Baseline Energy Consumption and Greenhouse Gas Emissions In Commercial Buildings in Australia

Part 2 - Appendixes November 2012

Council of Australian Governments (COAG) National Strategy on Energy Efficiency

Baseline Energy Consumption and Greenhouse Gas Emissions in Commercial Buildings in Australia – Part 2 - Appendixes

Prepared by pitt&sherry with input from BIS Shrapnel and Exergy Pty Ltd

Published by the Department of Climate Change and Energy Efficiency

www.climatechange.gov.au

ISBN: 978-1-922003-84-3 © Commonwealth of Australia 2012 This work is licensed under the Creative Commons Attribution 3.0 Australia Licence. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/au The Department of Climate Change and Energy Efficiency asserts the right to be recognised as author of the original material in the following manner:

or © Commonwealth of Australia (Department of Climate Change and Energy Efficiency) 2012. IMPORTANT NOTICE – PLEASE READ This document is produced for general information only and does not represent a statement of the policy of the Commonwealth of Australia. The Commonwealth of Australia and all persons acting for the Commonwealth preparing this report accept no liability for the accuracy of or inferences from the material contained in this publication, or for any action as a result of any person’s or group’s interpretations, deductions, conclusions or actions in relying on this material. Acknowledgment As part of the National Strategy on Energy Efficiency the preparation of this document was overseen by the Commercial Buildings Committee, comprising officials of the Department of Climate Change and Energy Efficiency, Department of Resources, Energy and Tourism and all State and Territory governments.

Table of Contents

Appendix A – Statement of Requirements ............................................. 1

Appendix B – Bibliography ........................................................................... 2

Appendix C – Model Documentation ........................................................ 4

Appendix D – Top-down Model Validation ........................................... 25

Appendix E – Statistical Analysis ............................................................. 41

Appendix A – Statement of Requirements

Page 1 of 95

Appendix A – Statement of Requirements

The project requirements, as detailed in the contract documentation, consist of the following deliverables:

a) A methodology for the efficient and accurate collection of energy consumption data from a representative sample of buildings for the 2008-2009 financial year.

The data will include:

i. Whole of building energy use

ii. Tenancy energy use

iii. End-use energy consumption (e.g. lighting, HVAC, IT services, elevators, water heating etc.)

iv. Relevant business parameters (e.g. purpose, location, floor area, number of seats, number of floors, energy services included in base building etc.)

v. From all purchased and on-site energy sources, including;

vi. Electricity

vii. Gas

viii. Solid fuel

ix. Liquid fuel

x. Renewable energy

b) A model for collecting, collating, storing and reporting commercial building energy consumption data which can be applied to this and future data collection processes

c) A report on the energy consumption of and greenhouse gas emissions from commercial buildings in 2008 – 2009

d) A report detailing trends from 1999 to the present and predicting trends in energy consumption of, and greenhouse gas emissions from, commercial buildings to 2020.

The contractor has stated they will provide the following deliverables, in order to meet the above requirements:

1. A documented methodology for the project, for approval by the client at the project commencement and for eventual inclusion in the Final Report (part of deliverable a)

2. An adaptable, documented and quality assured model for collecting, collating, storing and reporting commercial building energy consumption (deliverables a & b)

3. An estimate of the energy used and associated greenhouse gas emissions from the commercial building sector in Australia for the financial year 2008-09, broken down by sector, state and territory and climate zone (deliverable c)

4. An analysis of whole of building energy use, tenancy energy use and end use energy for a range of commercial building types (deliverable a cont’d)

5. A report analysing trends for commercial building energy use and associated greenhouse gas emissions to 2020, based on a number of different scenarios, as well as an analysis of past trends, 1999-2009 (deliverable).

A separate report analysing the publically-owned segment of the commercial building stock.

Appendix B - Bibliography

Page 2 of 95

Appendix B – Bibliography

ABARES 2010: Australian Bureau of Agricultural and Resource Economics and Sciences, Australian Energy Statistics – Australian Energy Update 2010

ABARES (2011a): Australian Bureau of Agricultural and Resource Economics and Sciences, Energy in Australia 2011, Commonwealth of Australia, 2011

ABARES (2011b): Schultz, A and Petchey, R. 2011, Energy update 2011, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, June

ABCB (2010): Australian Building Codes Board, BCA2010: Building Code of Australia: Class 2 to Class 9 Buildings: Volume 1, 2010

ABS (1999): Australian Bureau of Statistics, 1268.0.55.001 – ABS Functional Classification of Buildings, 1999 (last updated April 2006) accessed at http://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/1268.0.55.001Main+Features11999?OpenDocument

ABS (2011a): Australian Bureau of Statistics, 8731.0 – Building Approvals, Australia, June 2011, accessed at http://www.abs.gov.au/ausstats/abs@.nsf/mf/8731.0

ABS (2011b): Australian Bureau of Statistics, 8752.0 – Building Activity, Australia, March 2011, accessed at http://www.abs.gov.au/ausstats/abs@.nsf/mf/8752.0

ABS (2011c): Australian Bureau of Statistics, 8755.0 – Construction Work Done, Australia, Preliminary, March 2011, accessed at http://www.abs.gov.au/ausstats/abs@.nsf/mf/8755.0

ABS Cat. No. 4660.0 - Energy, Water and Environment Management, 2008-09

ABS Cat. No. 4602.0.55.001 Environmental Issues: Energy Use and Conservation

AGO (1999): Australian Greenhouse Office, Baseline Study of Greenhouse Gas Emissions from the Commercial Buildings Sector: with projections to year 2010, prepared by EMET Consultants Pty Ltd and SOLARCH GROUP, May 1999. Unpublished report

AIHW (2011): Australian hospital statistics 2009-10. Health services series no. 40. Cat. no. HSE 107. Canberra: AIHW

Altus Page Kirkland (2012): Online publication, Measurement of Building Areas, accessed at http://www.pagekirkland.com/home.asp?pageid=7132854A3C01D87C on 2 May 2012

Australian National Audit Office, 2012. Administration of the National Greenhouse and Energy Reporting Scheme Report

BREE, 2011, Resources and Energy Statistics 2011, Canberra

COAG (2009): Council of Australian Governments, National Strategy on Energy Efficiency, July 2009, accessed at http://www.coag.gov.au/coag_meeting_outcomes/2009-07-02/docs/Energy_efficiency_measures_table.pdf

DCCEE (2010): Department of Climate Change and Energy Efficiency, Report of The Prime Minister’s Task Group on Energy Efficiency, July 2010

DCCEE (2011): Department of Climate Change and Energy Efficiency, National Greenhouse Accounts Factors Workbook 2011, Commonwealth of Australia, 2011

DCCEE (2011b): Department of Climate Change and Energy Efficiency, Energy Use in the Australian Government’s Operations – 2008-09, Commonwealth of Australia, 2011

Appendix B - Bibliography

Page 3 of 95

DCCEE (2012): Department of Climate Change and Energy Efficiency, Australian National Greenhouse Accounts: national inventory report 2010 volume 1, Commonwealth of Australia, 2012

DEWHA (2008): Department of Environment, Water, Heritage and the Arts, Energy Use in the Australian Residential Sector 1986-2020, Commonwealth of Australia, 2008

DRET (2011): Department of Resources, Energy and Tourism, Continuing Opportunities Energy Efficiency Opportunities (EEO) Program – 2010 Report, A look at results for the Energy Efficiency Opportunities Program 2006-2010, Taken from public reports of assessments undertaken during the period July 2006-June 2010, 2011

McLachlan Lister et al (2009): McLachlan Lister and DataBuild, Commercial Buildings Energy Use Baseline Pilot Study, June 2009. Unpublished report

NFEE (2007): National Framework for Energy Efficiency, Stage 2 Consultation Paper, September 2007, accessed at http://www.ret.gov.au/Documents/mce/_documents/National_Framework_On_Energy_Effeciency%28NFEE%29_Stage2_Consultation20070904133959.pdf

Northern Territory Government, Building Energy and Greenhouse Report, 2006-7

NSW Government, Energy Use in Government Operations, Annual Report 2002-03

pitt&sherry (2010): The Pathway to 2020 for Low-Energy, Low-Carbon Buildings in Australia: Indicative Stringency Study, July 2010, accessed at http://www.climatechange.gov.au/what-you-need-to-know/~/media/publications/buildings/pathway-to-2020.ashx

Property Council of Australia (2011): Office Market Report, available for purchase online, http://www.propertyoz.com.au/Advocacy/Policy.aspx?p=69&id=57

Sandu, S and Petchey, R 2009, End use energy intensity in the Australian economy, ABARE research report 09.17, Canberra, November

Sandu, S and Syed, A 2008, Trends in Energy Intensity in Australian Industry, ABARE Report 08.15 Canberra, December

South Australian Department of Health, Annual Report, 2009-10

Tanaka, K, IEA Information Paper, Assessing measures of energy efficiency performance and their application, International Energy Agency, 2008

Victorian Department of Health, Annual Report, 2010-11

WebFinance Inc, InvestorWords.com, viewed August 2011, http://www.investorwords.com/5662/model.html

Appendix C – Model Documentation

Page 4 of 95

Appendix C – Model Documentation

AC.1 - Overview The model – known as NRBuild v1.1 (Non-Residential Buildings Model, Version 1.1) – is a large (168 Mb) MS Excel workbook comprising 76 interlinked spreadsheets (also referred to as ‘pages’ or ‘tabs’). The primary purpose of NRBuild is to estimate energy use (by major fuel types) and (scope 1 and 2) greenhouse gas emissions attributable to a range of non-residential building types in Australia, at the national level and by state/territory and region (capital city vs regional), over the period FY1999 to FY2020. In some cases, the model also resolves ownership type (government- or private-owned). The building types modelled in v1.1 comprise:

Offices (Base Buildings)

Offices (Tenancies)

Offices (Base + Tenancies)1

Hotels

Supermarkets (Total)

Supermarkets (Excluding those in shopping centres)

Shopping Centres (Base Buildings)

Shopping Centres (Tenancies)2

Shopping Centres (Base + Tenancies)

Hospitals

Schools

TAFE campuses

Universities

Public buildings (a compilation of libraries, galleries and museums)

Law Courts.

1 Note that ‘whole building’ energy data records for offices are not relied upon, as discussed in Appendix 1. 2 Including a facility to vary the weighting of supermarkets within shopping centres.

Appendix C – Model Documentation

Page 5 of 95

Figure AC.1.1 - NRBuild Model Schematic

Source:pitt&sherry

Figure AC.1 above provides a high-level schematic of the model’s operation. More detailed descriptions are provided in the sections below, specific to each major building type.

AC.2 - Stock Model - Overview BIS Shrapnel, an experienced service provider in the commercial buildings modelling and forecasting field, developed a bespoke stock model for this project. The model provides estimates of the stock of commercial buildings for the period from 1998/99 to 2019/20 by building classification and by geographic fields (state, capital city/regional), for a wide range of building types and sub-types. BIS Shrapnel drew on a wide range of data sources for this model including internal data, ABS data, industry associations (such as the Property Council of Australia) and data from Commonwealth, state and local governments. The primary metric used in the stock model is floor space measured in thousands of square metres (‘000m2). However, the definition of floor space varies across different building classifications and different data sources, with a wide variety of concepts employed but not necessarily made transparent. Generally, BIS Shrapnel has attempted to normalise all stock estimates to a ‘net lettable area’ concept. Retail buildings and data collections, however, typically utilise a GLAR concept. Information on the number of buildings is extremely limited. For example, it is possible to know the number of schools, universities, VETs or hospital precincts in Australia, but each one may be comprised of many separate buildings of many different types.

Appendix C – Model Documentation

Page 6 of 95

Other metrics have been incorporated or utilised where available. For example, for hospitals the stock model includes information on the number of hospital beds. Details on the other metrics are included in the discussion of individual building classification. These metrics are important indicators of activity associated with buildings and may in some cases correlate significantly with energy use. The stock model is based on selected classifications from the Australian Bureau of Statistics functional classifications of buildings (see Figure 16.2 below). The relevant classifications broadly include the sub-classifications of the ‘Commercial Building’ and ‘Other Non-residential’ functional classifications. ‘Industrial buildings’ are not included as they are outside of the scope of this study. In addition, the stock model provides floor space estimates and metrics for sub-classifications not resolved by the ABS. For example, the education building classification includes sub classifications for schools, higher education, and vocational education and training. Figure AC.2 - ABS Functional Building Classifications

(nec=not elsewhere classified) Source: BIS Shrapnel

There is no single ‘right’ classification structure for commercial buildings that will be optimal for all purposes. The choice depends primarily upon the expected uses of the resulting model. In principle the candidate approaches would include:

Building physical form/construction detail (size, construction methodology, building materials, etc)

Building function as above (offices, retail, etc)

The Australian Building Codes Board Building Classification System (Classes 1 – 10, which are “…determined by the purpose for which [the building] is designed, constructed or adapted to be used” (Australian Building Codes Board 2010, p. 39)

Non-residential buildingResidential building

Commercial buildingIndustrial building Other non-residential building

Retail/wholesale trade

Transport

Offices

Other commercial nec +

Factories

Warehouses

Agriculture/aquaculture

Other industrial nec

Educational

Religious

Aged care

Health

Entertainment/recreation

Accommodation

Other non-residential nec

= Included in project

ABS functional building classifications

Appendix C – Model Documentation

Page 7 of 95

The ANZSIC system, which assigns businesses and other organisational activities to economic sectors.

The benefit of focusing on the physical buildings, as distinct from the nature of their ownership, use or the economic sector(s) to which their owners and users belong, would be that these physical characteristics are less prone to change throughout the building’s life. By contrast, building ownership may change often, as may the economic sectors to which the building’s occupants or owners belong. This is particularly the case for multi-tenanted buildings such as commercial offices and shopping centres, but also warehouses. The primary function of buildings may also change through time (e.g. from office to hotel to strata residential accommodation), while the shares of different functions within a single building may also change through time (shares allocated to retail, office and accommodation within a single building, for example). This issue is discussed further under ‘mixed use buildings’. However, practical considerations dictate that the first approach – based on the physical characteristics of buildings – is not a good candidate for the top-level classification structure: data on these physical characteristics is too limited to include in the model at this time.3 The ANZSIC system has the advantage of being long-established and well defined, and it is widely used in ABS publications. However, as noted, classifications are by economic sector and provide a weak indication at best of building types or energy use. By default, then, a ‘building function’ approach may be optimal, notwithstanding that building function may change through time. The 1999 EMET/Solarch study (AGO 1999) used a functional approach similar to the ABS classifications discussed below. Further, we note that building energy use generally correlates better with building function than with other parameters such as construction type. The ABS framework distinguishes residential and non-residential buildings, and then within non-residential, it distinguishes industrial, commercial and ‘other non-residential’ buildings. Broadly, we adopted this framework, focusing on ‘commercial and ‘other’ buildings, implementing classifications (and often more detailed sub-classifications) as data would permit. Industrial buildings fall outside the scope of the study, while within these covered categories, certain types (e.g., some transport-related buildings and religious buildings) are excluded from the model given poor data availability together with an expectation of relatively low energy use in these building types. However, as already noted, where building stock data was available on commercial buildings types that was excluded from the current model, this is still be included within the building stock model itself. Should sample sizes for these building types improve with future data collections, this would enable the model to be updated, after the completion of this project, to accommodate the resulting higher level of detail. The commercial building stock model segments the commercial building stock by state and territory, and then by capital city/non-capital city. The ACT is treated as a single unit. BIS Shrapnel adopted a multi-faceted approach to estimating the building stock. To the greatest extent feasible, ‘bottom up’ estimates of building stock has been undertaken, assembling information from a wide array of primary or ‘hard’ data sources. These included BIS Shrapnel, the PCA, government departments and local councils. The search for this data proved more successful than had been expected, and therefore ‘hard’ data is the primary source for many of the stock estimates.

3 Where available – for example from state/territory governments, the physical characteristics of buildings will be included within the data set itself, even if these samples are presently too small to be reliable enough for the model.

Appendix C – Model Documentation

Page 8 of 95

Second, where actual building stock data was unavailable, alternative metrics were examined as measure of both activity and also a basis for estimating floor area. As an example, it was possible to estimate hotel floor space using data on hotel rooms by (quality) star rating and state. Frequently the quality of the estimation is strengthened by the existence of partial building stock data. For example, the data search process resulted in actual data on prison floor space for two states and a small sample of individual prisons. This enabled verification of typical prisoner capacity/m2 ratios, which were applied to other states. Third, there was limited use of ABS building commencement/completion data. There are a number of shortcomings involved in the use of the ABS building activity data. In particular:

The ABS building activity data is in value terms, and therefore needs to be converted to a m2 equivalent using a cost measure

Building completion data provides only a partial picture of net growth in floor space because of the unknown level of deletions (e.g., demolition)

Building alterations and additions may or may not involve additions to floor space

The ABS building activity data is of limited value in the estimation of sub-classifications, due to the limited disaggregation of the ABS series.

Other measures were also used to either complete the estimate of building stock or act as a verification check. For example, often per capita floor space ratios provide a verification measure. For example, the principal method used to estimate stand-alone office floor space in regional areas, where actual data was unavailable, was the measure of office employment. Office space per capita for the region acted as a ‘sense’ check. The methodological approach is shown schematically in Figure 16.3 below. Figure AC.3 - Commercial Building Stock Estimation Methodology

Source: BIS Shrapnel

Is there actual stock data?

Use actual stock data

Are there gaps?

Are there other estimation tools (egABS approvals)

Are there gaps?

Final stock figure

Are there high quality proxy data?

Use high quality proxy data?

Use other estimation tools (egABS approvals)

Are there other useful data (egemployment)

Use other useful data (egemployment)

Are there gaps?

Verification

Are there other estimation tools (egABS approvals)

Are there high quality proxy data?

Are there other useful data (egemployment)

Yes

Yes Yes

Yes Yes Yes

No No No

No No No

Yes Yes Yes Yes

Appendix C – Model Documentation

Page 9 of 95

Mixed Use Buildings It is important to note that there is no accepted statistical methodology in Australia for recognising mixed use buildings. Thus, even though there are a large number of buildings where the total space is allocated to different functional purposes, the ABS notes that in compiling its building activity data “…a building is classified according to its intended major function” (ABS 1999). In the case of a large building with multiple purposes (such as a building with offices and a hotel), the ABS attempts to split the details by function, although the methodology used for this purpose is not clear. It is clear that the functional classification is applied at the planning/approval stage in a building’s life and that, therefore, subsequent changes in the function of the building would not be captured in the ABS data: “The economic building collections classify buildings as they are reported in on approval documents and during construction activity, and therefore provide a count of buildings according to their original stated function (ABS 1999).” Also, “the function of the completed building is determined at the time the building approval is lodged”. The same applies to building ownership: “…buildings are further classified by ownership, according to the sector (i.e. public or private) of the intended owner of the building at the time of the approval (ABS 1999)”. In our view, this treatment of mixed use buildings will become increasingly problematic through time, as there is an increasing trend towards mixed use buildings and also for the function of buildings to change through time. In this project, we allocated buildings on the basis of ‘major intended function’ (the function associated with the largest share of the building in m2). . However, in most cases the data used did not require us to determine the building allocation. We note that this issue (including the allocation of energy production and consumption within mixed use buildings) may deserve further attention in future buildings research.

ANZSIC Codes The ABS uses ANZSIC classifications to segment a wide range of economic data including business counts, production and employment. We believe that there are limitations in the use of ANZSIC to classify buildings for the purpose of compiling the building stock model. These are:

ANZSIC classifications attached to a building can readily change as ownership/tenancies change

An office building will often have multiple ANZSIC classifications using the building

ABS building activity data, which was a tool in the compilation and verification of the building stock estimates, is not compiled on an ANZSIC classification basis.

Notwithstanding the differences between ANZSIC classifications and the ABS functional classifications of buildings, there are obvious areas of likely overlap. For example, most buildings under the functional classification ‘education’ would be used by units falling within the ANZSIC division ‘education and training’. However, there are also important differences, often arising from the different purposes of the classification systems. Examples of these include:

Restaurants and cafes completions are included in retail/wholesale trade under the functional classifications of buildings but these activities would be included in the accommodation, cafes and food services division of ANZSIC

The functional classification of buildings does not include separate classifications for categories such as finance and insurance, property and business services. Many of these activities would be likely be undertaken in buildings with the functional classification of offices

The functional classification of buildings has fewer levels of disaggregation than ANZSIC. Since the focus of this study is buildings, rather than economic classification of building owners or occupants and for the reasons noted above, we believe that relying upon ANZSIC codes as a primary classification structure would introduce excessive uncertainty into our model.

Appendix C – Model Documentation

Page 10 of 95

Geographic segmentation, including climate zones The NRBuild model segments the relevant building stock by state and territory, and then by capital city/non-capital city. The ACT and Canberra is treated as a single unit. It should be noted that data from non-ABS sources may use different geographic definitions of capital city, creating modest imprecision in the capital city/rest of state segmentation where this data is used as the basis for stock estimation.

Data Sources Data source used for compiling the stock estimates included: Government, broad building data:

Sydney City Floor Space and Employment Survey, 2006

Melbourne City Land Use and Employment Survey, 2008

WA Department of Planning, Perth Metropolitan Region Land Use and Employment Survey, 2008

ACT Department of Planning, planning data statistics

SA Department of Planning, Retail Database 2007

Adelaide Land Use and Employment Survey

VicClue data. Government, government buildings data:

OSCAR related data

Government departments and departmental annual reports. Building data, private sector sources:

Property Council of Australia, Office Market Reports

Property Council of Australia, Shopping Centre Directory

BIS Shrapnel internal office space data

BIS Shrapnel internal retail space data

Small scale sampling from mapping services, company and media reports. Sources of other metric and proxy data:

Australian Institute of Health and Welfare, selected data

Department of Health and Ageing, selected data

ABS, Tourist Accommodation data

ABS, Census data

ABS, Labour force data

ABS, Schools data

Department of Education, Employment and Workplace Relations, Higher Education data

Screen Australia data

National Centre for Vocational Education Research

Rawlinsons Australian Construction Handbook

Appendix C – Model Documentation

Page 11 of 95

Productivity Commission, Report On Government Services

Various previous studies on industry segments and floorspace.

Backcasting the building stock Backcasting the building stock poses greater challenges than estimating the current building stock due to data limitations. Conceptually, a similar approach was used for backcasting as was used to construct the estimates of the current building stock. Historical data on actual stock levels was used wherever possible. High quality historical data exists for some classifications, such as metropolitan office space. Historical data quality is less for some other classifications because of changes in data capture and scope. For example, some data series on government buildings have become more complete with time, with the improved coverage adding to the reported growth in building stock. Historical data may also be subject to period gaps. Activity metrics also provide a second source for backcasting estimates. For example, there is high quality historical data on the number of aged care places. This metric can be used as a basis for estimating floor space. However, it is subject to the assumption that either the aged care place/floor space ratio is unchanged over time or can be estimated with reasonable accuracy. Similarly, employment is a metric that can be used as a basis for estimating floor space and also for backcasting the stock estimates for some classifications. An example is the use of employment to estimate club floor space. One problem with using this metric as a basis for backcasting is that employment may be affected by cyclical factors, leading to any estimated building stock series that relied on employment also being overly subject to cyclical influences. For this reason we have smoothed employment data when used as a basis for historical stock estimates. ABS building completion data can provide an insight into the irregular pattern of changes in the building stock. The limitations in using this data were discussed earlier. However, the year-to-year variations in the value of completions and geography of completions provide a tool for enhancing the backcast stock series. BIS Shrapnel commission a special time series of completions data from the ABS and incorporated this within the model.

Forecasting the building stock The stock forecasts are based on a combination of existing BIS Shrapnel forecasts and more detailed sub-classification analysis. The forecasts reflect a wide range of parameters including investment and economic growth expectations, population growth expectations, and demographic and regional population and activity trends. Where possible, benchmarks were used to ensure the ‘sense’ of the forecasts for floor space. For example, the forecasts for aged care floor space reflect existing BIS Shrapnel analysis, but are also calibrated against the population aged 80 years plus as a check to ensure the floor space forecasts are consistent with industry norms.

Stock Model Validation Stock model estimates were compared in some detail with other sources, including Geoscience Australia. Geoscience Australia estimates of the stock of commercial buildings appear much higher than ours. In total, the GA model shows 820 million m2 of commercial building space, as compared to 287 million m2 for our model. A full investigation of these differences is beyond the scope of this study.

AC.3 - Energy Data Sources In total, some 20,000 records on individual buildings and portfolios were compiled from a wide range of sources. The sources are noted in Figure 16.4 below, including notes on completeness and/or quality. Figure AC.4 - Energy Data Sources

Appendix C – Model Documentation

Page 12 of 95

Energy Data Source Contents Notes

Commercial Buildings Benchmarking various buildings

Exergy End use breakdowns (EUBs) from previous projects.

High quality. EUBs fit desired data. No time series.

Various publicly available energy usage information

Internet Data from companies' websites and EEOs. Includes a lot of aggregate data.

Some relevant but most sources don't report scale data. May be useful for validating total energy use.

Exergy Archives, Data for various buildings

Exergy Data from various Exergy files

High quality. EUBs fit desired data. Very few buildings with time series data. Where there is time series data, it is not necessarily consecutive years. Data periods range from 2001 to 2011. Scale data included.

ACT schools Pitt & Sherry Energy usage data for ACT public schools.

High quality audit data. Total electricity usage & scale data for mostly two years (07-08).

SA Health 2009-10 SA Energy Markets and Programs Division (via DCCEE)

SA hospitals and health centres

Total energy use by fuel type, and total m2 for individual hospitals and health centres (2009 and 2010)

Retail data Exergy Retail data from Exergy hours study for NABERS

Very Relevant. Energy usage data for individual buildings. Scale data but no time series.

Newcastle Council Electricity Consumption October 2011

Newcastle Council (via Pitt & Sherry)

pdf report with some energy usage

Electricity usage for various council buildings e.g. galleries, library for five years 2007-2011. No scale data

NABERS Hours Study

Exergy Retail data from Exergy hours study for NABERS

High quality. Electricity usage for 2007 and 2008 but only one year for the same building. Scale data included.

IHG Hotels IHG (via Exergy) Data from 26 hotels. High quality. Electricity and gas usage data for individual hotels. Scale data but not time series.

DET school energy consumption for

DCCEE 95% of QLD state schools High quality. Energy usage data for 4 years (2007-

Appendix C – Model Documentation

Page 13 of 95

Energy Data Source Contents Notes

NFEE study - Sent 05-12-11

2010) for individual schools, with m2 for each school.

Anonymous data NABERS Hotels Benchmarking

Exergy Data from hotels benchmarking project

High quality data. Total energy usage for individual hotels. Scale data but not time series

Green Buildings Fund Data

GBF (via DCCEE)

Shopping centres, hospitals and hotels data from GBF

High quality but incomplete. Desensitised data (includes scale data) with total energy consumption. Scale data but not time series. Financial years had to be estimated. No EUBs.

Stockland Properties (several files)

Stocklands (via Exergy)

Retail and offices data Relevant. Total energy usage, but not always for a full year.

WA TAFE OSCAR data WA TAFEs Good quality. Time series energy data for 4 years (2002-06) energy usage data for individual TAFES, with m2 for each TAFE.

Tas Gov Buildings (several files)

TAS DPAC (via Pitt & Sherry)

Separate pdf energy audits Tas Gov buildings

High quality but limited audit data. Total energy usage for individual buildings, with m2 for 2008.

QLD retail, NABERS only no EUB

Exergy Retail tenancy data from Exergy shopping centre ratings

High quality. 20009-2011 for one shopping centre, only one year for the remainder. Scale data included.

DFEEST Energy Baseline 2007 08

DCCEE SA Education, mainly TAFES

Relevant. Time series energy usage data for 10 years, scale data included.

Crown Complex and Hotels

Exergy Casino & hotels Relevant. Energy usage for 2011 only. Scale data.

Westin, Park Hyatt, ShangriLa, Alexander Centre

Exergy Hotels and Correctional Very relevant. Scale data but no time series.

Tas gvt buildings (OSCAR)

Tas DPAC (via DCCEE)

several files of TAS police stations, health services data

Medium quality. Energy usage data and scale data for 4 years (2007, 2009-11). Issues with scale data.

NEW NABERS data DCCEE (Hotel, Office, Schools) - second dataset received. Total Consumption

High quality. Energy usage data for individual buildings, for one year.

Appendix C – Model Documentation

Page 14 of 95

Energy Data Source Contents Notes

information.

Stockland Properties

Stockland (via Exergy)

Offices and some other Relevant. Total energy usage, but not always for a full year. Scale data.

UTAS Buildings 2010

UTAS (via Pitt & Sherry)

Data on UTAS buildings High quality. Energy usage data for individual campus buildings for years 2008 and 2009. Scale data. Must be confidentialised.

Coles Data Wesfarmers (via Exergy)

retail data Medium quality data. Energy usage data for individual supermarkets. Scale data but no time series or EUBs.

SA OSCAR Data (various government agencies)

SA Government via DCCEE

SA buildings , some correctional centres, law courts, tertiary education , offices

Total energy usage data for 10 years (2001-11) and scale data

VIC Hospitals data Vic Dept of Health (via DCCEE)

VIC Hospitals consumption, incl diesel data

Total energy usage data for individual hospitals and centres for 6 years (2005-11) Scale data.

Investa Property Group Monthly Energy Data (excel file)

Investa (via DCCEE)

2003-2011 monthly kWh & MJ data. Has NLA and normalised consumption. Noted that some of these already included via NABERS database.

High quality. Total energy usage data for individual offices, 2003-11. Scale data

Australand Australand (via Exergy)

Electricity & Gas monthly consumption for Australand buildings, mostly offices - time series 2008-2011

High quality. Energy usage data 2008-2011, but only some buildings have more than one year's data. Scale data.

Wesfarmers (55 sites)

Wesfarmers (via Exergy)

Retail sites from Wesfarmers.

Medium quality. Electricity usage data. Scale data but no time series or EUBs.

GPT EEO data GPT (via Exergy)

GPT offices High. These EUBs fit desired data. Scale data but no time series.

Commonwealth OSCAR Data

DCCEE Commonwealth OSCAR Data.

Medium quality. Total energy use for agencies. Energy usage data for several individual Commonwealth public buildings; many records noted as "Australia Wide".

University James Cook University (via DCCEE)

Individual campus buildings of JCU

Relevant. Energy (including end-use) and scale data for two years

Appendix C – Model Documentation

Page 15 of 95

Energy Data Source Contents Notes

only

Detention centres DCCEE 7 detention centres Energy and some scale data.

Clubs DCCEE CSCC in ACT Good energy and scale data for years 2007-10

TAS gvt agencies OSCAR (via DCCEE)

Govt offices, health buildings and schools

Mostly relevant although QA process has revealed problems with some energy and scale data.

QLD gvt DCCEE Health buildings, gvt offices and health buildings

One year only and some missing scale data.

Various Energetics (via DCCEE)

Airports, tertiary, law courts and hospitals

High quality. Good energy and scale data for years 2007-10. Some records missing scale data.

Schools DCCEE ACT public schools Relevant. Some scale and energy data for 2009-11. Supplement/check with pitt&sherry data.

Courts DCCEE ACT magistrates court Relevant. Scale and energy data Years 2008-12

Various NT gvt DCCEE Govt offices, health care buildings, public buildings and schools

Relevant. Energy and scale data with various time series 2004-10.

Each record contains up to forty data fields including:

Building identification details, including an individual Building ID code, but also name and street address where available

Other locational details (State, region, postcode where available)

Data contacts

Scale data (varying depending on the building type

Other activity data (varying depending on the building type)

Data on cogeneration, green power or onsite renewables where available (very few records)

Energy consumption by fuel (generally electricity, gas, LPG, diesel/oil)

Total energy consumption (where no fuel mix available)

A large number of electrical end-use categories (which vary by building type) where available

A smaller number of gas end-use categories (varying by building type) where available. As a result of the quality assurance (QA) process described below, the final number of valid records utilised by the model was reduced to 5,654 individual buildings and 15,798 total records, as shown in Figure 16.5 below, significantly higher than the Department’s minimum target of 1,000 buildings. Including ‘agency level’ records – which do not resolve individual buildings and hence are not used directly in the model but which may nevertheless contain useful information – and also records relating to

Appendix C – Model Documentation

Page 16 of 95

building types not modelled, the data sets comprise data on 6,599 individual buildings4 and 19,942 total records, as shown in Figure 16.6 below. Note that there are more records than buildings, as some records relate to the same building but different financial years, while other records relate to different tenancies within the same building.

AC.3.1 - Energy Data Quality Assurance An extensive data quality assurance program was carried out over several months. In a first stage, the QA process was to ensure that data from the source data has been correctly transferred into the database. Several methods were used, including checking that the sum of each end-use-breakdown was within 10% of the building’s annual energy consumption, and calculating fuel intensities to identify outlier results. For other databases without end-use breakdowns, the team went back to the source data and checked that the data in the database agreed with the source data. Non-commercial buildings such as factories, speed cameras or transport vehicles were removed from the database. For example, this step identified several automated teller machines (ATMs) that were incorrectly listed as ‘banks’ with an area of 1m2. These were removed from the database. Figure AC.5 - Individual Building and Total Record Counts Utilised

4 For ‘agency level’ records, some “buildings” in fact refer to a portfolio of buildings with a single owner or tenant (often a government agency), but where individual buildings are not resolved. This is why such records were not able to be incorporated directly within the model. Some of these records may be used for model validation purposes, however.

Building

type:

Unique

building

count:

Total record

count:

Offices 1,715 4,308

Hotels 195 208

Retail 791 878

Retail -

tenancies 261 1,102

Hospitals 352 972

Schools 1,641 6,475

Tertiary 388 1,277

Public

buildings 28 235

Law courts 283 343

Totals 5,654 15,798

Summary statistics on the building

energy data used in the model:

Appendix C – Model Documentation

Page 17 of 95

Figure AC.6 - Total Records – All Data Sets

The ownership types of buildings (government owned/private) were verified where in doubt either by going back to the data source or by searching on the internet. This included buildings from the offices, schools, tertiary education, museums and galleries, agencies and ‘other’ building types. Identifiers were allocated to each individual building to enable anonymous building identification and also to ensure that there were duplicate records for buildings. The building area and energy consumption were cross-checked for to help identify duplicates. In some cases, reconciliation of inconsistent records for the same building and year but from different sources was required. Part of the metric used for this process was to check for addresses that were not fully identical (e.g. use of building name instead of the street address) but is actually referring to the same building. The second stage of the QA process involved the establishment of a shared team action list between the project partners, pitt&sherry, Exergy and BIS Shrapnel. This facilitated version control as well as collaboration between teams. Any data that seemed unrealistically isolated from its counterparts (e.g. similarly sized buildings/building types) were double-checked by going back to the source data. During this process, energy data was checked to be in the correct units (e.g. GJ instead of MJ). Further attempts were made to fill in any scale data that was missing by either going back to the source data provider, or conducting web searches for publicly available data. This helped to ensure that any energy data provided was able to be maximised and captured within the model.

Building type:

Unique

building

count:

Total record

count:

Offices 1,715 4,308

Hotels 195 208

Retail 791 878

Retail - tenancies 261 1,102

Hospitals 352 972

Schools 1,641 6,475

Tertiary 388 1,277

Public buildings 28 235

Law courts 283 343

Prisons 8 31

Aged care 30 30

Entertainment 17 51

Other 721 2,126

Agency level' offices 84 1,190

Agency level' law courts 5 43

Agency level' schools 11 33

Agency level' labs 11 110

Agency level' other 58 530

Totals 6,599 19,942

Summary statistics on the building energy data

contained in the data sets:

Appendix C – Model Documentation

Page 18 of 95

The method of classification for regional or capital city was originally based on an external university research source – this was later modified to be aligned with the ABS approach to regional city/capital city classification. A protocol was agreed between the Department and the project team that enabled missing financial year information for Green Building Fund (GBF) energy data to be estimated. This data was then able to be incorporated into the data analysis, aiding in time-series modelling. LPG or diesel records that were originally in litres were converted to GJ to allow all non-renewables to be analysed under the same metric. Many Queensland hospitals were discovered to have very low energy intensities due to source data quality issues (e.g. ‘offices’ with bed numbers, and large variations in the reported energy intensity of similarly sized hospitals). After discussions between Exergy, Pitt & Sherry and DCCEE, it was concluded that was probably due to the difference in energy intensities between medical centres, day centre hospitals and large scale hospitals. Further OSCAR data quality issues were discovered as pitt&sherry advanced in their modelling exercise. It seems that much of the OSCAR data provided has scale data assigned to entities that are ambiguous and difficult to assign to the corresponding entity names assigned to energy data. Clearly, changes in OSCAR book-keeping methods also caused fluctuations in the reporting of energy consumption or business measures. In most cases, Exergy has used their best judgment to assign the correct energy data to scale data, either by doing a web search or by sanity checking that the energy consumption is acceptable given the scale data. However, some data which is obviously of poor quality has been removed from the dataset. A number of Commonwealth records were able to be incorporated into the dataset following DCCEE’s confirmation that the entities were site level buildings and site level energy consumption (as opposed to agency level). We note that certain records with missing fields, and other records not immediately relevant to the initial model build, have been left in the data sets, against the prospect that they may be able to be completed and/or utilised in the future. However, such records are not ‘interrogated’ by the ‘Analysis’ pages with the model and therefore do not impact upon the model aggregates. A key example of this is that the ‘Hospitals – Model’ page excludes data relating to hospitals with less than 10,000 sqm, in order to maintain parallelism with the stock estimate, which is based upon major teaching hospitals. Further analysis of this issue is contained in Chapter 10 – Hospitals. An extensive data quality assurance (QA) program was carried out over several months. This included:

Examination of records showing unusually high or low energy intensities;

Correction of transcription errors;

Correction of units errors (for energy, e.g. GJ vs MJ) and standardisation of units;

Searching for missing fields, particularly scale metrics such as m2 but also address details, building sub-type and other fields;

Estimating missing scale metrics where feasible, for example, some hotel floor areas were estimated from hotel room numbers by State and (quality) star rating; other estimates (e.g., hospital area from other scale metrics such as separations) were excluded as unreliable;

All records were allocated consistently to a financial year, being the financial year at the data period (e.g., records finishing in either April or November 2007 - but covering 12 months of energy consumption – were allocated to FY2007);

Examining building sub-types fields that appeared inconsistent with other fields (offices listed with beds, for example) leading to reallocation of records to different data bases, or to ‘Other’ where not a type modelled.

Following an initial QA, regression analysis on the data (discussed further below) identified additional ‘outlier’ or improbable results, each of which was investigated, generally by confirming source data or direct contact with data owners, and in some cases individual building owners or managers.

Appendix C – Model Documentation

Page 19 of 95

AC.4 - Modelling Methodology This section provides further technical details on the functioning of the NRBuild model, along with key assumptions.

AC. 4.1 - ‘Energy Data’ Pages The ‘Energy Data’ pages have the primary function of storing the QA’d building energy data records by building type. However, several data manipulations may also be carried, depending on the building type.

Hours of Operation First, where data sets contained a significant number of records with values for ‘hours of operation’ (expressed as hours per week), then the mean value of hours/week was calculated. Note that if there are significant sub-types where hours of operation are likely to differ (e.g., office tenancies vs base buildings; or shopping centre tenancies vs supermarkets) then the averages are calculated separately by sub-type. Note that area-weighted averages for hours of operation were also calculated, but these did not differ significantly from simple averages. The mean value for each type or sub-type is reflected in the ‘Model’ pages, as discussed below. Second, fuel consumption and end-use fields were then normalised to the mean value for hours of operation, by factoring them by the ratio of the mean value to the declared value. Where no value for hours of operation is declared, the model assumes that the mean value applies and the record is not normalised. Note that this approach assumes that the energy consumption is linearly correlated with time. We consider this a reasonable assumption within bounds – at very high or very low hours of operation, this linear relationship is likely to break down, but this question fell outside the scope of this study.

Fuel and Energy Intensities Fuel and total intensities for each record were then calculated, to the right of the original data set, using data normalised for hours of operation – where relevant – as described above. Note that numerous records provide a value for ‘total energy’ in GJ, but no break-down by fuel. By contrast, where individual fuel data is available, the ‘total energy’ field is blank. Therefore, total fuel intensity calculations are based on a simple logical test: where fuel breakdowns are available, they are calculated individually (including standardising different units by to a common intensity metric, MJ/m2.a) and summed; where only ‘total energy’ is available, energy intensity is expressed as an average MJ/m2.a instead. For the larger data sets (such as offices), total energy intensities were plotted against area, to examine the distribution of intensities within the sample (see Figure 16.5). Typically, the larger samples exhibited a reasonable symmetrical distribution of values around the mean, but with ‘tail’ of higher intensities, smaller footprint offices. For these sets, simple and area-weighted average energy intensities are compared, and standard deviations calculated (by sub-type where appropriate). We note that generally simple mean values are typically modestly higher than area-weighted mean values, generally by less than 5%. This result follows directly from the skewed distribution (for offices) shown below, as area-weighted reduces the impact of the high intensity but small area values in the sample. Further statistical analysis of all the data sets is provided in Appendix E.

Appendix C – Model Documentation

Page 20 of 95

Figure AC.5 - All Offices – Total Energy Intensity vs Area

Source: pitt&sherry

End Use Intensities Finally, where data sets include a significant number of records with populated fields for end use, end use intensities (in MJ/m2.a) are calculated for each record (again using normalised energy data where appropriate). Data on the end use of fuels is restricted either to electricity and gas or, in some cases, electricity only. No data on the end-use of other fuels is available in any of the data sets. Summary values for end use intensities, including simple averages, standard deviations and sample size (‘n’ values), are then calculated at the top of the page. Note that the end uses vary by building type although, in the ‘End Use’ pages where these analyses are summarised by building type, an attempt is made to use common ‘aggregate’ end use categories such as ‘Total HVAC’, ‘Total Lighting’, ‘Total Equipment’, etc. The individual end-use values may be inspected in the ‘Energy Data’ pages (subject to user privileges), but it should be noted that sample sizes for many end use fields are not statistically significant.

AC.4.2 - ‘Analysis’ Pages As noted in the overview, these pages interrogate the relevant ‘Energy Data’ pages and extract and summarise key values, organising them into time series (from 1999 to 2012) by sub-type (where appropriate), ownership type (where appropriate), state and region.

Average Fuel and Energy Intensity Analysis For each of the summary analyses (that is, for each building sub-type modelled), the ‘Analysis’ pages extract:

Sample size (for that sub-type, year, ownership type, state and region);

Summary of floor area (noting that this is limited to the area associated with those records that include fuel breakdowns, so that average fuel intensities may be calculated accurately);

Average fuel intensities for electricity and gas (in all cases), and for LPG and diesel (where appropriate);

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

All Offices - Total Energy Intensity vs Area n=4308

Appendix C – Model Documentation

Page 21 of 95

Summary of floor area and average ‘total energy’ intensities (note these values are generated from records which are different from those in the two points above for, as noted earlier, records with ‘total energy’ and records with fuel breakdowns are mutually-exclusive);

Finally, an area-weighted average of the ‘total energy’ intensities and the sum of the fuel intensities is calculated (described as a ‘weighted average energy intensity’). This is the ‘highest level’ average energy intensity for that analysis (e.g., each sub-type, year, ownership type, state and region).

Note that where the ‘Energy Data’ pages have been normalised for hours of operation, the ‘Analysis’ pages selected the normalised rather than the unnormalised data. Note also that where no data (in the ‘Energy Data’ page) fits the logical tests above, a zero value is reported. Underneath the summary values by ownership type, state and region, sub-totals and totals are calculated (e.g., ‘all government owned offices’, ‘all capital city’ or ‘all regional’), for each financial year. As these values sum all the states and regions, they may be understood as ‘national averages’. Where there is a significant number of records for both private and government ownership, such as for offices, the above analysis is conducted first for government-owned buildings, and then for privately-owned buildings, and finally these are summed into national averages by financial year and region. To the right of the summary analyses by financial year there is an ‘average all years’ analysis, which simply sums the sample, area and intensities values across all financial years. Caution must be exercised in interpreting this analysis, as it deliberately confounds data across financial years (for example, area values may double count the same building in multiple financial years). Its primary purpose was to act as a ‘sense check’, as the fuel and energy intensity values it shows are associated with the highest possible sample size, or ‘n’ value, for that building sub-type. Note that for some sub-types where no or very little time series data was available (e.g., supermarkets and some shopping centre sub-types), then these average values are used in the model. Beneath each average fuel and energy analysis by building type, a tool has been provided that enables the user to quickly visualise the available sample sizes by financial year, ownership type, state and region (and indeed aggregates such as national averages). The user may test different minimum ‘n’ values by inserting their desired value in the light blue cell adjacent to the query, ‘Min. ‘n’ value (per FY)?’. The model then suppresses any record that falls below that ‘n’ value. Thus, if an ‘n’ value is selected that is higher than any actual ‘n’ value available to the ‘Analysis’ page for that building sub-type, all summary values will be suppressed and the analysis will appear blank. If ‘0’ is selected, this indicates that any ‘n’ value including zero is ‘good enough’, and therefore all records will be reported. As noted, this is simply a visual tool that enables the user to quickly visualise trends in significant data (i.e., for how many states and regions, for how many time periods, do we have significant data?). Note that this tool does not affect the regression analysis (discussed below).

Regression Analysis To the right of the ‘average all years’ analysis will be found regression analyses, generally on national average energy intensity values by building sub-type but, as discussed below, also on fuel mix in some cases. The absence of a regression analysis indicates the lack of a significant time series trend, and there will be a ‘highlighted’ user note to that effect. A table (for each analysis) sets out the total sample size used in that analysis (all time periods), the average energy intensity values drawn from the time series analysis, ‘n’ values for each year, and then linear regression values for each time period, including (as appropriate) estimates back to 1999 and forward to 2020. The regressions are also plotted on a chart immediately above each table, including trendlines, ‘R2’ values (between 0 and 1, with lower values indicating a poorer ‘fit’ of actual values to the calculated formulae, while a value of 1 would indicate all values align exactly with that formula) and total sample size. Linear regressions have been chosen, as they explain the values as well as other, more complex forms, but an advanced user may experiment with different regression approaches. Note that the calculated

Appendix C – Model Documentation

Page 22 of 95

‘best fit’ values are imported directly into the ‘Model’ pages, and therefore any changes to these regressions will flow through the entire model.

Fuel Mix Analysis Beneath the time series analyses discussed above, fuel shares by financial year are calculated. Where a significant time series is available for fuel mix, and a trend is apparent, a regression analysis (for electricity intensity) may be found to the right of the fuel mix time series. These values are then reflected in the fuel shares used in the ‘Model’ page for each building type. In many cases, however, no significant time series is available or trend apparent, in which case average fuel mix values (from the ‘average all years’ analysis) are used in the model.

AC.4.3 - ‘End Use’ Pages For those building types and sub-types where significant end use data is available (in the corresponding ‘Energy Data’ page), it was noted above that end use intensities are then calculated. For transparency, the summary analysis is then carried across to a separate ‘End Use’ tab for that building type. In that tab, the summary statistical analysis for each building sub-type and end-use is repeated, and then major end use shares (in percentages) are summarised in a table and also plotted in a pie chart. Separate tables and charts are prepared for electricity and gas end shares where appropriate.

‘Model’ Pages Each building type or sub-type that is able to be modelled has its own ‘Model’ page. At the top of this page there is a ‘dashboard’ of ‘National Fuel/Activity Settings’. This dashboard has two key functions. First, it makes transparent the ‘default’ values that are utilised to estimate total energy use (and, from this, greenhouse gas emissions). Default values are provided by financial year for:

Total energy intensity (national average, MJ/m2.a)

Electricity share of total energy (%)

Green Power/onsite renewables share of electricity use (%, generally defaults to zero)

Natural gas share of total energy (%)

LPG share of total energy (%, which may be zero)

Diesel share of total energy (%, which may be zero). These default values are calculated in the ‘Energy Analysis’ pages as described above. In addition, default values are provided for:

Annual stock growth (% change in m2 over previous year, Australia – these are calculated directly from the ‘Stock Summary’ pages)

Hours of operation (calculated from ‘Energy Data’ pages, as described above)

Weeks of annual shutdown (default values are estimates – the user may vary these as described below)

The two values above are then expressed as total operating hours per year (hours/week minus weeks of shutdown)

Total operating hours/year is then expressed as an ‘Energy Intensity Factor’ (%, where 100% = total operating hours/year).

Appendix C – Model Documentation

Page 23 of 95

These default values are ‘greyed out’, indicating that users may not change them (once the workbook is ‘protected’). As noted, their primary purpose is to make transparent to the user which values are being relied upon to generate estimates of total energy and fuel use and greenhouse gas emissions. The second purpose of the ‘dashboard’, however, is to enable the user to insert user-defined variables, if they wish, for these key parameters, in order to examine the impacts of their preferred variables on the total energy, fuel and greenhouse estimates. Since the model is calibrated to FY2009, the user-definable cells only relate to FY2010 onwards; earlier cells are blacked out. As always, these cells are denoted by a light blue colour. When these cells are empty, the model selects the ‘default’, greyed out values. When the light blue cells are populated by the user, the model selects those cells instead. Note that it is not possible to directly select the electricity share of total energy use – the column is greyed out. This is because the model allows the user to set the shares of other fuels (gas, LPG, diesel), with any value from 0% to 100%, and electricity is then assumed to be the residual. If all other fuels are set at 0%, for example, notice that the ‘greyed out’ value for electricity changes to 100%. Equally, users may set their own preferred values for annual stock growth rates, and for hours of operation/week and/or weeks of annual shutdown per year. Again the model defaults to the greyed out values, but selects user-defined values when they present. Note that as different values are inserted for ‘hours of operation’ or ‘weeks of shutdown’, the greyed out ‘Hours of Operation’ and ‘Operating Hours Energy Intensity Factor’ (the final two columns) also change, indicating the affect of the user-defined values on the total energy, fuel and greenhouse gas estimates that appear below the dashboard. Note that for those building types where no adequate time series for energy intensity was available, the years prior to FY2009 are entirely blacked out and no values are reported. Additional data capture would required to be able populate these cells. Also note that the ‘hours of operation’ feature is essentially redundant for those building types/energy data sets where no significant information is available on hours of use. Note that in these cases, the model applies an ‘hours of operation intensity factor’ of 100%, meaning that the results are unchanged. Below the ‘dashboard’ can be found, first, a summary table for total, Australia-wide energy use, total fuel use and total greenhouse gas emissions, by year, from 1999 – 2020, for each building sub-type modelled. The units are Petajoules (PJ) for energy, and million tonnes of CO2 equivalent (Mt CO2-e) for greenhouse gas emissions. These national estimates are calculated from the values from dashboard, either default or user-defined where present, applied to the stock time series by building type (from the ‘Stock – Summary’ pages). ‘Total Energy Use’ by year is calculated as stock times (national average) energy intensity for that year. Total fuel use is calculated from the fuel use shares revealed (or specified) on the dashboard. Finally, total greenhouse gas emissions are calculated by applying the greenhouse gas intensities revealed in the ‘Global Settings’ page to fuel use by year. Note these values are applied at the state level, given significant differences in the greenhouse gas intensity of electricity supply by state5, and then summed to a national value. Second, underneath the national summary table for each building sub-type is a series of three tables for each state and territory showing the same values as above (total energy, by fuel, and total greenhouse gas emissions), first as totals for that state, then for the capital city, and finally for the regional area, or balance of the state. The energy values are calculated by applying the stock shares by state and region to the national energy values calculated as above, while the greenhouse calculations are described above.

5 The default values derive the National Greenhouse Accounts Factors Workbook, however increasing interstate electricity flows in the NEM make these values questionable.

Appendix C – Model Documentation

Page 24 of 95

AC.4.4 - ‘Summary Figures’ Pages The ‘Summary Figures’ pages bring together the summary data on total energy and fuel use and greenhouse gas emissions from the ‘Model’ pages for ‘like’ building classes. That is, all ‘Office’ building types, all ‘Education’ building types, all ‘Retail’ building types and all ‘Public Building’ types are brought together into summary tables (at the national level). Apart from convenience, these tables are important in demonstrating how sub-types aggregate up to totals. For example in offices, values are calculated separately for base buildings, tenancies, and the combination of base + tenancies, but clearly the three cannot be summed for ‘total office’ energy use.6 Similarly for retail buildings, ‘Shopping Centres (Base building)’, ‘Shopping Centres (Tenancies)’ and ‘Shopping Centres (Total)’ are each reported but may not be summed. An additional complication – but also information set - is the above values include the energy use of supermarkets that are located in shopping centres, but a separate column estimates total energy consumption/emissions for all supermarkets (including those in shopping centres), and a final column estimates the energy consumption/ emissions of ‘standalone’ supermarkets, being those outside shopping centres. This approach was taken (as a value add, since supermarkets fall outside the terms of reference for the study) firstly because it was supported by the data7, and secondly because the ‘Analysis’ page revealed significantly higher average energy intensities for supermarkets than for other retail buildings/tenancies. Note that since there is significant uncertainty about the share of shopping centres which is occupied by supermarkets, the model allows the user to change the default weighting for supermarket intensity within overall shopping centre energy intensity, both for whole buildings and tenancies. The default value is set to 22% (a BIS Shrapnel estimate) in both the ‘Retail – Analysis’ and ‘Retail Tenancies – Analysis’ pages, but the user may vary this. Note that any value above about 40% (supermarket share of shopping centres) would imply that ALL supermarkets were located in shopping centres. A note to this effect is included in relevant ‘Analysis’ pages. These issues will be reported on further in the Final Report of this study. Finally, the ‘Summary Figures’ pages by major building class are brought together into ‘grand totals’ in the ‘Summary Tables + Charts’ page immediately after the introductory ‘Overview’ page. This page provides the highest level national estimate of total energy and fuel use, and greenhouse gas emissions, from the non-residential building stock modelled.

6 As will be discussed further in the Final Report of this study, we have elected not to rely upon the Offices (Whole Building) analysis in summary tables and the ‘Offices – Model’ page, although it is retained within the model and may be inspected by the user. This is because the energy intensity trend shown in that analysis conflicts with those shown in the tenancy and base buildings analyses, and the latter are significantly larger and more robust analyses from a statistical perspective. 7 Unfortunately, very little time series data was available for most retail types, with the exception of shopping centre (base buildings).

Appendix D – Top-down Model Validation

Page 25 of 95

Appendix D – Top-down Model Validation

This Appendix first examines top-down data, such as national energy consumption statistics, to estimate energy consumption in commercial buildings, for the purpose of comparing these results with the bottom up estimates from the NRBuild model. Second, it compares model outputs with other research and data sources.

AD.1 - Top-down Data Validation Estimating energy consumption in commercial buildings from top-down data is inherent difficult. The fundamental challenge is that the Australian Bureau of Statistics and NGER collect data on energy consumption by ANZSIC code and not by end use. There are no direct statistical collections on non-residential building energy consumption patterns in Australia, nor indeed on the floor area of such buildings or other key parameters. Therefore, even where there is an apparent close fit between a particular ANZSIC code and the share of total energy use in that sector that is likely to be attributable to buildings (for example, Division K - Finance and Insurance Services, or Division P – Education and Training), AES data is likely to overestimate the actual use of energy in buildings in these sectors. This is because the data may include transport- or process-related energy use, such as data centres within these sectors. A second consideration is that the model we have constructed does not cover the energy consumption of all non-residential buildings in Australia. Energy consumption ‘missing’ from the model includes that relating to:

Offices less than 1,000 m2 NLA

Motels

Resorts

Retail shopping strips, including restaurants, cafes, fast food outlets and bakeries (outside shopping centres)

Medical centres, clinics

Aged care buildings

Kindergartens

Correctional centres

Warehouses

Coolrooms and industrial freezers

Industrial buildings. As noted in Chapter 9 – Retail, restaurants, cafes and fast food outlets are highly energy intensive, Chapter 7 notes that ‘non standalone’ office energy consumption is not covered in the NRBuild model. It is beyond the scope of this study to estimate the ‘missing’ energy consumption. The key conclusion we can draw is that we should expect the model generally to show less energy consumption that is implied by the top-down analysis. Figure AD.1. summarises the degree of agreement between the bottom-up and top-down estimates of total energy consumption in key building types in the base year, FY2009.

Appendix D – Top-down Model Validation

Page 26 of 95

Overall, there appears to be reasonable agreement with known top-down data in total energy estimates for most building types in the base year for most building types. The main exceptions are hotels and offices. For hotels, top-down estimates are very likely to substantially over-estimate actual energy consumption. ANZSIC Division H – Accommodation and Food Services - also includes energy consumption in restaurants, cafes, pubs, bars, clubs, etc. While these building/enterprise types fell outside the terms of reference for this study, the data sets compiled nevertheless include some information on their energy intensity. A number of fast food outlets show total energy intensities of up to 48,000 MJ/m2 – some 32 times higher than the average retail tenancy in the data set. Indeed, all of the most energy intensive records in this data set represent fast food outlets. BIS Shrapnel estimate that in 2010, there were some 8 million m2 of café and restaurant space in Australia, and around 9 million m2 of pubs, clubs and taverns, or some 17 million m2 in total, compared with some 9.4 million m2 of hotels (defined as those with 5 rooms or above). With hotels accounting for some 36% of the floor area in Division H (excluding motels and smaller hotels), then, it is not surprising that the model finds around 31% of the estimated total energy consumption in this Division. Figure AD.1.1 - Bottom-Up vs Top-Down Energy Consumption Estimates, Non-Residential Buildings, Australia, FY2009

Source: pitt&sherry. Notes: Top-down estimates are derived primarily from Australian Energy Statistics, Energy Update 2011 (BREE) and Energy, Water & Environment Management, ABS Cat. 4660.0. As explained in the text, these are likely overstate energy consumption in buildings. The ‘offices’ estimate excludes ‘non-stand-alone’ offices of less than 1000sqm NLA. The allowance for their energy consumption applies the average energy intensity of the ‘stand-alone’ offices to the ‘non-stand-alone’ stock – this is an estimate not a research finding. The

Building type:

Bottom-Up

Energy

Consumption

Estimate (PJ)

Top-Down Energy

Consumption

Estimate (PJ)

Bottom-Up

Share of Top-

Down (%)

Offices 33.6 74 45%

Offices (including

allowance for 'non-

stand-alone' offices) 59.6 74 81%

Hotels 13.2 43 31%

Schools + TAFEs 9.4 8 118%

Universities 6.5 10 65%

Hospitals 19.1 22 87%

Retail 47.2 50 94%

Public Buildings 2.1 - -

Totals (excl. public

buildings) 188.7 281.0 67%

Bottom-Up vs Top-Down Energy Consumption Estimates, Non-Residential

Buildings, Australia, FY2009

Appendix D – Top-down Model Validation

Page 27 of 95

top-down estimate for hotels is particularly uncertain, as Division H also includes restaurants, cafes, pubs, bars, clubs, etc. No top-down estimate of public buildings is available, as the buildings are poorly identified in ANZSIC. For offices, the model estimate of 33.6 PJ in 2009 is less than half that implied by AES. However it is important to note that the top-down estimate is a residual value, including the energy use of ‘offices and related buildings’, and is therefore more likely than for other building classes to be an over-estimate of actual office energy consumption. Further, the model estimate does not include ‘non-stand-alone’ offices (defined as those below 1000 sqm NLA), which in total are estimated to account for a substantial 26.9 million sqm NLA in 2009. If these buildings had a similar energy intensity to the rest of the office stock (an assumption, not a research finding), then they would have accounted for around a further 26 PJ of energy in the base year, bringing total office energy consumption to some 62 PJ. Noting the large stock of non-stand-alone offices, small differences in the energy intensity of this stock, compared to this assumption, would result in large differences in estimated total energy consumption. We note that it may possible in future to develop a model of such ‘non-stand-alone’ offices. Another possible conclusion from Figure 17.1 is that the estimate of retail energy consumption may be high, given that ‘retail strips’ are not included in this estimate. This question, however, falls outside both our terms of reference and the capabilities of the existing data sets in the model to answer. It should be recalled that there are strict limitations on the ability to validate the model for historical time periods at present, as the key Australia Energy Statistics (AES) series is currently only available to 2009-10 without a series break. This situation has arisen due to the conversion of the AES to National Greenhouse and Energy Reporting System (NGERS) rather than Fuel and Electricity Survey (FES) data, with the two having differences in ANZSIC code definitions and other differences. This discontinuity is expected to be rectified (by the Bureau of Resource & Energy Economics) during 2012, and we recommend that further model validation occurs once the revised data series becomes available. The derivation of the top-down energy consumption estimates is set out below.

AD.2 - ANZSIC Coverage Estimates of national energy consumption are structured in terms of sectors of economic activity, defined by ANZSIC, rather than by the functional building type. It is assumed in this study that commercial buildings comprise buildings occupied by entities which are classified into the commercial and services sectors of the economy. Under ANZSIC, these comprise the following Divisions and Subdivisions.

Division F Wholesale trade

Division G Retail trade

Division H Accommodation and food services

Division I Transport, postal and warehousing (Subdivision 52, Transport support services, and Subdivision 53, Warehousing and storage services, only)

Division J Information media and telecommunications

Division K Finance and insurance services

Division L Rental, hiring and real estate services

Division M Professional, scientific and technical services

Division N Administrative and support services

Division O Public order, safety and regulatory services

Appendix D – Top-down Model Validation

Page 28 of 95

Division P Education and training

Division Q Health care and social assistance

Division R Arts and recreation services

Division S Other services. It will be apparent that there is a reasonable correspondence between some building types and some of the ANZSIC Divisions, and vice versa. For example, standalone retail buildings fall exclusively into Division G, hotels into Division H, schools into Division P, and hospitals into Division Q. On the other hand, office buildings are occupied by entities in a number of different ANZSIC Divisions. There are two national data sources which provide estimates of total energy consumed in these sectors of the economy. Australian Energy Statistics, Energy Update, Table F (AES) provides estimates of total energy consumption, disaggregated by fuel type, for all these economic sectors combined, excluding Subdivisions 52 and 53. ABS 4660.0 Energy, Water and Environment Management, 2008-09 (EWES) provides estimates of consumption of electricity and natural gas for most of these Subdivisions, with the exception of parts of Divisions K, N, O, P and Q. It also provides greater details, covering all fuels, for Subdivisions 52 and 53. These two sources are used together to make an estimate of total consumption of energy in commercial buildings, as defined for this project. It is important to note that AES provides separate estimates for each State and the NT, but EWES does not. This further limits the reliability of State level aggregate energy consumption estimates. In the remainder of this Appendix, these two data sources, supplemented by data from other sources, in particular NGERS, which are referenced as they are drawn on, are used to 1) make an estimate of total consumption of electricity and natural gas in the commercial and services sectors of the economy as a whole, nationally and by State, and 2) estimate electricity and natural gas consumption at the ANZSIC Division level, for some but not all of the Divisions listed above, at the national level only. NGERS public reports do not include energy consumption by fuel, but only total energy consumption, together with Scope 1 and Scope 2 emissions. In much of the analysis which follows, for the purpose of using NGERS data to compare with AES and EWES for commercial and services sector entities, the following heavily simplified approach is used.

• Natural gas and electricity are the only fuels used

• Scope 1 emissions are from combustion of natural gas, with an emission factor of 51.3 kg CO2-e/GJ1

• Quantity of natural gas consumed is calculated by applying these two assumptions

• Quantity of natural gas is subtracted from total energy consumption to obtain the quantity of electricity consumed

• Reported Scope 2 emissions by the estimated consumption of electricity, and the resultant implied emission factor is compared with the range of State/grid emission factors specified in the NGERS Technical Guidelines2

• If the implied emission factor is outside a range which appears reasonable, having regard to the location of the reporting entity’s principal operations, a further adjustment is made by assuming either that some Scope 1 emissions relate to the combustion of diesel (ADO), or to emissions of synthetic refrigerant gases, or both.

Appendix D – Top-down Model Validation

Page 29 of 95

AD.3 - Total Sectoral Energy Consumption The “face value” estimates of total sectoral electricity and natural gas consumption from the two sources are shown in Table 17.2. The figures are for 2008-09, because EWES data are for that year. It is also the base year named by the Department for the Baseline Model. The most recent (2011) AER release, containing data up to 2009-10, used NGERS data for the first time, with a significant increase in accuracy of the estimates. The 2011 release also included a revision of the estimates for 2008-09 (but not for any earlier years). This means that the current AER estimates for 2008-09 are the “best available” and are an appropriate basis for comparison with EWES figures. Table AD.2 - “Face value” estimates of electricity and natural gas consumption, 2008-09 (PJ)

Electricity Natural gas Coal and

briquettes Wood LPG ADO

Other petroleum

Town gas

207.8 45.5 1.5 0.3 3.2 22.7 0.4 0.2

178.7 30.6

1 Calculated from DCCEE, 2011, Table 2.3.2A 2 DCCEE, 2011, Table 7.2

These figures cannot be directly compared because the EWES figures, which are the sum of a series of Division level figures, exclude a number of Divisions and parts of Divisions. Adjustments for these exclusions are needed to place the two data sets on an equivalent basis. Each relevant Division is considered in turn, based on the list at the head of this note. AD.3.1 Sectors under-represented in EWES In a number of ANZSIC Divisions, substantial numbers of entities were excluded from the survey. Consequently, energy consumption in these Divisions is under-reported in EWES. Each affected Division is discussed in turn. Division I Transport, postal and warehousing (Subdivision 52, Transport support services, and Subdivision 53, Warehousing and storage services, only) AES reports energy consumption in Division I separately from the commercial and services sector, so energy consumption in Subdivisions 52 and 53 is not included in the AES figures in Table 1. EWES estimates of energy consumption in these two Subdivisions have also been excluded from the totals in Table 1. The two data sets are therefore directly comparable with respect to Division I as a whole – it is totally excluded. However, as previously observed, there could be grounds for classing Subdivisions 52 and 53, or at least parts of them, as part of the commercial sector. Subdivisions 52 includes the operation of airports and railways stations, much of which would be classed as commercial buildings, but also includes stevedoring and shipping terminal operations, which would not. Division 53 includes bulk grain storage and handling facilities and bulk petroleum storage facilities, neither of which would be classified as commercial buildings, but also includes refrigerated and controlled atmosphere storage facilities, wool stores, bond stores and furniture warehouses, all of which probably would be classified as commercial buildings. If the modelled stock includes any of these types of buildings, the best approach to reconciliation would be to remove them from the total energy consumption and undertake a separate reconciliation at the Subdivision level, using EWES and other data sources. This is taken up in Part (2) below.

Appendix D – Top-down Model Validation

Page 30 of 95

Division K Financial and Insurance Services (Subdivision 62, Finance, and Subdivision 63, Insurance and Superannuation funds) These two Subdivisions are excluded from EWES, meaning that it includes only Subdivision 64, Auxiliary finance and Insurance Services. NGERS includes data for the following major entities in Subdivisions 62 and 63:

AMP Limited

Australia and New Zealand Banking Group Ltd

Commonwealth Bank of Australia Macquarie Group Limited National Australia Bank Limited Westpac Banking Corporation.

Total reported energy consumption for these entities in 2009-10 was 5.73 PJ, analysing to approximately to:

Electricity 4.06 PJ

Natural gas 1.22 PJ. Macquarie Group did not report in 2008-09, but the other five showed little change between the two years, so the above figures are a reasonable estimate for energy consumption in 2008- 09. This is a minimum estimate of the quantity of energy not covered by EWES. Division O Public Administration and Safety EWES reports consumption of energy in this Division as follows:

Electricity 0.2 PJ

Natural gas NA However, the great majority of entities in this Division are parts of national, State or local government and are excluded from EWES. ANZSIC Division O includes all general government administrative activities, defence, police, the legal system and jails. However, the two largest sectors of energy use in most, if not all State government operations, health and education, fall into ANZSIC Divisions Q and P respectively. The Australian government and some State governments prepare detailed energy use reports and some local councils do also. Collecting data from all these sources would obviously be a significant task. Total Australian government stationary energy consumption in 2008-08, as reported in the whole of government energy report for 2008-09 was as follows:

Electricity 6.25 PJ

Natural gas 1.28 PJ

LPG and ADO 0.13 PJ

Total 7.66 PJ. Since the Australian government does not operate schools or hospitals, effectively all of its buildings related energy consumption can be allocated to Division O. By use of some old government energy use reports for NSW and SA, it has been estimated that total State government energy use in general administration, i.e. excluding education and health, may be roughly:

Appendix D – Top-down Model Validation

Page 31 of 95

Electricity 9 PJ

Natural gas 2 PJ

Total 11 PJ. Division P Education and Training EWES reports consumption of energy in this Division as follows:

Electricity 1.01 PJ

Natural gas NA. As with Division O, most entities in this Division are in the public sector, and excluded from EWES. This exclusion covers not only all government school systems, but also all public universities. Three of the largest universities, Monash, Melbourne and Queensland, report under NGERS. In 2009-10 their total energy consumption was 1.86 PJ, estimated to be divided:

Electricity 1.18 PJ

Natural gas 0.67 PJ. These three universities account for about 15% of total Australian university enrolments, in terms of equivalent full time students (DEEWR, 2012). However, the three universities listed above are more research intensive than many other universities, particularly the smaller and regional universities, and some types of research activities are significantly more energy intensive than teaching. Hence about 10 PJ would be a plausible estimate of total energy consumption by all universities. Data on aggregate energy consumption of state and territory government school and TAFE systems is likely to be available for most jurisdictions. For government schools and TAFE institutions, use of the NSW and SA reports referred to above yields the following rough estimates for the whole country:

Electricity 5 PJ

Natural gas 2 PJ

Total 7 PJ. Division Q Health Care and Social Assistance EWES reports consumption of energy in this Division as follows:

Electricity 16.2 PJ

Natural gas 4.3 PJ. As with Divisions O and P, but to a significantly lesser extent, many entities in this Division are public hospitals, and excluded from EWES. However, the State government previously referenced was used to estimate State and Territory government energy use in hospitals and other health facilities as follows:

Electricity 9 PJ

Natural gas 7 PJ

Total 16 PJ.

Appendix D – Top-down Model Validation

Page 32 of 95

This Division also includes, as Subdivision 86, Residential care services. This includes aged care services and also crisis accommodation, community mental health and other types of residential accommodation services. The great majority of such establishments are operated by private sector entities, either businesses or not for profit organisations, and hence are included in EWES. It is likely that a significant proportion of the non-government energy consumption shown above is associated with these types of facility.

AD.3.2 - Sectors over-represented in EWES Division F Wholesale Trade EWES reports consumption of energy in this Division as follows:

Electricity 16.2 PJ

Natural gas 4.3 PJ. This Division includes, in Subdivision 36, wholesaling of fresh food of all kinds, including meat, poultry, fish, dairy products, vegetables and fruit. Fresh food wholesaling requires the operation of substantial cold stores, with significant electricity consumption. It is likely that a significant proportion of the electricity consumption, and possibly some of the natural gas shown by EWES is used in refrigeration equipment at cold stores. This may be able to be estimated (e.g. ‘cold chain’ analyses by McCann et al) and netted out. Division J Information Media and Telecommunications EWES reports consumption of energy in this Division as follows:

Electricity 10.6 PJ

Natural gas 0.2 PJ

Total 10.8 PJ. This sector includes telecommunications businesses, data centre operators and businesses which operator broadcast transmission facilities. All these activities use considerable quantities of electricity and are excluded from the current study. NGERS includes three major telecommunications businesses (Telstra, SingTel Optus and Vodafone Hutchison), one specialist data centre operator and one specialist broadcast transmitter operator. Together, these businesses report energy consumption of 8.0 PJ, the great majority of which is clearly electricity. The majority of this energy consumption should be deducted to conform to the scope of the present study. Division L Rental, Hiring and Real Estate Services EWES reports very large total consumption of energy in this Division, made up as follows:

Electricity 28.9 PJ

Natural gas 4.9 PJ. This Division includes (in Subdivision 67) property management activities, including the management of offices and apartment buildings, including both strata titled and tenanted. ABS has confirmed that where property managers are responsible for paying electricity bills, the associated energy consumption will be attributed to the property management business.

Appendix D – Top-down Model Validation

Page 33 of 95

Virtually all tenanted and strata titled buildings, both residential and non-residential, employ a property manager with responsible for common area energy consumption. Consequently, energy reported by EWES under this Division includes a substantial quantity used in residential apartment buildings and which therefore, in functional terms, should be allocated to the residential sector. An approximate estimate of the quantity of energy to be reallocated to the residential sector has been made as follows:

Apartments proportion of occupied private dwelling stock: 13% (ABS Cat. No. 4602.0, Table 2)

Assume average per dwelling electricity consumption of apartments (including common are energy) equals per dwelling electricity consumption of the whole housing stock

Total residential energy consumption in 2008-09 215 PJ, in 2009-10 216 PJ. Hence total electricity consumption in apartment buildings 28 PJ

Assume common are electricity consumption is 30% of total building electricity consumption

(Energy Australia, 2005; Sustainability Advice Team, pers. comm.) Hence common area electricity consumption is 8 PJ

Add 2 PJ electricity and 2 PJ natural gas for apartment buildings without separate unit metering and/or centrally supplied space heating/cooling and/or hot water

Total energy reallocated to residential sector is therefore 10 PJ electricity and 2 PJ natural gas.

AD3.3 - Summary The adjusted EWES totals, which should, in principle more closely align with the AES totals for the commercial and services sector as a whole, can be calculated as follows (Table AD.3 below). This is clearly a close reconciliation. Access to more comprehensive and up to date data on State government energy consumption would allow a further refinement. The following uses of energy are included in both AER and EWES totals, because they are part of the commercial and services sector. However, they are outside the scope of the present project. Table AD.3 - Summary Reconciliation, Top Down Data, Commercial Building Energy Consumption, Australia, 2009 (PJ)

(PJ) Electricity Natural gas

AER total reported 207.8 45.5

EWES total reported 178.7 30.6

EWES “deficit” 29 15

minus Division L residential apartment buildings 10 2

plus Division K banks and insurance businesses Say, 6 Say, 1.5

plus Division O Australian government 6.3 1.3

Appendix D – Top-down Model Validation

Page 34 of 95

(PJ) Electricity Natural gas

plus Division O State government operations Say, 9 Say, 2

plus Division O local government operations ? ?

plus Division P government schools and TAFEs Say, 5 Say, 2

plus Division P universities Say 8 Say 2

plus Division Q public hospitals and other health facilities Say, 9 Say, 7

EWES total adjusted 212 44

Finally, AES reports 23 PJ of ADO and other petroleum fuels plus 3 PJ of LPG and very small quantities of coal, briquettes and wood. EWES do not report consumption of these fuels in physical units, but does report expenditure, which is large in a number of Divisions. However, it seems likely that much of this expenditure is on fuels for road transport. AES reports all fuel used for road transport under Division I, irrespective of whether it is being used by a business whose main activity is road transport, or any other business or final consumer, whereas EWES reports road transport fuel consumption against the sector of the business using the fuel. Thus in theory the nearly 23 PJ of ADO reported by AES as used by the commercial and services sector is being used for purposes other than transport (motor vehicles). This seems rather high, given the strong and steady shift towards substitution of natural gas for liquid fuels in stationary applications which has occurred in Australia over the past three decades.

AD.4 - Allocating Energy Consumption to Building Categories A number of ANZSIC divisions are closely associated with particular functional categories of buildings. This makes it possible to make a rough allocation of aggregate energy use to the different main categories of buildings.

Airport terminals

As previously noted, operation of airport terminals is included in ANZSIC Subdivision 53, together with stevedoring facilities, shipping terminal operations (bulk freight and containers), and railway stations. EWES reports total energy consumption (excluding petroleum products) as:

Electricity 5.1 PJ

Natural gas 0.3 PJ. NGERS includes only one commercial building related entity falling into this Subdivision, which is Southern Cross Airports Holdings Corporation Limited, which is the owner/operator of Sydney Airport. Reported total energy consumption in 2009-10 was 0.44 PJ. Application of the analysis described above results in the following estimates of energy consumption:

Electricity 0.36 PJ

Natural gas 0.065 PJ

ADO 0.013 PJ. ADO consumption is presumably fuel used by the various types of vehicles and other mobile equipment at airports. Having regard to the relative size of Sydney Airport, total energy consumption in airport buildings across Australia may be of the order of three to four times that at Sydney, i.e. around 1.5 PJ.

Appendix D – Top-down Model Validation

Page 35 of 95

Warehouses

It can be assumed that most building related energy use in Division F, Wholesale Trade is associated with the operation of warehouses. As previously noted, EWES reports energy consumption as:

Electricity 16.2 PJ

Natural gas 4.3 PJ. It was also previously noted that some of this energy consumption would be associated with the operation of cold store refrigeration. Retail establishments EWES reports total energy consumption of Division G Retail trade as:

Electricity 49 PJ

Natural gas 1.3 PJ. While very high, these figures are consistent with the 8.40 PJ energy consumption reported for the retailing parts of Woolworths and the 8.69 PJ reported for the retailing parts of Wesfarmers (Coles, Kmart, Target, Bunnings) in their respective EEO report. Total energy consumption by these two, plus the other four retail businesses reporting under NGERS (Harvey Norman, Aldi, Myer, David Jones) is 19.9 PJ. It should be noted that some energy reported in Division L, Rental, Hiring and Real Estate Services, will also be associated with retailing, such as the 1.30 PJ which the Westfield EEO report for 2010-11 records as used in its shopping centres. Hence the EWES figure may be a minimum estimate.

Hospitals

As previously noted, energy use in Division Q, Health Care and Social Assistance consists mainly of energy used in hospitals and energy used in residential care facilities of various kinds. The rough estimate of energy use in government hospitals is 16 PJ. Three large private hospital operators report under NGERS and have total energy consumption of 2.1PJ. The addition of other privately operated public hospitals and private hospitals might increase total hospital energy consumption to the order of around 22 PJ.

Universities

Total energy consumption by universities has been estimated to be of the order of 10 PJ.

Schools and TAFEs

Total energy consumption by public and private schools and TAFEs is estimated to be of the order of:

Electricity 6 PJ

Natural gas 2 PJ.

Hotels

ANZSIC Division H Accommodation and Food Services includes hotels, but also restaurants, cafes, pubs, bars and clubs. EWES reports total energy consumption as follows:

Electricity 30 PJ

Natural gas 13 PJ. Presumably the majority, but no means all of this energy consumption is associated with hotels, which constitute Subdivision 44. Woolworths, which is a large owner of pubs, indicates in its EEO reports that

Appendix D – Top-down Model Validation

Page 36 of 95

it consumed 2.6 PJ in activities other than retailing in 2010-11. The McDonald’s EEO report records 0.48 PJ used in its restaurants.

Arts and entertainment

ANZSIC Division R Arts and Recreation Services include most of the activities associated with this category of buildings, including casinos. However cinemas are included in Division J, Information Media and Telecommunications. EWES reports total energy consumption in Division R of 6 PJ. Two major casino operators, Crown and Tabcorp, report total energy consumption of 1.8 PJ under NGERS. Amalgamated Holdings, which owns Greater Union cinemas, reports 0.8 PJ energy consumption, but it also operates hotels, resorts and film production facilities.

Miscellaneous buildings

ANZSIC Division S, Other services, includes motor vehicle and machinery repairs and maintenance, funeral services and crematoria, laundries, dry cleaners, car parking facilities, and religious establishments. EWES reports total energy consumption in Division S of 8.5 PJ. By way of example, Spotless Group reports 0.64 PJ used in its laundry services in its 2010-11 EEO report.

Offices and related buildings

If it is assumed that all the remaining energy consumption reported by EWES takes place in offices or related buildings, the resultant estimated energy consumption figures are:

Electricity 64 PJ

Natural gas 10 PJ.

AD.5 - Comparison with Other Reports Several reports were publicly available that summarised the total energy use of state and territory government owned and leased buildings. These were used as part of the validation process whereby the reported energy consumption (and in some cases energy intensities) was compared to estimates within the NRBuild model. Data was obtained for various government buildings in NSW, Victoria, SA and the NT for various years.

AD.5.1 - NSW Sources The NSW Energy Use in Government Operations Report 2002-3 published the energy consumption of public hospitals, public buildings, law courts, office tenancies and office buildings (combined services) for 2003. The table below shows the reported values in NSW and those estimated by the NRBuild model.

Hospitals

Table AD.4 below shows that in 2002-03, the NSW Report estimated that Public Hospital energy use was 3.42 PJ. By comparison, the NRBuild model estimates NSW total (public and private) hospital energy use at 5.7 PJ. The stock data within NRBuild estimates that 90% of total hospital floorspace is public hospitals. On that basis, and assuming that the energy intensities of public and private hospitals are the same, the estimate for public hospital energy use within the NRBuild model would be 5.13PJ which is about 50% more energy than the NSW reported amount.

Appendix D – Top-down Model Validation

Page 37 of 95

Table AD.4 - Comparison, NSW Health Data and NRBuild

Public Hospitals

Public Buildings

Law Courts

Office tenancies

Office Buildings (Combined Services)

NSW Annual Report 2002-03

3.42 PJ (1,175 MJ/m2.a)

0.3 PJ

0.16 PJ

488 MJ/m².a

Reported range of energy intensity was from 298 to 2,113 MJ/m².a, with a weighted average of 890 MJ/m².a

NRBuild Model

5.7 PJ (1454 MJ/m2.a capital city and 1039 MJ/m2.a regional hospitals, averages for all years)

0.43PJ

0.14PJ

Australia average of 377 MJ/m2.a. NB no NSW specific data obtained for NSW capital city

Australia average of 930MJ/m2 .a for base building+ tenancy. NB no NSW specific data was obtained

There are several possible reasons for the discrepancy. The first relates to floor area. The floor areas estimates within the NRBuild model may not align with those used to calculate total energy consumption in the NSW report. It is unclear from the NSW report how total area is calculated (total it is not reported), however, it does note that the Hospitals category covers the energy consumption in buildings and facilities primarily used as hospitals, which includes mental health and other hospitals as per the New South Wales Public Hospitals Comparison Data Book. Second, the report acknowledges that there are possible reporting errors, which is also a possibility for data gathered for the NRBuild model. The third issue relates to average energy intensity figure used in the NRBuild model to calculate NSW total hospital energy, and figure in the NSW report. The figure used in the NRBuild model was the estimated national average figure of around 1500MJ/m2, which is about 28% higher than the weighted average figure reported for NSW. This would account for a considerable proportion of the difference in total energy between the two sources. Relatedly, the weighted average energy intensity figure used in the NRBuild model is calculated using energy data captured for various hospitals and healthcare facilities, for which the energy intensity can vary considerably.

Public Buildings

The buildings included this category correspond to those used in the NRBuild model, namely public libraries, museums and art galleries. The table shows that in NSW 2002-03, the reported estimated Public Building energy use at about 0.3 PJ, whereas the NRBuild model estimates it to be about 0.43 PJ. One reason for at least part of the discrepancy is that the NSW Report covers only NSW state government public buildings while the NRBuild model also covers local government public buildings such as libraries and galleries.

Appendix D – Top-down Model Validation

Page 38 of 95

In 2002-03, energy intensities reported in NSW for public buildings ranged from 373 MJ/m².a to 3,617 MJ/m².a (Art Gallery of New South Wales), with a weighted average of 1,112 MJ/m²/annum. For the NRBuild model, the energy intensity for NSW capital cities public buildings in 2002-03 was based on one sample, the National Maritime Museum, and therefore is not representative of all NSW public buildings. However, the weighted average energy intensity for all Australia public buildings for all years, which was used to estimate the NSW total in the NRBuild model, was 1062MJ/m2.a. This is very similar (about 5% less) than the reported NSW weighted average. That the weighted average intensities are similar, yet the estimates of total energy consumption between the two sources vary by about 43%, reinforces the view that the difference relates to the greater coverage of buildings.

Law Courts

The total energy consumption reported under the Law courts category in the NSW was about 0.16 PJ in 2002-03, which is reasonably similar to the NRBuild model estimate of 0.14 PJ for NSW in that year. In 2002-03, energy intensities reported in the Government Energy Management Policy (NSW) Report ranged from 317 to 872 MJ/m².a, with a weighted average of 557 MJ/m².a. Although the average energy intensity estimate for NSW in the NRBuild model for 2003 is based on 2 records only, they also range considerably from 325 MJ/m2.a to 721 MJ/m2.a. In the NRBuild model, the 2003 national weighted average energy intensity for law courts is estimated at 500 MJ/m2.a, about 10% less than NSW reported weighted average for that year. This difference accounts for almost all of the difference in the estimates for total energy consumption.

Office Buildings

The NSW Report notes that energy performance indicators were not necessarily reliable or available for all office categories and that more reliable reporting of normalisation factors (e.g. occupancy, area) would be required for any trends to be accurately established and tracked over time. It should also be noted that the NSW Report does not distinguish between government owned and government leased office space whereas this distinction is made in the NRBuild model. In 2002-03, the weighted average energy intensity of offices tenancies reported in the NSW was 488 MJ/m².a. In the NRBuild model there is only one record for NSW in 2003, which is a regional office with an energy intensity of 293 MJ/m2.a. However, it was evident from the analysis of NRBuild data that the energy intensities of offices vary considerably, and depend on their size and location, a point the NSW Report also makes. Weighted average energy intensities were reported for combined office services (tenancies + base buildings). Reported energy intensity ranged from 298 to 2,113 MJ/m².a, with a weighted average of 890 MJ/m²/annum national. By comparison, the Australia weighted average estimated in the NRBuild model was of 930MJ/m2.a, about 4% higher the reported NSW figure.

AD.5.2 - Victorian Sources The Victorian Department of Health 2010-11 Annual Report presents a summation of energy data reported by health services to the department, although not all data for small healthcare facilities is reported. The report also notes that the data presented has not been externally verified. : 1833-00: Health SA

Appendix D – Top-down Model Validation

Page 39 of 95

Table AD.5 - Comparison, Victorian Public Hospital Energy Use, 2010-2011, and NRBuild

Total Energy

Energy Intensity

Victoria Health Dept 2010-11 Annual Report

4.43 PJ

1,800MJ/m2.a

NRBuild (all hospitals)

4.29 PJ

1,393MJ/m2.a for Victoria capital city and 1,627MJ/m2.a regions

Table 17.5 above compares the reported total energy use and average energy intensity for Victoria public hospitals in 2010-11 with NRBuild estimates for 2011. The Table shows that in 2010-11, the Victoria Health Department estimated that Public Hospital energy use was 4.43 PJ. By comparison, the NRBuild model estimates NSW total (public and private) hospital energy use at 4.29 PJ. The stock data within NRBuild estimates that about 96% of total hospital floorspace in Victoria is public hospitals. On that basis, and assuming that the energy intensities of public and private hospitals are the same, the estimate for public hospital energy use within the NRBuild model would be 4.25 PJ which is about only about 4% less energy than the Victoria reported amount.

AD.5.3 - South Australian Sources The SA Department of Health Annual Report 2009-2010 provides estimates of total energy, total floor area, and average energy intensity for SA public health units for 2001 and 2009-10. (A health unit is all floor area that relates to public health facilities but excludes office space and the ambulance service). The table below compares those estimates with the estimates within the NRBuild model for 2001 and 2010. Table AD.6 - Comparison, SA Health Data and NRBuild

SA Annual Report NRBuild

Total Energy Total floor area

Energy Intensity

Total Energy (public hospitals)

Total floor area (public hospitals)

Energy Intensity

2001

1,250,725GJ

964, 641 m2

1,310 MJ/m2

1,080,112GJ

748,000

SA capital city average, all years, 1,259MJ/m2.a

2009-10

1,174,471 GJ

1,093,093m2

1,290 MJ/m2

1,332,454GJ

851,000

SA capital city average, all years, 1,259MJ/m2.a

For years 2001 and 2009-10, the public hospital floor area estimates within the NRBuild model are about 23% less than the SA estimates. In the NRBuild model, floor area estimates are made on NLA basis, however, it is not clear how floor area is calculated for the SA report. If it was calculated on a GFA basis it would provide a possible explanation for the difference between the two sources, as NLA is typically about 20% less than GFA. Irrespective of whether SA Health has calculated floor area as NLA or GFA, it is unclear as to what is included in the total floor space calculation. As discussed in the body of this report, there are definitional differences between the states/territories as to what is; a) a hospital and b) what parts of a hospital are counted in the total square metre calculation.

Appendix D – Top-down Model Validation

Page 40 of 95

For 2001 the SA Health Department reported that total energy estimate for public health space was 1,250,725GJ whereas the estimate in the NRBuild model total energy use is about 14% less, at 1,080,112 GJ. For 2010 the SA reported total energy estimate was 1,174,471GJ whereas the estimate for total energy in the NRBuild model is about 13% more at 1,332,454GJ. For 2001 and 2010, the SA reported energy intensities of public health units were 1,310 MJ/m2.a and 1,290 MJ/m2.a, respectively. These energy intensities are very similar to the SA capital city average, for all years, of 1,259MJ/m2 estimated in the NRBuild model, noting however that the total energy consumption has been calculated using the Australia weighted average energy intensity for all years.

AD.5.4 - Northern Territory Sources The Northern Territory Government Building Energy and Greenhouse Report, 2006-07 presents estimates of the total energy consumption for public hospitals, public schools and law courts for the year 2006-07. Table AD.7 below compares those estimates with the estimates within the NRBuild model for the year 2007. The table shows that reported energy use for NT public hospitals and the total estimated in the NRBuild model are very similar, with the NRBuild estimate being about 1% higher. For public schools the total reported energy use is about 260 % higher than the estimate in the NRBuild model. The total energy estimate in the NRBuild model has been calculated by multiplying the estimated building stock by the Australia weighted average energy intensity for schools. The Australia weighted average over all years is 211MJ/m2.a, which is about 50% lower than the NRBuild model NT average over all years (416MJ/m2.a and 408 MJ/m2.a for capital city and regional schools, respectively). However, even if these figures were applied to the public school stock in the NRBuild model, the total energy would still be about 50% less than the NT reported amount. The table shows that for law courts the total reported energy use is about 58% of the NRBuild model estimate. This could be due to one or a combination of reasons. The average energy intensity figure used in the NRBuild may not accurately reflect the actual energy intensity of NT law courts; the NRBuild overestimates the law court floor space; or the data used in the NT report is inaccurate. Table AD.7 - Comparison, NT Energy and Greenhouse Report Data and NRBuild

Annual Report NRBuild

Public Hospitals

Public Schools

Law Courts Public Hospitals

Public Schools

Law Courts

Total Energy

250,805 GJ

156,811 GJ

11,752 GJ

253,800GJ

60,000 GJ

20,000GJ

Appendix E – Statistical Analysis

Page 41 of 95

Appendix E – Statistical Analysis

AE.1 Minimum Sample Size The minimum number of building records required in order to make any observations or conclusions is dependent on two variables, namely the confidence level and accuracy required. The confidence level and accuracy are variables that will be pre-determined by the user. We have approximated our analysis based on normal distribution. In reality, the data bears more resemblance to a Poisson distribution, which is a skewed distribution. As such, we have noted several caveats in the corresponding sections regarding our assumptions.

AE.1.1 Infinite Population Size Statistically, the minimum number of building records required for any analysis is calculated according to Equation 1. Equation 1 – Minimum number of buildings required for infinite population

(

⁄

)

⁄

( ) Using Equation 1 we have calculated the minimum number of buildings required for each building type using the standard deviation and mean of the current dataset. Note that this assumes that a confidence level of 95% that each sample mean is within 10% of the population mean. The results are tabulated in Table AE.1- Note that these are numbers worked out based on the assumption that the population (actual number of buildings within category) is infinite. Table AE.1 - Minimum number of samples required for each building type, n0

Building Type Classification Minimum Number of Building Records Required per year

Offices

Government Owned (Tenancy)

259

Government Owned (Base Building)

252

Government Owned (Whole Building)

199

Privately Owned (Tenancy)

236

Privately Owned (Base Building)

103

Privately Owned (Whole Building)

117

Hotels - 240

Retail

Shopping Centre (Base Building)

182

Shopping Centre (Whole Building)

296

Supermarket (Whole Building)

31

Shopping Centre (Tenancy)

929

Supermarket (Tenancy) 24

Hospitals - 481

Schools - 926

Appendix E – Statistical Analysis

Page 42 of 95

Building Type Classification Minimum Number of Building Records Required per year

Tertiary Education TAFE Campus 124

Universities 1056

Public Buildings Government Owned 335

Privately Owned 41

Law Courts - 415

Correctional Centres - 50

AE.2 Finite Population Size In the case where the population (particularly when divided into regions) is not infinite and the required sample size presented in Table AE1 may be greater than the actual population for smaller states (e.g. Tasmania or Northern Territory). The minimum number of buildings required is calculated using Equation 2 Equation 2 - Minimum number of buildings required for finite population

( )

For finite population sizes, we have approximated the minimum sample size as a percentage of

population (

) given the same confidence interval and accuracy in Section AE.2

(rearrangement of Equation 2 gives Equation 3). The population size has been approximated using the building stock model by BIS Shrapnel (FY2010), with the average building size calculated from the current energy data available within this project. The relationship between nsample and n0 is illustrated in AE.1 Equation 3 - minimum number of buildings required as a function of building population

(

)

(

)

(

) (

)

Table AE.1

Figure AE.1 - Relationship between minimum samples required for an infinite population (n0) and minimum samples required for a finite population (NSAMPLE)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 200% 400% 600% 800% 1000% 1200%

nsa

mp

le/N

po

pu

lati

on

n0/Npopulation

Appendix E – Statistical Analysis

Page 43 of 95

The equation inputs used to calculate the minimum samples required for a finite population (Nsample) are presented in Tables AE2-AE10. Abbreviations used in the following tables, in alphabetic order: BB: Base building CAP: Capital city GOV: Government-owned PRI: Privately-owned REG: Regional TEN: Tenancy WB: Whole building Table AE.2 – Offices

Location State Population

Size Ownership

Type Category

Standard Deviation (MJ/m2)

Mean (MJ/m2)

CAPIT

AL C

ITY

NSW 1066

GO

VER

NM

EN

T

TEN

318.4 388.1

VIC 744

QLD 332

WA 247 BB

386.6

477.4 SA 144

TAS 38

ACT 200 WB

530.3 737.3

NT 25

REG

ION

NSW 913

PR

IVA

TE

TEN

347.1

443.1 VIC 468

QLD 604

WA 166 BB

290.9

563.5 SA 100

TAS 101

NT 30 WB 522.3 946.6

Table AE.3 – Hotels

Location State Population

Size

Standard Deviation (MJ/m2)

Mean (MJ/m2)

CAPIT

AL C

ITY

NSW 91 1,122.5 1,422.7

VIC 62

QLD 27

WA 27

SA 19

TAS 6

ACT 13

NT 8

REG

ION

NSW 148

VIC 65

QLD 183

WA 40

SA 22

TAS 17

NT 18

Appendix E – Statistical Analysis

Page 44 of 95

Table AE.4 - Retail Shopping Centre

Location State Population

Size Category

Standard Deviation (MJ/m2)

Mean (MJ/m2)

CAPIT

AL C

ITY

NSW 186 BB

268.1

390.5 VIC 199

QLD 73

WA 71

SA 51

TAS 12

ACT 17 WB 703 802

NT 6

REG

ION

NSW 175

VIC 103

QLD 159

WA 36 TEN

2,410.9

1,550.3 SA 33

TAS 25

NT 5

Table AE.5 - Retail Supermarket

Location State Population

Size Category

Standard Deviation (MJ/m2)

Mean (MJ/m2)

CAPIT

AL C

ITY

NSW 540 WB 976.7 3,457.4

VIC 688

QLD 349

WA 286

SA 194

TAS 34

ACT 53

NT 16

REG

ION

NSW 429 TEN 840.7 3,366.1

VIC 150

QLD 354

WA 91

SA 70

TAS 44

NT 14

Appendix E – Statistical Analysis

Page 45 of 95

Table AE.6 - Hospital

Location State Population

Size

Standard Deviation (MJ/m2)

Mean (MJ/m2)

CAPIT

AL C

ITY

NSW 41 1,965 1,756.7

VIC 27

QLD 20

WA 17

SA 12

TAS 2

ACT 3

NT 2

REG

ION

NSW 45

VIC 32

QLD 42

WA 14

SA 10

TAS 4

NT 3

Table AE.7 - Schools

Location State Population

Size

Standard Deviation (MJ/m2)

Mean (MJ/m2)

CAPIT

AL C

ITY

NSW 1604 347.4 223.8

VIC 1548

QLD 656

WA 654

SA 476

TAS 95

ACT 166

NT 59

REG

ION

NSW 2024

VIC 1249

QLD 1734

WA 507

SA 394

TAS 270

NT 93

Appendix E – Statistical Analysis

Page 46 of 95

Table AE.8 - Public Buildings

Location State Population Size Ownership Type Standard Deviation (MJ/m2)

Mean (MJ/m2)

CAPIT

AL C

ITY

NSW 15

GO

VER

NM

EN

T

787.5 843.8

VIC 16

QLD 7

WA 10

SA 6

TAS 1

ACT 11

NT 2

REG

ION

NSW 15

PR

IVA

TE

318.2 975.1

VIC 8

QLD 17

WA 6

SA 4

TAS 3

NT 2

Table AE.9 - Law Courts

Location State Population

Size

Standard Deviation (MJ/m2)

Mean (MJ/m2)

CAPIT

AL C

ITY

NSW 40 475.3 457.5

VIC 25

QLD 5

WA 16

SA 12

TAS 0

ACT 19

NT 4

REG

ION

NSW 0

VIC 0

QLD 0

WA 38

SA 0

TAS 0

NT 7

Appendix E – Statistical Analysis

Page 47 of 95

Table AE.10- Universities

Location State Population

Size

Standard Deviation (MJ/m2)

Mean (MJ/m2)

CAPIT

AL C

ITY

NSW 105

202.4

357.4

VIC 113

QLD 55

WA 56

SA 34

TAS 10

ACT 19

NT 5

REG

ION

NSW 153

VIC 86

QLD 105

WA 0

SA 1

TAS 1

NT 0

Table AE.11- VETS

Location State Population

Size

Standard Deviation (MJ/m2)

Mean (MJ/m2)

CAPIT

AL C

ITY

NSW 34

1,439.5

868.5

VIC 40

QLD 16

WA 13

SA 13

TAS 3

ACT 4

NT 1

REG

ION

NSW 30

VIC 26

QLD 23

WA 6

SA 5

TAS 4

NT 2

The results are presented in Tables through to AE.12 – AE.16, where the ratio is 0%, it means that the population size is 0 (there are no buildings within that category). To determine the minimum number of buildings required to be sampled to achieve 95% confidence level and an accuracy within 10% of the mean, multiply the corresponding ratio with the known population size. The resulting numbers suggest that where we have large ratios e.g. 98% of population, the remaining small percentage of buildings (e.g. 2%) is causing a large deviation in the mean. This is not possible as the finite population requirement should be reducing the sample size, and must also be significantly less than the population size itself. In practice, the sampling of small population should have a smaller variance, and therefore a smaller minimum sample size requirement. This is not captured in our analysis due to the normal distribution assumption. For the numbers presented in tables below

several caveats must be noted as the (

) ratio approaches 100%:

1. The population is tiny, OR

2. The extrapolation of variance to population is likely to be invalid, OR

3. The mean to standard deviation ratio is very low.

Appendix E – Statistical Analysis

Page 48 of 95

Table AE.12 - Ratio of Minimum sampled buildings to population size for office buildings

Region Classification Government

Owned (Tenancy)

Government Owned (Base

Building)

Government Owned (Whole

Building)

Privately Owned

(Tenancy)

Privately Owned (Base

Building)

Privately Owned (Whole

Building)

Capital City

NSW 20% 19% 16% 18% 9% 10%

VIC 26% 25% 21% 24% 12% 14%

QLD 44% 43% 38% 42% 24% 26%

WA 51% 51% 45% 49% 30% 32%

SA 64% 64% 58% 62% 42% 45%

TAS 88% 87% 84% 86% 74% 76%

ACT 57% 56% 50% 54% 34% 37%

NT 92% 91% 89% 91% 81% 83%

Regional NSW 22% 22% 18% 21% 10% 11%

VIC 36% 35% 30% 34% 18% 20%

QLD 30% 29% 25% 28% 15% 16%

WA 61% 60% 55% 59% 38% 41%

SA 72% 72% 67% 70% 51% 54%

TAS 72% 72% 67% 70% 51% 54%

NT 90% 90% 87% 89% 78% 80%

Table AE.13 - Ratio of minimum sampled buildings to population size for hotels, hospitals and schools

Region Classification Hotels Hospitals Schools

Capital City NSW 73% 92% 37%

VIC 80% 95% 37%

QLD 90% 96% 59%

WA 90% 97% 59%

SA 93% 98% 66%

TAS 98% 100% 91%

ACT 95% 100% 85%

NT 97% 100% 94%

Regional NSW 62% 92% 31%

VIC 79% 94% 43%

QLD 57% 92% 35%

WA 86% 97% 65%

SA 92% 98% 70%

TAS 94% 99% 78%

NT 93% 100% 91%

Appendix E – Statistical Analysis

Page 49 of 95

Table AE.14 - Ratio of minimum sampled buildings to population size for retail

Region State Shopping

Centre (Base Building)

Shopping Centre (Whole Building)

Supermarket (Whole

Building)

Shopping Centre

(Tenancy)

Supermarket

(Tenancy)

Capital City

NSW 50% 62% 5% 83% 4%

VIC 48% 60% 4% 82% 3%

QLD 72% 80% 8% 93% 6%

WA 72% 81% 10% 93% 8%

SA 78% 86% 14% 95% 11%

TAS 94% 96% 48% 99% 42%

ACT 92% 95% 37% 98% 32%

NT 97% 98% 67% 99% 62%

Regional NSW 51% 63% 7% 84% 5%

VIC 64% 74% 17% 90% 14%

QLD 54% 65% 8% 85% 6%

WA 84% 89% 26% 96% 21%

SA 85% 90% 31% 97% 26%

TAS 88% 93% 42% 97% 36%

NT 98% 99% 70% 100% 65%

Table AE.15 - Ratio of minimum sampled buildings to population size for tertiary education facilities

Region State University Buildings VET Campuses

Capital City NSW 91% 79%

VIC 90% 76%

QLD 95% 89%

WA 95% 91%

SA 97% 91%

TAS 99% 99%

ACT 98% 98%

NT 100% 100%

Regional NSW 87% 81%

VIC 93% 83%

QLD 91% 85%

WA 0% 97%

SA 100% 97%

TAS 100% 98%

NT 0% 100%

Appendix E – Statistical Analysis

Page 50 of 95

Table AE.16 - Ratio of minimum sampled buildings to population size for public Buildings8

Region State Government-Owned Privately Owned

Capital City NSW 96% 76%

VIC 96% 74%

QLD 98% 88%

WA 98% 83%

SA 99% 89%

TAS 100% 100%

ACT 97% 82%

NT 100% 99%

Regional NSW 96% 76%

VIC 98% 87%

QLD 96% 73%

WA 99% 90%

SA 99% 94%

TAS 100% 97%

NT 100% 99%

Table AE.17 - Ratio of minimum sampled buildings to population size for law courts and correctional centres

Region State Law Courts Correctional Centres

Capital City NSW 91% 72%

VIC 95% 83%

QLD 99% 85%

WA 97% 86%

SA 97% 94%

TAS 0% 98%

ACT 96% 100%

NT 99% 98%

Regional NSW 0% 88%

VIC 0% 96%

QLD 0% 86%

WA 92% 98%

SA 0% 100%

TAS 0% 100%

NT 99% 100%

Tables AE.18-AE.27 below show the minimum sample size required for each year, for each buildings type, to achieve a 95% confidence level and an accuracy within 10% of the mean.

8 Note that for public buildings, the stock model does not split buildings into government-owned and privately owned buildings. In this instance, we have used the same population (Npopulation)for both categories. As such, the actual ratio might not be accurate in its entirety e.g. almost all public buildings in the ACT are likely to be government-owned.

Appendix E – Statistical Analysis

Page 51 of 95

Table AE.18 - Minimum sample size, finite population, Office whole buildings

Capital City

Region

NSW 171 164

VIC 156 140

QLD 126 151

WA 111 91

SA 84 67

TAS 32 67

ACT 100

NT 22 26

Table AE.19- Minimum sample size, finite population, Hotels

Capital City Region

NSW 66 92

VIC 50 51

QLD 24 104

WA 24 34

SA 18 20

TAS 5 16

ACT 12

NT 7 17

Table AE.20- Minimum sample size, finite population, Hospitals

Capital City Region

NSW 38 41

VIC 26 30

QLD 19 39

WA 17 14

SA 12 9

TAS 2 3

ACT 3

NT 2 3

Table AE.21- Minimum sample size, finite population, Schools

Capital City Region

NSW 594 627

VIC 591 537

QLD 387 607

WA 386 330

SA 314 276

TAS 86 211

ACT 141

NT 55 85

Appendix E – Statistical Analysis

Page 52 of 95

Table AE.22- Minimum sample size, finite population, University buildings

Capital City Region

NSW 96 133

VIC 102 80

QLD 52 96

WA 53

SA 33 1

TAS 9 1

ACT 18

NT 5 0

Table AE.23- Minimum sample size, finite population, VETS

Capital City Region

NSW 27 24

VIC 30 22

QLD 14 20

WA 12 5

SA 12 4

TAS 2 3

ACT 3

NT 1 2

Table AE.24- Minimum sample size, finite population, Shopping centres

Capital City Region

NSW 93 90

VIC 96 66

QLD 53 86

WA 51 30

SA 40 28

TAS 11 22

ACT 16

NT 5 5

Table AE.25- Minimum sample size, finite population, Supermarkets

Capital City Region

NSW 27 30

VIC 28 26

QLD 28 28

WA 3 24

SA 27 22

TAS 16 19

ACT 20

NT 11 10

Appendix E – Statistical Analysis

Page 53 of 95

Table AE.26- Minimum sample size, finite population, Public buildings

Capital City Region

NSW 14 14

VIC 15 7

QLD 6 10

WA 9 5

SA 5 3

TAS 1 3

ACT 10

NT 2 2

Table AE.27- Minimum sample size, finite population, Law courts

Capital City Region

NSW 36

VIC 24

QLD 4

WA 16 35

SA 11

TAS

ACT 18

NT 4 6

AE.3 Statistical Analysis Analysis was conducted on the underlying data to determine the statistical significance of any trends observed from the dataset. The series of tests undertaken are listed below:

1. Histograms. This reveals the spread of the data and facilitates identification of any abnormalities in the data.

2. T-tests. A two-tail T-test assesses whether the means of two groups (which can be different) are statistically different from each other (μ1≠μ2). A one-tail T-test assesses the statistical confidence of any magnitude comparisons between two means (μ1>μ2 or μ1<μ2).

3. x-y scatter plots. This is a visual method to determine the relationship between two variables.

4. Linear regression. This reveals the correlation between two variables. A low R2 value signifies low correlation.

5. Error bars. These shows if the difference between each mean (in this case, average EUI) is statistically different and also the accuracy of each mean. It is calculated assuming 95% confidence level and Equation 4.

Equation 4 - Standard error assuming an infinite population

√

In each case, the hours of operation weighted averages found in NRBuild1point1 were used.

Appendix E – Statistical Analysis

Page 54 of 95

AE.3.1 Offices

Data Distribution The spread of EUI for the different office building types collected is presented using a histogram. The histogram is shown below.

Figure AE.2 - Histogram energy use intensity

Error Bars Error bars were added to each time-series plot assuming a 95% confidence level. The error bars and R2 values for each office building type reveal the following:

The standard error is within 8% to 28% of the mean for offices (whole building), 6% to 12% of the mean for offices (base building) and 16% to 48% of the mean for offices (tenancy). This suggests that we have higher confidence in base building offices data as compared to the latter two.

There is a low correlation between years and average EUI. Note that the high R2 value for the Offices (Whole Building) plot (AE.5) is misleading. This is explained below and a new Offices (Whole Building) plot is presented in figure AE.9.

Therefore no time-series trends can be predicted using these data.

Appendix E – Statistical Analysis

Page 55 of 95

Figure AE.3 - Offices (tenancy) time-series average energy intensity

y = -1.5439x + 3486.6 R² = 0.0379

0

100

200

300

400

500

600

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

Ave

rage

EU

I p

er

year

n>=95

Linear (n>=95)

Appendix E – Statistical Analysis

Page 56 of 95

Figure AE.4 - Offices (Base Building) time-series average energy intensity

y = -6.1342x + 12856 R² = 0.1434

0

100

200

300

400

500

600

700

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

Ave

rage

EU

I p

er

year

n>50 Linear (n>50)

Appendix E – Statistical Analysis

Page 57 of 95

Figure AE.5 - Offices (Whole building) time-series average energy intensity

Discrepancy between Whole Building and Base Building +Tenancy The average EUI per year for Offices (Whole Building) seems unusually low for years prior to 2007. The whole building plot also seems to suggest a strong upward trend for average EUI per year, conflicting observations from the base building and tenancy plot. To investigate this discrepancy, histograms for pre-2007 and post-2007 were plotted for data sources, average EUI and NLA (Figures AE.7 and AE.8) Conclusions drawn from that are:

The pre-2007 data sources are mostly from OSCAR data and post-2007 data sources are mostly from NABERS data.

Figures AE.7 and AE.8 show that OSCAR data quality is questionable, given that roughly 50% of the data has EUI that is less than 400MJ/m2. Figure AE8 reveals that the majority of OSCAR data set has NLA less than 400m2 or between 2000-5000m2. OSCAR buildings between 2000-5000m2 have an average EUI of 283MJ/m2 and buildings with an NLA that is smaller than 400m2 have an average EUI of 536MJ/m2 (excludes outliers with power density greater than 90W/m2) further demonstrating that the OSCAR data set is not very reliable.

Given the non-homogenous nature of the offices (whole building) data sources, OSCAR data should probably be excluded from any trend prediction, or at least treated differently due to the poor data quality. A new Offices (Whole Building) time-series average energy intensity graph is plotted in Figure AE.9 excluding OSCAR buildings. This new plot solves the former incongruity between the (base building + tenancy) and whole building trend lines.

y = 54.726x - 109078 R² = 0.7225

0

200

400

600

800

1,000

1,200

1,4001

99

9

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

Ave

rage

EU

I p

er

year

n>=30

Base + TenancyBest Fit

Total n, all periods, B+T: 3152

Appendix E – Statistical Analysis

Page 58 of 95

Figure AE.6 - Data sources for office (Whole Building) pre-2007 and post-2007

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

OSCAR Exergy NTGovernment

DCI

NTGovernment

DPC

NTGovernment

NRETAS

NABERS OfficeAssessments

QLD PublicWorks

QLD Health ParsonsBrinckerhoff

Pe

rce

nta

ge F

req

ue

ncy

(N

um

be

r o

f B

uild

ings

/Sam

ple

Siz

e)

Data Sources

Pre- 2007 Frequency Post -2007 Frequency

Appendix E – Statistical Analysis

Page 59 of 95

Figure AE.7 - Average EUI for offices (whole Building) pre-2007 and post-2007. As can be seen from the histogram, the average EUI for pre-2007 is concentrated within 201-400MJ/m2 p.a.

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

200 400 600 800 1000 1200 1400 1600 1800 2000 2200 More

Perc

enta

ge F

requ

ency

(Num

ber o

f Bui

ldin

gs/T

otal

Sam

ple

Size

)

Average EUI

Pre-2007 Frequency Post 2007 Frequency

Appendix E – Statistical Analysis

Page 60 of 95

Figure AE.8 - Histogram of NLA for offices (Whole Building) pre-2007 and post-2007. As can be seen from the histogram, the NLA for pre-2007 is concentrated within the 0-400sqm bin and 2001-5000sqm bin.

0%

5%

10%

15%

20%

25%

30%

35%

400 800 1200 1600 2000 5000 10000 15000 20000 30000 More

Perc

enta

ge F

requ

ency

(Num

ber o

f Bui

ldin

gs/T

otal

Sam

ple

Size

)

Average NLA (m2)

Pre-2007 Frequency Post-2007

Appendix E – Statistical Analysis

Page 61 of 95

Figure AE.9 - Offices (Whole building) excluding Oscar Data Time-Series Average Energy Intensity

Tenancy Roughly 4% of office tenancies data have EUI greater than 80W/m2 (assumes 60 hours/week operation). This is an exceptional load that falls at the extreme of the viable definition of tenancy operation, and most likely is strongly affected by exceptional IT loads. These high load tenancies have a significant impact on the standard error/ standard deviation for this data set. The effects of the suspect tenancies were examined by comparing the standard deviation9 and standard error10 for each year, and depicted in Table AE.28 it can be seen, the standard deviation is almost halved in most years following the removal of the suspect tenancies. This means that the smaller the standard deviation, the less the uncertainty in any predictions or modelling done with this dataset. Where we can verify that the tenancy has a data centre, we recommend that the tenancies are treated separately from the general population as tenancies with data centres are not representative of the average office tenancy. This will improve the accuracy of any benchmarks produced using the dataset. However, we note that high EUIs can also be caused by long operating hours e.g. out of hours equipment left running or high intensity trading floors. The energy use of tenancies is widely variable for a wide range of factors. Without further classification by hours and IT application, this cannot be narrowed down. To get better certainty of this, we recommend that the tenancy data set is broken down into smaller sub-populations based on a range of variables.

9 Describes how much the data point deviate from the mean (average) value. 10 Affected by the standard deviation of the data, describes the accuracy and uncertainty of the data points

R² = 0.6617

R² = 0.0053

0

200

400

600

800

1,000

1,200

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

MJ/

m2

OSCAR Others (excl OSCAR)

Base Building + Tenancy Best Fit Linear (OSCAR)

Linear (Others (excl OSCAR))

Appendix E – Statistical Analysis

Page 62 of 95

Table AE.28 - Standard deviation and Standard error comparison with and without tenancies with EUI greater than 80W/m2

YEARS 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Current standard deviation:

138.8 91.4 302.0 192.5 201.8 235.1 251.2 259.3 405.1 255.7 417.4 378.4 410.3

Standard deviation with tenancies>80W/m

2

removed

138.8 91.4 189.4 165.4 158.3 180.1 135.9 149.6 164.9 150.0 163.2 172.0 146.4

Current standard error

58 38 55 39 39 43 38 41 56 35 52 47 68

Standard error with tenancies>80W/m

2

removed

58 38 35 34 31 33 21 24 24 21 21 22 25

Statistical Significance of Conclusions Drawn for Office Buildings

Introduction T-tests were conducted to compare the statistical confidence in the difference between two data sets. The results are summarised below. We note that the mean obtained in our T-tests using simple averaged EUIs and the mean obtained using area weighted EUIs shown in the report (Ref: DCCEE 600/2010) can vary up to 35%. The statistical difference between two data sets are determined by P(T<=t) values in two-tail t-tests. The smaller the P value, the greater the statistical likelihood of genuine mean difference. The criteria used in our analysis are as below:

P(T<=t) >= 0.05 : no statistical confidence in difference

0.01 <= P(T<=t) < 0.05 : minor statistical confidence in difference

P(T<=t) < 0.01 : strong statistical confidence in difference.

The following section is laid out in such a way that text in italics are conclusions drawn in the report, the non-italics text are Exergy’s comments and analysis.

Analysis - Office Tenancies Government Owned

Tasmanian office tenancies are the most energy intensive in Australia for both capital cities and regions, with their respective energy intensity exceeding the national average by 39% and 53%.

Minor statistical confidence in mean difference for TAS vs. other capital city office tenancies Strong statistical confidence in mean difference for TAS vs. other regional office tenancies P(T<=t) two-tail value for TAS vs. Other States (Capital City): 0.036132 P(T<=t) two-tail value for TAS vs. Other States (Regional): 0.004965 P(T<=t) one –tail value for TAS vs. Other States (Regional): 0.002464

Appendix E – Statistical Analysis

Page 63 of 95

NT office tenancies have the lowest energy intensity for capital cities. No statistical confidence in mean difference. P(T<=t) two-tail value: 0.3582

Regional SA office tenancies have the lowest energy intensity. No statistical confidence in mean difference P(T<=t) two-tail value: 0.3830

Difference between average energy intensity for capital cities and regional government-owned offices.

Minor statistical confidence in mean difference. P(T<=t) two-tail values: 0.2846 Privately Owned

Capital city average EUI of government owned office tenancies is marginally lower than privately owned office tenancies.

No statistical confidence in mean difference. P(T<=t) two-tail value: 0.3525

The energy intensity of regional government owned office tenancies is significantly lower than privately owned office tenancies.

Strong statistical confidence in mean difference and that regional government office tenancies have a lower energy intensity than private office tenancies. P(T<=t) two-tail value: 2.91E-06 P(T<=t) one-tail value: 1.45E-06

Table AE.29 - Two-tail T-Test results for Regional offices (Tenancy) – Government owned and privately owned

Gov-Owned Regional Office(Tenancy)

Privately Owned Regional Office(Tenancy)

Mean 676.0584 371.9255

Variance 382001.7 106328.2

Observations 108 331

Hypothesized Mean Difference 0

Df 127

t Stat 4.89628

P(T<=t) one-tail 1.45E-06

t Critical one-tail 1.65694

P(T<=t) two-tail 2.91E-06

t Critical two-tail 1.97882

It could not be concluded definitively that government owned offices are less energy intensive than privately owned offices.

Strong statistical confidence in mean difference and that government offices are less energy intensive than privately owned offices. P(T<t) two-tail value: 0.002356 P(T<t) one-tail value: 0.001328

Appendix E – Statistical Analysis

Page 64 of 95

SA has the lowest average energy intensity for capital city privately owned office tenancies.

Strong statistical confidence in mean difference and that SA has lowest average energy intensity within the capital city private office tenancies category. P(T<t) two-tail value: 2.75E-13 P(T<t) one-tail value: 1.375E-13

NSW has the highest average energy intensity for capital city privately owned office tenancies.

Minor statistical confidence in mean difference and that NSW has the highest average energy intensity within the capital city private office tenancies category. P(T<t) two-tail value: 0.03356

NT has the lowest average energy intensity for regional privately owned office tenancies.

No statistical confidence in mean difference. P(T<t) two-tail value: 0.1199

TAS has the highest average energy intensity for regional privately owned office tenancies.

Strong statistical confidence in mean difference and that TAS has the highest average energy intensity within the regional private office tenancies category. P(T<t) two-tail value: 5.4944E-05 P(T<t) one-tail value: 2.7472E-05 Table AE.30 - Two-tail T-TEST results for privately owned regional office tenancies - tasmania and the rest of australia

TAS Private Regional Office(Tenancy)

Rest of Australia Private Regional Office(Tenancy)

Mean 835.5322 427.3242

Variance 562576.3204 14906.5301

Observations 65 42

Hypothesized Mean Difference 0

df 69

t Stat 4.3005

P(T<=t) one-tail 2.74722E-05

t Critical one-tail 1.6672

P(T<=t) two-tail 5.49443E-05

t Critical two-tail 1.9949

The high Tasmania average is influencing the national average. Strong statistical confidence in mean difference and that TAS regional private office tenancies have a higher average energy intensity. P(T<t) two-tail value for TAS vs. All States: 5.67E-05 P(T<t) two-tail value for TAS Cap City vs. All States: 0.732046 P(T<t) two-tail value for TAS Regional vs. All States: 7.42E-06 P(T<t) one-tail value for TAS Regional vs. All States: 3.7084E-06

Appendix E – Statistical Analysis

Page 65 of 95

Recommendation: To confidently determine (statistically) if climate is affecting TAS energy, the relationship between Cooling Degree Days (CDD) data and MJ/m2 should be examined for different states. Based on Exergy’s experience, the energy consumption difference between the two extreme climates (hottest and coolest) should be no more than 6-8%11. It is not possible to confidently infer that weather is the driver of the difference in EUI without further analysis. Tasmania also has a different fuel mix compared to the rest of the country that should suggest contradictory results (lower EUI due to higher coefficient of performance for electric heating compared to gas heating). This result warrants further analysis and research in order to understand better the causes for Tasmania’s higher average energy intensity.

Analysis - Office Base Building Privately Owned

Of those four states (NSW, Victoria, QLD and WA), QLD has the highest average energy intensity for privately owned capital city base building offices.

No statistical confidence in mean difference. P(T<t) two-tail value for QLD vs. NSW, VIC and WA: 0.05113 (no confidence)

NT has the highest average energy intensity vs. all states’ capital city base building offices.

No statistical confidence in mean difference. P(T<t) two-tail value: 0.6567

National average energy intensity of regional offices is greater than it is for capital cities.

No statistical confidence in mean difference. P(T<t) two-tail value: 0.3105

Difference between QLD regional and capital city buildings average energy intensities. Strong statistical confidence in mean difference. P(T<t) two-tail value: 2.41E-10 P(T<t) one-tail value: 1.2E-10 Table AE.31 - T-TEST results for Queensland privately-owned Offices (Base Building) - Regional and capital city

QLD Private Regional Office(Base Building)

QLD Private Capital City Office(Base Building)

Mean 582.7338 334.8321

Variance 67357.07 8192.52

Observations 137 16

Hypothesized Mean Difference 0

df 52

t Stat 7.824914

P(T<=t) one-tail 1.2E-10

t Critical one-tail 1.674689

P(T<=t) two-tail 2.41E-10

t Critical two-tail 2.006647

11 Valid for Adelaide to Brisbane comparison. Not necessarily for Hobart to Darwin comparison.

Appendix E – Statistical Analysis

Page 66 of 95

Comparison between Government Owned Buildings and Privately Owned Office Buildings There is no statistical confidence in mean difference between the average energy intensity of government owned buildings and privately owned buildings across Australia and across all office building types. Neither is there any statistical confidence that government offices have a lower average energy intensity compared to private offices. The T-test results are shown below. Table AE.32 - T-TEST results for all office buildings (government-owned and privately owned)

Government Owned Privately Owned

Mean 465.0562 500.4138

Variance 142261.4 204402.1

Observations 1313 487

Hypothesized Mean Difference 0

df 751

t Stat -1.53865

P(T<=t) one-tail 0.062156

t Critical one-tail 1.646885

P(T<=t) two-tail 0.124312

t Critical two-tail 1.963128

AE.3.2 Hotels

Data Distribution The hotels energy sample data covers over 40,000 guest rooms in a total of 195 hotels across Australia. The distribution of hotels according to hotel size is presented graphically in AE.10 and AE.11. Figure AE.12 shows that most hotel energy data was derived between financial years 2005 and 2011, with almost 40% of the data set from the financial year 2010.

Appendix E – Statistical Analysis

Page 67 of 95

Figure AE.10 - Distribution of hotels sample according to number of guest rooms

0%

5%

10%

15%

20%

25%40 80 12

0

160

200

240

280

320

360

400

Mor

e

Perc

enta

ge F

requ

necy

(Num

ber o

f Bui

ldin

gs/T

otal

Sa

mpl

e Si

ze)

Number of Guest Rooms

Appendix E – Statistical Analysis

Page 68 of 95

Figure AE.11 - Distribution of hotel sizes according to gross floor area

Figure AE.12 - Distribution of sample data by financial years

The EUI distribution for the hotels sample is presented in figure AE.13. It can be seen that average energy intensity is between 800-1200MJ/m2 per year.

0%

5%

10%

15%

20%

25%

30%

50

00

10

00

0

15

00

0

20

00

0

25

00

0

30

00

0

35

00

0

40

00

0

45

00

0

Mo

re

Fre

qu

en

cy (

No

. of

Bu

ildin

gs)

Area Bin (m2)

0

10

20

30

40

50

60

70

80

90

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

No

. of

Bu

ildin

gs

Appendix E – Statistical Analysis

Page 69 of 95

Figure AE.13 - Histogram of energy intensity for hotels

Error Bars Error bars were added to each time-series plot assuming a 95% confidence level. The error bars and R2 values for hotels energy intensity between financial year 2005 and 2011 reveal the following:

The standard error is within 11% to 72% of the mean, suggesting a huge uncertainty in data quality. The financial years with large errors (>50%) are FY2005, FY2008 and FY2009. When BID4684 (8772MJ/m2) is excluded from the FY2005 dataset, the standard error for FY2005 decreases to a more acceptable level of 18% within the mean.

There is a low correlation between financial years and average EUI

Therefore no time-series trends can be predicted using these data.

0

10

20

30

40

50

60

70

20

0

40

0

80

0

12

00

16

00

20

00

24

00

28

00

32

00

36

00

40

00

44

00

Mo

re

Fre

qu

en

cy (

Nu

mb

er

of

Bu

ildin

gs)

EUI Bins (MJ/m2)

Appendix E – Statistical Analysis

Page 70 of 95

Figure AE.14 - Hotels-Time-series average energy intensity

Statistical Significance of Conclusions Drawn for Hotels

Introduction The criteria used in our analysis are as below:

P(T<=t) >= 0.05 : no statistical confidence in mean difference

0.01 <= P(T<=t) < 0.05 : minor statistical confidence in mean difference

P(T<=t) < 0.01 : strong statistical confidence in mean difference. The following section is laid out in such a way that text in italics are conclusions drawn in the report, the non-italics text are Exergy’s comments and analysis.

Analysis The capital city and regional national averages are very similar. No statistical confidence in mean difference.12 P(T<t) two-tail value: 0.3164

QLD has the lowest energy intensity for capital cities hotels. Minor statistical confidence in mean difference and that QLD has the lowest average energy intensity within the capital city hotels category. P(T<t) two-tail value: 0.02743 P(T<t) one-tail value: 0.01379

QLD and WA show slightly lower average energy intensities than the remaining states. Strong statistical confidence in mean difference between QLD and WA capital city hotels vs. the rest of Australian capital city hotels (Table AE).

12 Therefore, there is high statistical confidence that the statement is true.

R² = 0.0606

0

500

1,000

1,500

2,000

2,500

3,000

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

Ave

rage

En

erg

y In

ten

sity

n>=8 (per year)

Linear (n>=8 (per year))

Appendix E – Statistical Analysis

Page 71 of 95

However, note that when a T-test (capital cities only) is conducted individually for QLD vs. other states and WA vs. other states, the individual results show only minor statistical confidence in mean differences between the two individual states against the rest of Australia capital city hotels. Table AE.33 - T-TEST results for capital city hotels - QLD & WA vs. other Australian states

Capital City Regional

Mean 1037.422 1345.548

Variance 187472.3 326067.7

Observations 27 123

Hypothesized Mean Difference 0

df 48

t Stat -3.14571

P(T<=t) one-tail 0.001422

t Critical one-tail 1.677224

P(T<=t) two-tail 0.002843

t Critical two-tail 2.010635

AE.3.3 Retail

Data Distribution

Shopping Centre (Tenancy) The data distribution for shopping centre tenancies according to gross lettable area (GLA) and average energy intensity is presented graphically in figures AE15 and AE16 below. Most shopping centre tenancies have a GLA between 50m2 to 150m2 and average energy intensity between 50MJ/m2 to 100 MJ/m2.

Figure AE.15 - Shopping centre (tenancy) data distribution according to GLA

0%

5%

10%

15%

20%

25%

30%

35%

50

10

0

15

0

20

0

25

0

30

0

35

0

40

0

45

0

50

0

55

0

60

0

65

0

70

0

75

0

80

0

85

0

90

0

95

0

10

00

Mo

re

Fre

qu

en

cy (

No

. of

en

trie

s)

GLA Bin (m2)

Appendix E – Statistical Analysis

Page 72 of 95

Figure AE.16 - Shopping centre (tenancy) data distribution according to average energy intensity

Shopping Centre (Base Building) The data distribution for shopping centre (base building) according to GLA and average energy intensity is illustrated in Figure AE.17 and AE.18. The histograms reveal that the majority of shopping centres (base building) have GLA clustered between 10,000 to 30,000m2. The average energy intensity for most shopping centres (base building) is 400MJ/m2.

Figure AE.17 - Shopping Centre (Base Building) Data Distribution according to Gross Lettable Area (GLA)

0%

5%

10%

15%

20%

25%

30%

35%

50

10

0

15

0

20

0

25

0

30

0

35

0

40

0

45

0

50

0

55

0

60

0

Mo

re

Fre

qu

en

cy (

No

. of

en

trie

s)

EUI Bin (MJ/m2)

0%

5%

10%

15%

20%

25%

30%

10

,00

0

20

,00

0

30

,00

0

40

,00

0

50

,00

0

60

,00

0

70

,00

0

80

,00

0

90

,00

0

10

0,0

00

11

0,0

00

12

0,0

00

13

0,0

00

Fre

qu

en

cy (

No

. of

en

trie

s)

GLA Bin (m2)

Appendix E – Statistical Analysis

Page 73 of 95

Figure AE.18 - Shopping Centre (Base Building) Data Distribution According to average energy intensity

Supermarket (Tenancy) Data was also collected for supermarkets (tenancy). The GLA for tenancy supermarkets are mostly between 2,000 to 3,000m2. As expected, the histogram for average energy intensity of supermarket (tenancy) reveals that the average energy intensity for supermarkets are extremely high compared to a standard shopping centre tenancy. The majority of supermarket (tenancy) has an average energy intensity of between 2,000MJ/m2 to 4,000MJ/m2.

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

200 400 600 800 1000 1200 1400 1600 More

Fre

qu

en

cy (

No

. of

en

trie

s)

EUI Bin (MJ/m2)

Appendix E – Statistical Analysis

Page 74 of 95

Figure AE.2 - Supermarket (tenancy) data distribution according to gross lettable area (gla)

Figure AE.3 - Supermarket (tenancy) data distribution according to average energy intensity

Error Bars Error bars were added to the shopping centres (base building) time-series plot assuming a 95% confidence level. The error bars and R2 values reveal the following:

0%

10%

20%

30%

40%

50%

60%

70%

1000 2000 3000 4000 5000 6000

Fre

qu

en

cy (

No

. of

Dat

a En

trie

s)

GLA Bin (m2)

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

1000 2000 3000 4000 5000 More

Fre

qu

en

cy (

No

. of

Dat

a En

trie

s)

EUI Bin (MJ/m2)

Appendix E – Statistical Analysis

Page 75 of 95

The standard error for shopping centres (base building) varies between 17% to 62% of the mean, representing a large uncertainty in the calculated mean value

There is a low correlation between years and average EUI. Therefore we have low confidence in any time-series trend can be predicted using these averages.

Figure AE.4 - Shopping Centres (Base Building) Time-Series Average Energy Intensity

Statistical Significance of Conclusions Drawn for Retail Buildings

Introduction The statistical difference between two data sets are determined by P(T<=t) values in T-tests. The smaller the P value, the greater the statistical likelihood of genuine difference. The criteria used in our analysis are as below:

P(T<=t) >= 0.05 : no statistical confidence in mean difference

0.01 <= P(T<=t) < 0.05 : minor statistical confidence in mean difference

P(T<=t) < 0.01 : strong statistical confidence in mean difference.

It is important to note that the boundaries between base building and tenancy are not firm for retail buildings and may have non-homogenous regional variations. Therefore, observed differences between states may be traceable to how the energy is measured, as opposed to actual energy usage. The following section is laid out in such a way that text in italics are conclusions drawn in the report, the non-italics text are Exergy’s comments and analysis.

Analysis - Shopping Centre (Tenancy) Queensland regional shopping centres (tenancy) have a lower average energy intensity

compared to capital city shopping centres (tenancy).

Strong statistical confidence in mean difference and that QLD regional shopping centre tenancies have a higher average energy intensity. P(T<t) two-tail value: 5.1563E-03 P(T<T) one-tail value: 2.5782E-03

y = 11.855x - 23433 R² = 0.0723

0

100

200

300

400

500

600

700

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

n>=13

Linear (n>=13)

Appendix E – Statistical Analysis

Page 76 of 95

Table AE.34 - T-Test Results for Queensland Shopping Centres (Tenancy) – Capital City vs. Regional

QLD Capital City QLD Regional

Mean 1719.805156 1286.076969

Variance 7862675.926 2699474.291

Observations 517 310

Hypothesized Mean Difference 0

df 825

t Stat 2.804601002

P(T<=t) one-tail 0.002578152

t Critical one-tail 1.646702708

P(T<=t) two-tail 0.005156305

t Critical two-tail 1.962843616

Comparison between Queensland shopping centre (tenancy) average EUI and the rest of Australia shopping centre (tenancy) average EUI.

No statistical confidence in mean difference P(T<t) two-tail value QLD capital city vs. rest of Australia capital city comparisons: 0.5763 Minor statistical confidence in mean difference P(T<t) two-tail value QLD regional vs. rest of Australia regional comparisons: 0.0173 Therefore, conclusions cannot be drawn using the current dataset and sample size.

Analysis - Supermarket (Tenancy) The national average energy intensity of regional retail tenancies (Supermarkets) is 10%

higher than the capital city national average.

Strong statistical confidence in mean difference and that regional supermarket tenancies have a higher average energy intensity than capital city supermarket tenancies. P(T<t) two-tail value: 1.387E-03 P(T<t) one-tail value: 6.933E-04 Table AE.35

Capital City Regional

Mean 3220.619156 3573.497979

Variance 623347.8927 759082.8339

Observations 144 101

Hypothesized Mean Difference 0

df 202

t Stat -3.242407996

P(T<=t) one-tail 0.0006933

t Critical one-tail 1.652431964

P(T<=t) two-tail 0.001386599

t Critical two-tail 1.971777385

The results show that the warmer states (NT, QLD and WA) have the highest average energy intensity for both capital cities and regions.

Appendix E – Statistical Analysis

Page 77 of 95

Strong statistical confidence in mean difference and that NT, QLD and WA have the highest average energy intensity for both capital cities and regions. P(T<t) two-tail value for regional QLD & WA vs. other regions: 2.6133E-09 P(T<t) one-tail value for regional QLD & WA vs. other regions: 1.3066E-09 P(T<t) two-tail value for capital city QLD, NT & WA vs. other capital cities: 1.771E-08 P(T<t) one-tail value for capital city QLD, NT & WA vs. other capital cities: 8.893E-09

Table AE.36 - T-Test Results for regional supermarkets (Tenancy) - QLD & WA vs. Other Australian Regions

QLD & WA Regional Other Regional

Mean 3949.160216 3023.748363

Variance 704102.7997 337692.0239

Observations 60 41

Hypothesized Mean Difference 0

df 99

t Stat 6.548327558

P(T<=t) one-tail 1.30664E-09

t Critical one-tail 1.660391156

P(T<=t) two-tail 2.61328E-09

t Critical two-tail 1.984216952

Appendix E – Statistical Analysis

Page 78 of 95

Table AE.37 - T-test results for capital city supermarkets (Tenancy) - QLD, NT & WA vs. other Australian Cities

QLD, NT & WA Capital City

Other Capital Cities

Mean 3690.87287 2943.570845

Variance 739407.3539 279783.8626

Observations 63 95

Hypothesized Mean Difference 0

df 93

t Stat 6.167479583

P(T<=t) one-tail 8.8932E-09

t Critical one-tail 1.661403674

P(T<=t) two-tail 1.77864E-08

t Critical two-tail 1.985801814

Retail tenancies (supermarkets) in the ACT, Victoria and Tasmania which have cool-temperate climates, have energy intensities which are lower than the national averages.

Strong statistical confidence in mean difference and that VIC, ACT and TAS have lower average energy intensities within the respective capital city and regional supermarket tenancies categories. P(T<t) two-tail value for capital city VIC & ACT vs. other capital cities: 2.3851E-15 P(T<t) one-tail value for capital city VIC & ACT vs. other capital cities: 1.19257E-15 P(T<t) two-tail value for regional VIC & TAS vs. other capital cities: 3.8243E-09 P(T<t) one-tail value for regional VIC & TAS vs. other capital cities: 1.91218E-09

Table AE.38 - T-Test Results for Capital city supermarkets (Tenancy) – VIC & ACT vs. other Australian cities

VIC & ACT Capital City Other Capital Cities

Mean 2619.580153 3436.085968

Variance 90722.29299 639366.2274

Observations 38 106

Hypothesized Mean Difference 0

df 142

t Stat -8.89865234

P(T<=t) one-tail 1.19257E-15

t Critical one-tail 1.655655173

P(T<=t) two-tail 2.38513E-15

t Critical two-tail 1.976810994

Appendix E – Statistical Analysis

Page 79 of 95

Table 1 - T-Test Results for regional supermarkets (Tenancy) – VIC & TAS vs. other Australian Regions

VIC & TAS Regional Other Regions

Mean 2716.114709 3711.4677

Variance 133354.7464 723570.7636

Observations 14 87

Hypothesized Mean Difference 0

df 41

t Stat -7.4516489

P(T<=t) one-tail 1.91218E-09

t Critical one-tail 1.682878002

P(T<=t) two-tail 3.82436E-09

t Critical two-tail 2.01954097

Analysis - Shopping Centre - Tenancy including 12% weighting to supermarket and Whole Building For the statistical difference between means for shopping centre tenancies (88% shopping centre tenancy + 12% supermarket tenancy) and shopping centre whole building (base building + tenancies), it is not feasible for Exergy to conduct analysis as our calculations are based on raw data. The 12% weighting has been applied on the aggregate level.

AE.3.4 Hospitals

Data Distribution Figure AE22 shows the distribution of hospital sample size according to floor area. About 35% of the hospital sample data are less than 5000m2 and 46% of the hospitals sample has floor area greater than 10,000m2. This means that only 46% of the hospitals data set is used in this benchmarking exercise.

Figure AE.22 - Hospital Sample building size distribution by floor area

0%

5%

10%

15%

20%

25%

30%

35%

40%

50

00

10

00

0

15

00

0

20

00

0

25

00

0

30

00

0

35

00

0

40

00

0

45

00

0

50

00

0

55

00

0

60

00

0

65

00

0

70

00

0

75

00

0

80

00

0

85

00

0

90

00

0

95

00

0

10

00

00

Mo

re

Fre

qu

en

cy (

No

. of

Bu

ildin

gs)

Floor Area Bin (m2)

Appendix E – Statistical Analysis

Page 80 of 95

Figure AE.23 - EUI distribution of hospitals building sample

Error Bars Error bars were added to each time-series plot assuming a 95% confidence level. The error bars and R2 values reveal the following:

The standard error of the annual average EUI ranges between 9% to 28% of the mean

The low R2 value reveals a low correlation between years and average EUI. Therefore we have low confidence in any time-series trend can be predicted using these averages.

0%

10%

20%

30%

40%

50%

60%

10

00

20

00

30

00

40

00

50

00

60

00

70

00

80

00

90

00

10

00

0

Mo

re

Fre

qu

en

cy (

No

. of

Bu

ildin

gs)

EUI Bin (MJ/m2)

Appendix E – Statistical Analysis

Page 81 of 95

Figure AE.24- Average Energy Intensity –Hospitals, Australia

Statistical Significance of Conclusions Drawn for Hospitals

Introduction The statistical difference between two data sets are determined by P(T<=t) values in T-tests. The smaller the P value, the greater the statistical likelihood of genuine difference. The criteria used in our analysis are as below:

P(T<=t) >= 0.05 : no statistical confidence in mean difference

0.01 <= P(T<=t) < 0.05 : minor statistical confidence in mean difference

P(T<=t) < 0.01 : strong statistical confidence in mean difference.

The following section is laid out in such a way that text in italics are conclusions drawn in the report, the non-italics text are Exergy’s comments and analysis. Only hospitals with a floor area greater 10,000m2 are considered in our analysis.

Analysis The national average energy intensity of capital cities hospitals is 15% lower than the

national average energy intensity of regional hospitals.

Strong statistical confidence in mean difference and that capital city hospitals have a lower average energy intensity compared to regional hospitals. P(T<t) two-tail value: 0.001531 P(T<t) one-tail value: 0.000765

y = 12.173x - 22914 R² = 0.1513

0

500

1,000

1,500

2,000

2,5001

99

9

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

n>=40/year

Total 'n': 448

Appendix E – Statistical Analysis

Page 82 of 95

Table AE.40 - T-test results for hospitals – regional and capital city

Regional Capital City

Mean 1749.566 1393.326

Variance 2987692 403725.2

Observations 322 126

Hypothesized Mean Difference 0

df 445

t Stat 3.188511

P(T<=t) one-tail 0.000765

t Critical one-tail 1.648285

P(T<=t) two-tail 0.001531

t Critical two-tail 1.965309

The average energy intensity of capital city hospitals ranges between 1,259MJ/m2.a in SA and 1,918MJ/m2.a in ACT.

Minor statistical confidence in mean difference. P(T<t) two-tail value capital city SA vs. ACT: 0.0355 P(T<t) two-tail value capital city SA vs. rest of Australia: 0.04375

The average energy intensity of regional hospitals in SA is 360% higher than the average energy intensity of regional hospitals in NSW.

No statistical confidence in mean difference. P(T<t) two-tail value: 0.27 Therefore, no conclusions can be drawn using this sample data.

AE.3.5 Schools

Data Distribution Figure AE.5 and Figure AE.6 shows the EUI distribution of the school buildings data set. According to the histograms, the EUI for schools are generally between 100MJ/m2 to 200MJ/m2.

Appendix E – Statistical Analysis

Page 83 of 95

Figure AE.5 - Schools sample distribution by floor area

Figure AE.6 - Schools EUI

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

50

0

10

00

20

00

30

00

40

00

50

00

60

00

70

00

80

00

90

00

10

00

0

Mo

re

Freq

uen

cy (

No

. of

Entr

ies)

Floor Area Bin (M2)

0

500

1000

1500

2000

2500

3000

3500

10

0

20

0

30

0

40

0

50

0

60

0

70

0

80

0

90

0

10

00

Mo

re

Freq

uen

cy (

No

. of

Entr

ies)

EUI Bin (MJ/m2)

Appendix E – Statistical Analysis

Page 84 of 95

Error Bars Error bars were added to each time-series plot assuming a 95% confidence level. The error bars and R2 values reveal the following:

The standard error ranges within 6% to 69% of the mean. FY2007, which has a standard error of 69% is skewed by several regional NT primary schools which have EUI greater than 1,000MJ/m2 (up to 7,200MJ/m2). While it is unclear if these schools are genuinely outliers, removing them from the dataset reduces the standard error to a more acceptable level of 17%. Further investigation revealed that all occurrences of average EUIs exceeding 1,000MJ/m2 are ACT and regional NT schools.

There is a low correlation between years and average EUI. Therefore we have low confidence in any time-series trend can be predicted using these averages.

Figure AE.27 - Schools Time-Series Average Energy Intensity

Statistical Significance of Conclusions Drawn for Schools

Introduction The statistical difference between two data sets are determined by P(T<=t) values in T-tests. The smaller the P value, the greater the statistical likelihood of genuine difference. The criteria used in our analysis are as below:

P(T<=t) >= 0.05 : no statistical confidence in mean difference

0.01 <= P(T<=t) < 0.05 : minor statistical confidence in mean difference

P(T<=t) < 0.01 : strong statistical confidence in mean difference. The following section is laid out in such a way that text in italics are conclusions drawn in the report, the non-italics text are the author’s comments and analysis.

y = 1.1967x - 2226.2 R² = 0.189

0

50

100

150

200

250

300

350

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

Appendix E – Statistical Analysis

Page 85 of 95

Analysis The results are heavily weighted towards the QLD average energy intensities because

of the significant sample size in that state.

Strong statistical confidence in mean difference and that QLD average energy intensity is lower compared to other states. P(T<t) two-tail value: 5.97E-68 P(T<t) one-tail value: 2.98E-68 Table AE.41 - T-test results for schools - queensland and other states

QLD Other States

Mean 162.8774 509.4641

Variance 4536.344 464822.4

Observations 4760 1323

Hypothesized Mean Difference 0

df 1329

t Stat -18.4655

P(T<=t) one-tail 2.98E-68

t Critical one-tail 1.646001

P(T<=t) two-tail 5.97E-68

t Critical two-tail 1.961751

In fact, all three data sets – Queensland, ACT and Northern Territory’s depict high statistical confidence that the means are different.

Strong statistical confidence in mean difference. The t-test between the ACT and NT samples resulted in a P(T<t) two-tail value of 9.18E-07. This result is supported by the fact that:

1. Queensland – cooling-dominated

2. ACT – heating dominated

3. Northern Territory – not comparable demographically or climate-wise to major capital cities

4. All three states are under very different governing bodies and policies. Recommendation: QLD, ACT and NT are not representative of the general school population in Australia due to the extremities. The sample also has no representation the major cities in NSW, VIC and WA. Should forecasting or national benchmarks be required, we recommend that at least NSW schools data is collected as the climate there is more neutral and also demographically and geographically more representative of the typical Australian school. Ideally, schools data from each state is collected.

Appendix E – Statistical Analysis

Page 86 of 95

AE.3.6 University Buildings

Data Distribution Histograms describing the spread of the sample data according to floor area and EUI are presented in

Figure AE.7 and Figure AE..

Figure AE.7 - Floor area distribution for university buildings sample

Figure AE.29 - EUI distribution for university buildings sample

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

20

00

40

00

60

00

80

00

10

00

0

12

00

0

14

00

0

16

00

0

18

00

0

20

00

0

22

00

0

24

00

0

26

00

0

28

00

0

30

00

0

32

00

0

34

00

0

36

00

0

38

00

0

40

00

0

Mo

re

Freq

uen

cy (

No

. of

Entr

ies)

Floor Area Bin

0%

5%

10%

15%

20%

25%

30%

20

0

40

0

60

0

80

0

10

00

12

00

14

00

16

00

18

00

20

00

22

00

24

00

26

00

Mo

re

Freq

uen

cy (

No

. of

Entr

ies)

EUI Bin

Appendix E – Statistical Analysis

Page 87 of 95

Error Bars Error bars were added to each time-series plot assuming a 95% confidence level. The error bars and R2 values reveal the following:

The standard error is massive for this dataset, ranging from 21% to 66% of the mean. This is probably because all university facilities are being compared within the same category, i.e. research laboratories and computer centres are being treated the same way as normal teaching buildings, resulting in a large sample variance.

There is a low correlation between years and average EUI. Therefore we have low confidence in any time-series trend can be predicted using these averages.

Figure AE.30 – University Buildings - Time-Series Average Energy Intensity

Statistical Significance of Conclusions Drawn for University Buildings The statistical difference between two data sets are determined by P(T<=t) values in T-tests. The smaller the P value, the greater the statistical likelihood of genuine mean difference. The criteria used in our analysis are as below:

P(T<=t) >= 0.05 : no statistical confidence in mean difference

0.01 <= P(T<=t) < 0.05 : minor statistical confidence in mean difference

P(T<=t) < 0.01 : strong statistical confidence in mean difference. Our analysis excludes data points with 0MJ/m2 EUI. We also note that the university buildings sample is non-homogenous as the sample includes whole university campuses, TAFE campuses, laboratories and individual university buildings that are all being treated equally. This means that this sample data might generate misleading conclusions, which is unlikely to be clearly reflected in the statistical analysis conducted here.

Comparison of average energy intensities for capital cities university buildings and regional university buildings.

No statistical confidence in mean difference. P(T<t) two-tail value: 0.6135 Also note that the simple average obtained is quite different – 885 MJ/m2 for capital cities (864 MJ/m2 in DCCEE 600/2010 report) and 926MJ/m2 for regional (418 MJ/m2 in DCCEE 600/2010 report).

y = 8.5494x - 16437 R² = 0.0567

0

200

400

600

800

1,000

1,200

1,400

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

n>=49

Linear (n>=49)

Appendix E – Statistical Analysis

Page 88 of 95

Average EUI for VIC capital city university buildings and the rest of Australian capital city university buildings.

No statistical confidence in mean difference. P(T<t) two-tail value: 0.0824

ACT university buildings have a higher EUI compared to the general Australian capital city university buildings.

Strong statistical confidence in mean difference and that ACT university buildings have a higher EUI compared to the other Australian capital city university buildings. P(T<t) two-tail value: 0.005679 P(T<t) one-tail value: 0.002839

ACT Other States

Mean 1265.21 846.5607

Variance 216890.5 1503620

Observations 14 743

Hypothesized Mean Difference 0

df 17

t Stat 3.163256

P(T<=t) one-tail 0.002839

t Critical one-tail 1.739607

P(T<=t) two-tail 0.005679

t Critical two-tail 2.109816

Mean difference between SA capital city energy intensity and SA regional energy intensity. SA capital cities have a much higher energy intensity compared to regional universities within the same state.

Strong statistical confidence in mean difference and that SA capital city university buildings have a higher average energy intensity compared to its regional counterparts. P(T<t) two-tail value: 1.09E-08 P(T<t) one-tail value: 5.47E-09

Appendix E – Statistical Analysis

Page 89 of 95

SA Capital City SA Regional

Mean 1412.448 187.983

Variance 4270324 22027.16

Observations 110 43

Hypothesized Mean Difference 0

df 112

t Stat 6.173989

P(T<=t) one-tail 5.47E-09

t Critical one-tail 1.658573

P(T<=t) two-tail 1.09E-08

t Critical two-tail 1.981372

Difference between TAS capital city universities and TAS regional universities.

Minor statistical confidence in mean difference. P(T<t) two-tail value: 0.019502

AE.3.7 Public Buildings

Data Distribution The figures below graphically presents the distribution of energy data collected according to financial year, state, building type and energy intensity. The energy intensity versus floor area scatter plot also shows possible outliers in Figure AE.. Note that the sample data is very non-homogenous from year to year. The sample data from FY1998 to FY2004 are dominated first by the ACT, and subsequently by SA, meaning that any conclusions drawn from that period might not be representative of the national population set.

Figure AE.31 - data distribution for public building by region and financial year

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FY19

98

FY19

99

FY20

00

FY20

01

FY20

02

FY20

03

FY20

04

FY20

05

FY20

06

FY20

07

FY20

08

FY20

09

FY20

10

FY20

11

Pe

rce

nta

ge F

req

ue

ncy

NT

TAS

SA

NSW

ACT

Appendix E – Statistical Analysis

Page 90 of 95

Figure AE.8 - Data distribution for public buildings according to financial year and state

Figure AE.32 - data distribution according to public building type

Figure AE.33 - EUI distribution of public buildings

0%

10%

20%

30%

40%

50%

60%

70%

80%

Library Public museum/ artgallery

Unsure/ Other

Pe

rce

nta

ge F

req

ue

ncy

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

10

0

20

0

30

0

40

0

50

0

60

0

70

0

80

0

90

0

10

00

11

00

12

00

13

00

14

00

15

00

16

00

Mo

re

Freq

uen

cy

EUI

Appendix E – Statistical Analysis

Page 91 of 95

Figure AE.34 - Energy intensity vs. floor area for public buildings

Error Bars Error bars were added to each time-series plot assuming a 95% confidence level. The error bars and R2 values reveal the following:

The standard error is large, varying between 22% to 62% of the mean

The R2 value is quite high, suggesting a strong downward linear trend in terms of energy intensity (Figure AE.35). However, the large error bars also suggest that the uncertainty is quite large and any trend observed has a low statistical confidence due to the non-homogenous nature of the sample data. In fact, if we remove FY1999 to FY2004, which comprise of only ACT and SA samples, the sample data for FY2005 to FY2011 show a very low correlation (Figure AE.36).

Given that the standard errors are so large, we have low confidence in any time-series trend that is predicted using these averages.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0 20 40 60 80 100 120 140 160

Ener

gy In

ten

sity

(M

J/m

2)

Floor Area (m2)

Thousands

Appendix E – Statistical Analysis

Page 92 of 95

Figure AE.35 - Annual Average energy intensity and trending for public buildings (FY2001 to FY2010)

Figure AE.36 - Annual Average Energy Intensity and Trending for Public Buildings (excluding FY2001-FY2004)

R² = 0.623

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,0001

99

9

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

Total n value, all periods: 227

R² = 0.0606

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

Total n value, all periods: 227

Appendix E – Statistical Analysis

Page 93 of 95

AE.3.8 Law Courts

Data Distribution Figure AE. and Figure AE. are histograms showing the spread of the data according to energy intensity and floor area. The histograms show that most of the law courts in this sample data have a floor area less than 500m2.

Figure AE.37 - Histogram showing energy intensity and frequency of occurrence (law courts)

Figure AE.38 - histogram showing floor area and frequency of occurrence (law courts)

Error Bars Error bars were added to each time-series plot assuming a 95% confidence level. The error bars and R2 values reveal the following:

The standard error varies within 8% to 86% of the mean. The large error bars suggest that the standard error is quite large and any trend observed using the current dataset has a low statistical confidence.

The R2 value is low suggesting a weak correlation between financial year and average energy intensity.

0%

5%

10%

15%

20%

25%

30%

200 400 600 800 1000 More

Per

cen

tage

Fre

qu

ency

Energy Intensity

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

500 1000 1500 2000 2500 3000 More

Pre

cen

tage

Fre

qu

ency

Floor Area (m2)

Appendix E – Statistical Analysis

Page 94 of 95

Therefore we have low confidence in any time-series trend that is predicted using these averages.

Figure AE.39 - Annual Energy Intensity and Trending for Law Courts

AE.4 Glossary of Terms Confidence Level: Using sample data to make conclusions and estimates about the population is not always going to be correct. For this reason, we build into the statistical inference a measure of reliability. The confidence level is the proportion of times that an estimating procedure will be correct. In this project, we estimated the minimum sample size required in order to ensure that estimates based on this energy data will be correct 95% of the time. Finite population: Where the population is not infinitely large. Generally, if the sample size is greater than 1% of the population, the population is assumed to be finite. In this project, smaller states such as Northern Territory have a very small number of buildings. In the calculations for the minimum number of buildings required, assuming an infinite population means that the minimum number of buildings required to achieve the prescribed confidence level and accuracy will exceed the actual number of buildings available in the Northern Territory. In such a situation, the population is considered finite and the associated calculations will assume finite population size. Infinite population: Where the population is assumed to be infinitely large. Generally, if the sample size is less than 1% of the population, the population is assumed to be infinite. Mean: In computing numerical descriptive measures of the data, interest usually focuses on two measures: (1) a measure of the central, or average, value of the data and (2) a measure of the degree to which the observations are spread out about this average value. The mean measures the central location of the data, also expressed as the ‘average’ in this project. Population: A population is the set of all items of interest in a statistical problem. For example, the population referred to in this project will be the actual number of buildings within a prescribed category, e.g. actual number of government owned office buildings in NSW. Regression: Regression is used to predict the value of one variable on the basis of other variables. The coefficient of determination, denoted R2, measures the strength of the linear relationship between two variables. In this project, R2 is often predominantly used to describe the linear relationship between financial years and average EUI. The higher the value of R2, the better the model fits the data.

R² = 0.2853

0

100

200

300

400

500

600

700

800

900

1,000

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

n>=6

Linear (n>=6)

Appendix E – Statistical Analysis

Page 95 of 95

Sample: A sample is a set of data drawn from the population. In this project, the sample data is the energy data collating for each category. A descriptive measure of a sample is called a statistic. We use statistics to make inferences about the population (e.g. use the proportion of commercial buildings energy data collected to make inferences about general characteristics of all commercial buildings in Australia). Standard deviation: The standard deviation is a measure of variability that is expressed in the same units as the original data/observations, as is the mean. It is merely the square root of variance, which measures the variability of a set of quantitative data. Standard Error: The standard error referred to in this report is the standard deviation of the mean. It is also referred to as ‘accuracy’ in this report. T-test: A t-test tests and estimates the difference between two population means by assuming the distribution is normal. T-tests are conducted wherever we have claimed or drawn conclusions about two means (e.g. the average EUI of capital cities buildings is higher than regional buildings) to test the difference between the two means. If the p-value of the test is small, we can conclude that there is sufficient evidence to infer that the average EUI of data set 1 is higher/lower (different) from the average EUI of data set 2. However, note that t-tests does not validate the source of the data or reliability of the data set, e.g. if certain numbers are self-reported without any clear standards or Rules to which energy use is reported, the data might not be reliable at all. Z-score: Z-score is also called a standard score. It has the effect of transforming the original distribution to one in which the mean becomes zero and the standard deviation becomes 1. A negative Z-score means that the original observation was below the mean. A positive Z-score means that the original observation was above the mean. The actual value corresponds to the number of standard deviations the observation is from the mean in that direction (positive or negative. The z-score is dependent on the confidence level and standard error. In Equation 1, we have assumed a confidence level of 90% and a standard error of ±10% of the mean.