This student will work under the MaREI National Retrofitting Modelling Group in partnership with fellow ERBE CDT PhD students already established in this area. This research, in the context of Irelands residential climate action targets, will model energy conservation and CO2 mitigation in a dwelling stock as described by Figure 1:
Segmentation: Model energy demand and supply of reference dwellings/archetypes using dynamic simulation programmes to develop new knowledge to inform policy.
Characterisation: Specifically, model Reference Dwellings (RD’s) in order to facilitate;
- the identification of sensitive parameters important to overall performance,
- through changing such parameters, forecasting the consequences of specific scenarios or policy-interventions,
- policy-makers in preparing substantive arguments for particular retrofit interventions and contemporaneous insight-driven policies.
Fig. 1 Modelling Energy Conservation and CO2 Mitigation in the European Building Stock Source: Mata É. (2013)
Quantification: Through first using the dynamic models created for use at RD or end-use level and then aggregating upwards, to observe, analyse and inform energy use characteristics at stock level.· Validation: Validate model created.Advise on realisable targets along with specific and tailored policy interventions for a dwelling stock.
The PhD student will gain expertise inter alia in, building energy simulation and national/technical strategy evaluation for decarbonisation of national building stocks in a data led manner.
Energy analyses of dwelling stocks combine a stock model and an energy model. The stock model describes the stock size, composition and renovation status, whereas the energy model describes the average energy intensities of the various segments of the stock and assumed energy savings obtained when dwellings are renovated. Dwelling stock models that include the renovation status of the dwelling stock enable energy analyses of the stock to inform policy.
The all-encompassing disaggregated thermophysical input data required to effectively inform residential stock energy consumption models are computationally intensive and have relied traditionally on laborious manual data analysis. Since it has been impractical to model every single building, it is normal to define a set of reference dwellings (RDs) that are representative of typical national or regional building. RDs are used to produce overall energy saving extrapolations as shown
Figure 2 Number of State Refurbishment grants paid in Ireland (Source: Ahern and Norton 2019)
in Figure 1. Policy that seeks to reduce domestic energy use is driving rapid change in the sector, the MaREI National Retrofitting Modelling Group established that significant retrofits have taken place in the Irish housing sector (see Figure 2 and Table 1), resulting in approximately 60% of the existing stock being well insulated in 2014 (see Table 2). The level of retrofit is significantly higher than is assumed by policy which is attributed to energy stock models lagging the renovation wave.
Table 1 – Thermal Refurbishment status of the Irish housing stock in 2014 (Source: Ahern & Norton 2019)
Table 2 – Improvement in Thermal Refurbishment status of Irish housing stock from 2001 to 2014 (Source: Ahern & Norton 2019)
Ahern and Norton 2019
The rate and number of thermal retrofits, ‘the state of a stock’ is captured contemporaneously within national Energy Performance Certificate (EPC) databases. A rapid, robust and automated process to extract information from these datasets is necessary to keep pace with the rate of renovation and to track the effectiveness of policy. The MaREI National Retrofitting Group are defining the conditions for a validated EPC dataset and automating methods for cleaning an EPC dataset to an acceptable level before using machine learning and unsupervised clustering methods to derive objectively, contemporaneous RDs characterising a national stock model. This research will advance this work through micro-modelling the RDs created to ultimately advise realisable cost-optimal retrofit targets along with specific and tailored policy interventions for a national housing stock as per the outline methodology described by Figure 3
Figure 3 – Outline Project Methodology
Project Overview
MaREI’s National Retrofitting Modelling Group is currently developing a rapid, robust and automated process to extract information from household Energy Performance Certificate datasets to create a real-time stock model for Irish housing. This PhD project will model energy use in Irelands housing stock using this new information and will develop new knowledge that will directly inform climate policy decisions. The analysis undertaken by the PhD student will address key information gaps that are required to achieve Ireland’s ambitious emissions reduction targets. The doctoral candidate will be supported by three leading experts and become part of the ERBE centre for doctoral training team and MaREI’s National Retrofitting Modelling Group.
Through completing this PhD, the student will gain expertise inter alia in, building energy simulation and national/technical strategy evaluation for decarbonisation of national building stocks in a data led manner to inform national climate policy decisions.
Supervisors
Dr. Ciara Ahern, TU Dublin, is a founding member of the MaREI national retrofitting modelling group and is supervising currently 2 other PhD students working in this area will act as lead supervisor. Ciara has a strong track record in this area having modelled, as far back as 2010, heat pumps retrofitted into a thermally refurbished stock. Since 2014, Ciara has worked closely with Prof. Brian Norton who is Head of Energy Research at the Tyndall National Institute and who will act as co-supervisor along with Prof. Brian Ó Gallachóir, Professor of Energy Engineering in University College Cork’s School of Engineering, Vice-Director of UCC’s Environmental Research Institute and Director of the national SFI MaREI Centre. The student will thus be able to benefit from a wealth of experience in this area as well as in the supervision of PhD’s.
Minimum of a 2.1 honours degree (level 8) in a relevant discipline
Please review detailed Admission Requirements at TU Dublin GRADUATE RESEARCH REGULATIONS
There is currently no funding available for this area and relevant research fees apply.
If you are interested in submitting an application for this project, please complete the application steps given in https://www.marei.ie/phd-positions-erbe-2022/ and email your CV to ciara.ahern@tudublin.ie with the subject “Building Stock Modelling Application”.