A method for determining a true national statistical distribution of building element U-values and its application to a dataset underpinning a building stock energy model

The presence of default U-values in Energy Performance Certificate (EPC) databases disrupts the natural variability in measured U-value distributions. Default values, often used by assessors when empirical data is unavailable, introduce deterministic artefacts into datasets, creating artificial peaks that obscure underlying statistical patterns. This study examines the impact of these default values and identifies appropriate statistical models – Lognormal, Gamma and Normal – for measured U-values in Ireland’s national EPC dataset, comprising 463,582 dwellings. Lognormal and Gamma distributions aligned better with the central peaks, while the Normal distribution more accurately captured the tails. Although no single distribution perfectly fits the data, the Normal distribution was selected as a reasonable and interpretable approximation due to its simplicity, stable parameters, and practical advantages for modelling. This choice was validated through internal data-splitting, external comparison using an independent housing sample, and simulations of typical wall configurations. The findings highlight the distorting influence of default U-values on national modelling efforts and support the use of updated, data-driven defaults. Adopting an empirically grounded Normal distribution for measured U-values enables more robust characterisation of building stock thermal performance and supports improved interpretation of thermal energy performance in built environment policy development. Practical Application: The research improves the accuracy of building stock energy models by identifying the Normal distribution as the best fit for measured U-values in Ireland’s EPC database. It addresses inaccuracies caused by deterministic default U-values, advocating for data-driven dynamic defaults to better represent thermal performance. The findings enable the development of statistically robust reference dwellings (RDs) and more accurate retrofit strategy planning. Validated through internal and external methods, the approach supports improved policy formulation and energy efficiency assessments. The methodology can be generalised to other regions, enhancing the accuracy of national energy models and guiding effective retrofit policies.