Neighborhood characteristics influence natural urban energy fluxes and the choices made by urban actors. This article focuses on the impact of urban density as a neighborhood physical parameter on building energy consumption profiles for seven different metropolitan areas in the United States. Primarily, 30 × 30 m² cells were classified into five categories of settlement density using the US Geological Survey’s National Land Cover Dataset (NLCD), the US Census, and Census Block data. In the next step, linear hierarchical spatial and non-spatial models were developed and applied to building energy data in those seven metropolitan areas to explore the links between urban density (and other urban form parameters) and energy performance, using both frequentist and Bayesian statistics. Our results indicate that urban density is correlated with energy-use intensity (EUI), but its impact is not similar across different metropolitan areas. The outcomes of our analysis further show that the distance from buildings within which the influence of urban form parameters on EUI is most significant varies by city and negatively changes with urban density. Although the relationship between urban density parameters and EUI varies across cities, tree-cover area, impervious area, and neighborhood building-covered area have a more consistent impact compared to building and housing density.

Three building analysis software programs, DOE-2 eQUEST, IESVE Revit Plug-in and Autodesk Green Building Studio, are used to quantify the predicted energy savings of a scheduled envelope retrofit on a university dormitory. The study includes investigating the potential energy savings created by the removal and replacement of all original windows and exterior non-structural infill brick panels coupled with installation of supplementary insulation materials between the new brick panels and the interior concrete masonry unit walls. The rehabilitation will take place in four over 7000 m² seven-storey residence halls originally constructed in 1965. Reports generated from baseline design simulations are compared with real energy consumption data in order to ascertain the accuracy of the three modelling programmes. Analysis of the retrofit proposal is carried out by comparing results of each retrofit design alternative against the baseline and assessing CO ₂ emissions reduction resulting from the proposed retrofit process. Advantages and disadvantages of each modelling programme are also discussed.