Date post: | 10-Jan-2017 |
Category: |
Engineering |
Upload: | smart-infrastructure-facility |
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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?