Document Type

Conference Object

Publication Details

Presented at the Collaborative European Research Conference, held in Cork Institute of Technology, Cork, Ireland, 16-18 October, 2013.


Large scale, non-invasive and largely off site modular building retro-fit solutions offer scalable opportunities to assist in climate change adaptation for existing residential built stock. In Ireland, local authority housing (LAH) hosts a large proportion of the existing domestic built stock. This paper outlines the basis for development of a methodology to catalogue and analyze existing LAH developments in Cork City based on their thermal energy performance. This process informs location, elevation, orientation, superstructure and current energy rating. Firstly LAH existing typologies are statistically analyzed to produce a generalized house type representation of a mean terrace typology. Climate data is measured at a number of site specific locations; variations within the data are identified and comparisons are analyzed. Weather files generated for various locations are inputted into the statistical energy model and pre-retrofit findings are analyzed. This tests the efficacy of the climatic data files theoretical foundation, looking at the extent to which it generates an effective support to real time sustainable retro-fit modelling. A detailed systematic retro-fit application can then be designed and modelled using a dynamic thermal model based on the typologies identified following the statistical study. Findings from building simulation modelling will further inform the design methodology. Initial energy modelling is done using PHPP (Passive House Planning Package) and DEAP (Dwelling Energy Assessment Procedure) for preliminary findings. Cork City is used as the case study as it hosts such a large topographical variation; illustrating the effect of micro climate on building energy performance. This paper outlines a proposed research methodology to achieve the objectives set out above and identifies some initial findings which support the research hypothesis presumptions. Variations are found in site specific climate files in close proximit