Pitch made by Dr Daniel Daly, Associate Research Fellow, Sustainable Buildings Research Centre, University of Wollongong on Day 2 of ISNGI 2016.

1. Energy Epidemiology in the
Existing Australia Housing
Stock
Daniel Daly
Associate Research Fellow
Sustainable Buildings Research Centre

2. 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

3. 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

4. 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.

5. Background and
Significance
• Performance Gap
• Energy modelling/
Forecasting errors
• Rebound effect
Design Actual

6. 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!

7. Background and
Significance

8. 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

9. Limitations
• Data Availability and Accessibility
• Data Granularity
• Data Coverage
• Data Definitions
• Data Reliability and Quality

10. Limitations
Type
Parameters Coverage
Dwelling Specific Dwelling structure BASIX, HPSP, ABS, AURIN
Floor area (m2) OR Number of Bedrooms BASIX, INS OR AURIN, HPSP, BASIX
Insulation location OR Added/Total R-Value INS
Floor construction detail BASIX
Roof construction detail BASIX
Age/Construction period BASIX, NEXIS
Wall construction type BASIX
Orientation and size of main glazing BASIX
Exposure of fabric None
Number of storeys BASIX
System Specific Heater type BASIX, INS, HPSP Supp
Cooler type BASIX, INS, HPSP Supp
Is the space conditioned? AURIN, BASIX
Hot water system type HPSP, HWS, BASIX
Solar PV system output (OR angle, size and type) SBS
Other
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 None
Historical records of gas consumption None

11. Limitations
• Data Availability and Accessibility
• Data Granularity
• Data Coverage
• Data Definitions
• Data Reliability and Quality

12. 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

13. 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)

14. 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?