5 33.3

Plaster Wall 6460 401.5 16.11.8 GJ/m2

Hence you need to save 1.7 GJ per m2 of glass replaced with wall !!479.2 kWh in 25 years19.2 kWh /yr_m2

Decreased window in Bed 1Original 1810x1914 mm2Reduced Size 1810x850 mm2

1.93 m2 Decrease size of living doorOriginal 2410x2088 mm2Reduced Size 2410x1108 mm3Replace 2.72 m2 COST 447.52$ Saving

Single Glazed Windows~ 10.00$ per m2Discount for Dale Alcock~ 20%Area to be glazed 47.72 m2Double Glazing Cost 381.72$

NORTH FACING EAVES DECREASE VENTILATIONDepth of eaves 750 mm Cost of seals per window^ 40.00$ Length of extension 14040 mm Number of Windows 10Area of extension 10.53 m2 Number of Doors 3Original Roof Cost* 7,600.00$ Total Cost 560.00$

Plus additions 7,830.20$ Difference 230.20$

CONCRETE BLOCKSCost of fastwall brick $0.80 per brick * Alcock, 2002Size 305x90x162 ^ Horn, 2002Cost of concrete block $1.55 per brick # Midland Brick, 2002Size 390x90x162 ~ Jasons WindowsArea to be replaced 181 m2Replace 3667 brickswith 2445 blocksNet Cost $856

Documents

Energy Efficiency for Everyone · 2011. 5. 31. · Energy Efficiency for Everyone i AAbbssttrraacctt At present the application of solar passive design principles in new residences