|Dataset||Type of data||Comments|
|Valuation Office (VO)||Floor areas by building and Small Area||List of Valuation Office Rateable properties with Use Type, and Small AreaProvides floor areas for most properties in Dublin, Limerick and Waterford. No floor areas available for other locations.|
|CIBSE TM 46 energy benchmarks||Energy demand by sector per m2||Benchmark dataset developed for UK DECs. Covers a number of different sectors (use types).|
|Geodirectory||Address points||Provides geographic location of each address and associated Small Area at building level.|
|Non Domestic BER||41,718 BERs with address, use type, heating type, floor area and BER rating||Data available with Eircodes (but no conversion to location/Small Area)|
|“Unlocking The Energy Efficiency Opportunity” report, June 2015||Energy demand benchmarks by sector for different size and building use types with energy efficiency||Provides energy demand benchmarks for public sector buildings of different use types, building sizes, and HVAC systems.Contains benchmarks for different efficiency specifications.Based on analysis of the BER dataset and survey data.Provides distribution of building sizes and performance levels per type|
|NRA||Population Growth Estimates||Estimates of population growth per county and CSO_DED level from 1986 in 5 year intervals.|
Buildings and Small Areas
A first stage of processing required VO data cleaning, excluding not applicable entries (e.g. yards, car parks,), and separating commercial from industrial entries. Re-classification of VO use types to CIBSE sectors was also produced to enable benchmarking. The Geodirectory is used to identify the county for each Small Area in the VO data set. There are VO entries for 12,904 Small Areas (70%). In total, there are 153,260 VO premises in the commercial data set. 30% of these (all in Dublin, Limerick and Waterford) have floor areas.
The BER records were matched using the Geodirectory to obtain the Small Areas for each. 66% of BER records were matched to a Small Area.
In order to allow the calculation of heat demand, CIBSE benchmarks were used which correspond to kWh per m2. Therefore, the aim of this stage is to estimate floor areas for all known commercial buildings (VO entries) for which no areas are available (70% of VO premises).
When the number of BER buildings in a Small Area is higher than 50% of the VO buildings with no floor area then the BER average floor area for the use type/sector for the Small Area is used to estimate the missing floor areas. Otherwise, a BER county floor area average for the use type/sector is used to approximate the missing floor areas.
Energy consumption benchmarks and heat demands
For the total floor area of commercial buildings calculated, the CIBSE use type/sector fossil-thermal benchmarks were used to obtain heat demands using a counterfactual efficiency for heat of 85%. This forms the baseline heat demand for the heat map for commercial buildings.
To estimate the energy efficient heat demand of the existing commercial building stock, energy benchmarks from the “Unlocking The Energy Efficiency Opportunity” report were used for each use type/sector. An energy efficient benchmark was calculated for each as follows:
- An assumption was made that baseline buildings are distributed in size as follows:
|Building size distribution||Small||Large|
For other building types, the overall building distribution is used (93% small / 7% large).
- An assumption was made that baseline buildings are 50:50 split of ventilation type.
- An assumption was made that baseline buildings are Good or Poor depending on their percentage distribution of double/triple glazing (80% or over for Good, the rest are considered Poor)70:
|Building performance level||Poor||Good|
*Warehouse figures estimated from Figure 22 in the Extensive survey of the commercial buildings stock in the Republic of Ireland
For other building types, the overall building distribution is used (64% Good / 36% Poor).
- A composite energy benchmark for each use type was calculated as above as the baseline benchmark (using the Initial benchmarks) on which current consumption would be based. In a similar way, a composite energy benchmark for each use type is calculated as above using the Medium package benchmarks. A factor between the two is obtained which is applied to the current heat demand Based on CIBSE energy benchmarks) to obtain the estimated energy efficient heat demand of current building stock.
Finally, estimated heat demand for 2025 (10 year projection) was calculated by adding estimated building growth 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 level (or county level if no Small Area identified) spread equally amongst the Small Areas for each and applied to the energy efficient heat demand. One of the weaknesses of this approach is that, for those Small Areas with no recorded buildings, no growth of such building type is predicted. This assumes that new commercial buildings have a similar heat demand as current buildings with energy efficiency.
For the % distribution of fuel type per Small Area approximated by the County level BER distribution, a calibration has been introduced to adjust the electricity % allocation per sector as per the % shown in the “Unlocking The Energy Efficiency Opportunity” report, with the remaining fuels adjusted accordingly.
The “Unlocking The Energy Efficiency Opportunity” report identified the annual volume of oil and gas used for heating in the Commercial sector (2.3TWh). This figure was used to provide a check on the heat demand estimates obtained through the model. Heat demand obtained from the commercial heat map model was identified to be very high in relation to this. This is believed to be mainly due to the approach taken to the allocation of floor area estimates for the VO entries in locations outside Dublin, Limerick and Waterford (see above). For Small Areas where no sufficient BER data allowed for estimates to be provided at this level, overall county level estimates were used. As BER data is believed to be greatly skewed towards new buildings rather than existing ones, and these are highly likely to contain bigger commercial buildings than probably are present in many of the locations involved (more rural areas in general with lesser likelihood of this type of building sizes), a reduction factor of 90% was applied when approximating unknown floor areas using the county level BER averages. This provides a good match between the modelled gas and oil data and figure from the Unlocking The Energy Efficiency Opportunity” report.
Commercial data set methodology
 CSO_DED level is a geographical level intermediate between County and Small Area – population growth estimates were available at this level of disaggregation.