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ISNGI 2016 - Pitch: "Energy epidemiology in the existing Australia housing stock" - Dr Daniel Daly

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

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ISNGI 2016 - Pitch: "Energy epidemiology in the existing Australia housing stock" - Dr Daniel Daly

  1. 1. Energy Epidemiology in the Existing Australia Housing Stock Daniel Daly Associate Research Fellow Sustainable Buildings Research Centre
  2. 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. 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. 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. 5. Background and Significance • Performance Gap • Energy modelling/ Forecasting errors • Rebound effect Design Actual
  6. 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. 7. Background and Significance
  8. 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. 9. Limitations • Data Availability and Accessibility • Data Granularity • Data Coverage • Data Definitions • Data Reliability and Quality
  10. 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. 11. Limitations • Data Availability and Accessibility • Data Granularity • Data Coverage • Data Definitions • Data Reliability and Quality
  12. 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. 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. 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?

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