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Smart Dublin – Energy
Innovation of Cities Tour
Friday, 4th Dec 2015
Dr Gerry Wardell, Director of Codema
About Codema
 Founded in 1997 on initiative
of Dublin City Council under
SAVE II Programme
 Energy Agency for 3 Dublin
Local Authorities
Codema’s Competencies
 Team of 10 staff
ENERGY-SMART CITY?
CORE IS:
DUBLIN’S ICT INDUSTRY
CONVERGING WITH:
ABUNDANCE OF RENEWABLE ENERGY
..IN A COMPLEX BUSY CITY
OF 1 MILLION PEOPLE
NOT SIMPLE…
A SMARTER WAY OF LIVING
ENERGY-SMART CITY?
Dublin City Spatial Energy Demand Analysis
Donna Gartland – Strategic Sustainable Energy Planner, Codema
Methodology: Residential Sector
Building Energy Rating
Database
16 Dublin City Postcodes
4 Dwelling Types
7 Construction Periods
448 Data Sub-sets for Energy Use in
Dwellings
Census 2011
2202 Small Areas in Dublin City
4 Dwelling Types
7 Construction Periods
50-200 dwellings per Small Area
61,656 Dublin City Housing Data
Sub-sets
72,097 BER’s in Dublin City
202,468 Total dwellings in Dublin City
∴ 36% statistical representation
Methodology: Residential Sector
Methodology: Commercial Sector
Valuation Office
21,352 Commercial (Industrial & Services) Properties in Dublin City
Property Use Floor Area (m2) XY Coordinates
CIBSE Energy Use per m2 Floor Area per
Property Use
Mapping Software
Match to Small Area
Energy Use per Commercial Property
Methodology: Commercial Sector
Reducing waste heat:
District Heating
Results: District Heating Potential
 EU legislation requires all member states to map heating and/or
cooling demands
 New EU Heating and Cooling Strategy to be released in January
 Dublin City Heat Map reveals over 75% of small areas have a
high enough heat density to be suitable for DH
 Docklands area is ideal location for first phase development
 Already work done on DH in Docklands
 Pipes already in the ground
 Waste to Energy Plant – 90 MW waste heat
 New developments planned & will be DH enabled in SDZ
Results: District Heating Potential
Heat Density in
Docklands
SDZ >470 TJ/km2
Results: Energy Poverty Areas
Three influencing factors affecting energy poverty, as defined
by the DCENR:
 Ability to pay energy bills
 Cost of energy
 Energy efficiency of the home
Mapped areas at most at risk of energy poverty according to
 Unemployment/Unable to work (ability to pay bills)
 Energy efficiency rating of the home
 Cost of energy per household
Thank you
www.codema.ie
Tel: (01) 707 9818

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Smart Dublin Energy 04/12/2015

  • 1. Smart Dublin – Energy Innovation of Cities Tour Friday, 4th Dec 2015 Dr Gerry Wardell, Director of Codema
  • 2. About Codema  Founded in 1997 on initiative of Dublin City Council under SAVE II Programme  Energy Agency for 3 Dublin Local Authorities
  • 6. CONVERGING WITH: ABUNDANCE OF RENEWABLE ENERGY
  • 7. ..IN A COMPLEX BUSY CITY OF 1 MILLION PEOPLE NOT SIMPLE…
  • 8. A SMARTER WAY OF LIVING ENERGY-SMART CITY?
  • 9. Dublin City Spatial Energy Demand Analysis Donna Gartland – Strategic Sustainable Energy Planner, Codema
  • 10. Methodology: Residential Sector Building Energy Rating Database 16 Dublin City Postcodes 4 Dwelling Types 7 Construction Periods 448 Data Sub-sets for Energy Use in Dwellings Census 2011 2202 Small Areas in Dublin City 4 Dwelling Types 7 Construction Periods 50-200 dwellings per Small Area 61,656 Dublin City Housing Data Sub-sets
  • 11. 72,097 BER’s in Dublin City 202,468 Total dwellings in Dublin City ∴ 36% statistical representation Methodology: Residential Sector
  • 12. Methodology: Commercial Sector Valuation Office 21,352 Commercial (Industrial & Services) Properties in Dublin City Property Use Floor Area (m2) XY Coordinates CIBSE Energy Use per m2 Floor Area per Property Use Mapping Software Match to Small Area Energy Use per Commercial Property
  • 15. Results: District Heating Potential  EU legislation requires all member states to map heating and/or cooling demands  New EU Heating and Cooling Strategy to be released in January  Dublin City Heat Map reveals over 75% of small areas have a high enough heat density to be suitable for DH  Docklands area is ideal location for first phase development  Already work done on DH in Docklands  Pipes already in the ground  Waste to Energy Plant – 90 MW waste heat  New developments planned & will be DH enabled in SDZ
  • 16. Results: District Heating Potential Heat Density in Docklands SDZ >470 TJ/km2
  • 17. Results: Energy Poverty Areas Three influencing factors affecting energy poverty, as defined by the DCENR:  Ability to pay energy bills  Cost of energy  Energy efficiency of the home Mapped areas at most at risk of energy poverty according to  Unemployment/Unable to work (ability to pay bills)  Energy efficiency rating of the home  Cost of energy per household

Editor's Notes

  1. Some postcodes have higher representation than others, Dublin 1 has 60% representation of BERs. BER map shows areas with best and worst BERs, newer areas showing up with better ratings and older inner city areas showing up the worst Detail in the BERs also allows us to map things like average floor areas in each small area, see where larger housing is located And when we apply todays energy costs for fuel and electricity, we can see areas with highest annual energy spend per household, usually areas with the largest floor areas and those with high electricity usage.
  2. CIBSE has the most well established source of energy data for the commercial sector, and is widely used in Ireland for energy modelling and is used for comparative benchmarking in Display Energy Certificates by SEAI.
  3. Allows us to map the locations of all the commercial properties and colour code them according to energy demand
  4. Common practice for many other EU countries to map and make plans for heating provision.
  5. We can see from the BER map that the energy demand in new developments has been somewhat lowered by new building regulations and new technologies.
  6. Areas with high unemployment or those that are unable to work on sickness benefit were overlapped with areas with BERs of E or lower, and of those areas, which had highest costs of energy per household were identified as the areas most at risk of energy poverty. 865 households identified in top ten areas worst affected.