Energy Epidemiology in the Existing Australia Housing Stock

Daniel DalyAssociate Research Fellow

Sustainable Buildings Research Centre

The 30 Sec. Pitch

1. Create an empirical, robust, geo-located database of relevant building and energy data for the existing and

future building stock with minimal data gaps. 2. Develop powerful, spatially explicit and user-friendly

Housing Stock Mapping visualisation and analysis tools to access this information

Epidemiology:the study of health and disease conditions in a population.

Energy Epidemiology:The study of energy use in a population

The 30 Sec. Pitch

1. Create an empirical, robust, geo-located database of relevant building and energy data for the existing and

future building stock with minimal data gaps. 2. Develop powerful, spatially explicit and user-friendly

Housing Stock Mapping visualisation and analysis tools to access this information

Background and Significance

Emissions reduction targets:• 26-28% reduction from 2005

by 2030 Australia's housing stock:

• Contribute ≈ 12% of emissions

• demolition rate ≈0.18% per annum, new stock addition ≈2% per annum

• In 2030, ≈ 75% of the housing stock will remain.

Background and Significance

Performance Gap

Energy modelling/ Forecasting errors

Rebound effect

Design Actual

Background and Significance

Currently, there is no centralised data repository to house building and energy related information

Last major survey of Australian Housing was in 1986 (ABS National Energy Survey)

There is data related to the housing stock, but it is held by disparate organisations, e.g.

• Planning (BASIX)• Rebate, audit and assessment schemes• ABS surveys and Census • Utilities information• Research: sample interventions, surveys, etc…)• Related demographic data (census, etc…)

We don’t know what we know!

Background and Significance

Innovation

Development of a Housing Stock Database is catch-up research:• UK have English Housing Survey • US have Residential Energy Consumption Survey• EU have Energy Performance Certificate Database

Energy Epidemiology is an emerging field, with great opportunity for innovation:• Energy Epidemiology is the analyses of real building energy use

(and relevant contextual information) at scale. • RCUK Centre for Energy Epidemiology• IEA Annex 70: Energy Epidemiology

Limitations

Data Availability and Accessibility Data Granularity Data Coverage Data Definitions Data Reliability and Quality

LimitationsType

Parameters Coverage

Dwelling Specific Dwelling structure BASIX, HPSP, ABS, AURINFloor area (m2) OR Number of Bedrooms BASIX, INS OR AURIN, HPSP, BASIX

Insulation location OR Added/Total R-Value INSFloor construction detail BASIXRoof construction detail BASIXAge/Construction period BASIX, NEXISWall construction type BASIX

Orientation and size of main glazing BASIXExposure of fabric NoneNumber of storeys BASIX

System Specific Heater type BASIX, INS, HPSP SuppCooler type BASIX, INS, HPSP Supp

Is the space conditioned? AURIN, BASIXHot water system type HPSP, HWS, BASIX

Solar PV system output (OR angle, size and type) SBSOther

Property Address AURIN, HPSP, HWS, TLT, WMR, BASIX, INS

Historical records of electricity consumption End En (SA1 Level)

Number of residents HPSP, HWS, TLT, TLT (SW), WMR, WMR (SW), INS

Historical records of Water consumption NoneHistorical records of gas consumption None

Limitations

Data Availability and Accessibility Data Granularity Data Coverage Data Definitions Data Reliability and Quality

The 30 Sec. Pitch

1. Create an empirical, robust, geo-located database of relevant building and energy data.

2. Develop visualisation and analysis tools to access this information

Pitch version II - Specifics

Continue sourcing and compiling data into centralised, fused database (HSM Phase II)

Expand database to include relevant non-building/energy data (e.g. demographics)

Establish common data collection, definition, and storage standards to capture new data.

Identify key data gaps, and develop data sourcing or sampling methodologies to source data (Census, targeted field surveys)

Develop discrete inference and projection layer in database (Innovation)

Nominal Questions

Who do you see as the key stakeholders in this work, and who are the key end-users?

How do we get diverse stakeholders to agree to a common data definition, format and collection strategy for fundamental housing characteristics (e.g. Dwelling Type, Age, etc..)?

What methods may be used to help deal with the data quality concerns?