SEAI Mapping Systems Wiki

Domestic Sector Datasets

Data have been collected from different sources and analysed using benchmarks and other defaults and assumptions to estimate heat demands for different sectors: domestic, commercial, public sector and industrial.  The following section describe the data and methodology for the domestic sector.

Data sources Type of data Comments
CSO Small Area (SA) Household types and numbers at Small Area level together with fuel sources distribution Provides information on housing form and heating / fuel type at Small Area level.
Geodirectory Address points Provides exact geographic location of each address and associated Small Area at building level.
BER 577,000 BERs with address, dwelling type and BER rating Limited BER data for around 1/3 of the housing stock.Data available with Eircodes (but no conversion to location/Small Area)
“Unlocking The Energy Efficiency Opportunity” report, June 2015 Energy benchmarks by house type Provides energy benchmarks for different house types and different energy efficiency packages.Contains benchmarks for different efficiency specifications.
NRA Population Growth Estimates Estimates of population growth per county and CSO_DED level from 1986 in 5 year intervals.
SEAI Final Energy Demand statistics, 2013 Ireland consumption by fuel type for Residential sector National level consumption statistics broken down by fuel type.Based on national import data with model supporting fuel splitNot available at a disaggregated geographic level.

The Domestic heat demand has been modelled based on the Small Areas set out in the CSO Census data of 2011. This identifies a total of 18,488 Small Areas in the Republic of Ireland to allocate heat demand to.

Household types

The CSO Census data provides the total number of houses and apartments (over 1.6 million households) in each Small Area (98%, with 2% allocated to Other or Unknown types) with a distribution overall of 11% Apartments and 87% Houses. In order to estimate the heat demand of these dwellings, benchmarks of annual kWh per household were used from the “Unlocking The Energy Efficiency Opportunity” report, June 2015. This study identifies different benchmarks for different house types (detached, semi-detached and terraced) and therefore, there is a requirement to understand the specific distribution of each type of house within each Small Area out of the total number of houses provided by the CSO data set.

To estimate this spread per Small Area, the BER data has been used to provide a representative sample of different house types in each Small Area.

The BER data provides the following house types which have been in turn allocated to each of the three main benchmark categories:

Apartment Apartment
Basement Dwelling Apartment
Detached house Detached house
End of terrace house Semi-D House
Ground-floor apartment Apartment
House Semi-D House
Maisonette Apartment
Mid-floor apartment Apartment
Mid-terrace house Terraced House
Semi-detached house Semi-D House
Top-floor apartment Apartment

In order to quantify the numbers of each house type from BERs in each Small Area, the address recorded in each BER was matched against the GeoDirectory of Ireland postal addresses to identify the Small Area for each, and thus the number of benchmark house types in each Small Area. Using a number of algorithms on an Access database, out of the ~ 576,000 BERs with addresses (considered for the purpose of this study to correspond to unique buildings and not include more than one BER for a given building), over 340,000 (59%) were matched to a postal address with a 90% match score or higher. About 320,000 (94% of matches) were matched to a 100% accuracy level, with the reminder matched to a level of 90% and above. Not all addresses in the Geodirectory have a Small Area and therefore, although addresses were found for 340,000 BERs, only about 330,000 of these have a Small Area. This is the number of BERs finally used to provide approximations for distribution of house types and energy benchmarks.

The representativeness of the BER distribution of house types varies across Small Areas. In cases where no BERs were identified for a given Small Area, a BER County level distribution of house types and energy benchmarks was used as a default approximation. This was applied to 2,221 Small Areas (12%). 83% of these are identified as Urban Small Areas, with the rest being Rural or Unknown.

Small Areas with BER house type distribution at County level


For the 88% Small Areas for which at least one BER was identified:

  • 26% of them have house type distributions corresponding to BERs for less than 10% total houses (excluding apartments).
  • 53% of them have BERs identified for between 10% and 30% of total houses, and
  • 21% with BERs identified for more than 30% of total houses.

No significant differences in these levels were identified for Urban or Rural Small Areas.

Small Area with BER house type distribution at SA level


% Small Areas with BER house types’ distribution at SA level and % BERs coverage of total number of houses in each


A final distribution of household types was derived per Small Area. The aggregated country-wide distribution looks as follows:

Apartment 11%
Detached House 33%
Semi-D House 41%
Terraced House 13%

Energy consumption benchmarks and heat demands

For each Small Area, the number of BERs per Rating (A1 to G) and household type was identified, and an average, composite energy consumption benchmark was produced for each from the Initial (baseline) benchmarks identified in the “Unlocking The Energy Efficiency Opportunity” report. The benchmarks used correspond to Natural Gas heating fuel for Natural Gas consumption as follows:


INITIAL (MWh per building)
A1-B2 B3-C1 C2-C3 D1-D2 E1-E2 F G
Apartment 5 6 7 9 10 12 13
Detached House 11 14 16 19 24 27 37
Semi-D House 7 9 11 14 18 21 29
Terraced House 6 7 8 11 13 15 20

A counterfactual efficiency for heat of 85% was used to estimate annual heat demand for each household type per Small Area providing a total country-wide baseline of about 20.2 million MWh.

Energy efficiency was then introduced by re-calculating the heat demand baseline using the same methodology as above but applying the “Unlocking The Energy Efficiency Opportunity” report benchmark for Medium Installed energy efficiency package. This includes cavity wall and roof insulation, draught proofing, energy efficient lighting, energy efficient appliances and heating controls.

A1-B2 B3-C1 C2-C3 D1-D2 E1-E2 F G
Apartment 5 5 7 8 9 10 11
Detached House 11 13 15 17 20 22 27
Semi-D House 7 9 10 12 15 17 20
Terraced House 6 6 8 9 11 12 15

The annual country-wide aggregated energy efficiency heat demand of existing households is estimated to be in the region of 17.3 million MWh.

Finally, estimated heat demand for 2025 (10 year projection) was calculated by adding estimated growth of households per Small Area to the energy efficient estimated heat demand of existing buildings. The growth was calculated using population growth estimates (from 2011, year of the census data, to 2026) at CSO_DED[1] level (or county level if no Small Area identified) spread equally amongst the Small Areas for each and applied to the current volume of household types. One of the weaknesses of this approach is that, for those Small Areas with no recorded households of a specific type (e.g. no apartments), no growth of such household type is predicted. For estimating the heat demand of the growth, energy benchmarks from the “Unlocking The Energy Efficiency Opportunity” report for Medium Installed package and rating of A1-B2 are allocated to each house type:

Apartment 5
Detached House 11
Semi-D House 7
Terraced House 6

The annual country-wide aggregated 10 year projection is estimated to be around 18.4 million MWh.


To sense check the results obtained, the Energy Balance[2] figures for Residential Consumption by Fuel Type for 2013 were obtained. These national energy balance figures are themselves based on models (due to lack of metered fuel use in every house) and therefore provide a useful comparison, but may not be robust enough for “calibration” purposes.  In terms of estimated heat demand, overall results obtained from the heat map domestic model fall within less than 9% difference from those shown by the Energy Balance, with a very similar distribution per fuel type. Following this check, a decision was taken not to adjust the results obtained by the model.

Domestic data set methodology


[1] CSO_DED level is a geographical level intermediate between County and Small Area – population growth estimates were available at this level of disaggregation.