Several building-stock modelling techniques have been employed to investigate the impact of energy efficiency measures (EEM), where the description of the building-stock generally consists of an age-type classification to specify building characteristics for groups of buildings. Such descriptions lack the appropriate level of detail to differentiate the potential for EEM within age groups. This paper proposes a methodology for building-stock description using building-specific data and measured energy use to augment an age-type building-stock classification. By integrating building characteristics from energy performance certificates, measured energy use and envelope areas from a 2.5D GIS model, the building-stock description reflects the heterogeneity of the building-stock. The proposed method is validated using a local building portfolio (N=433) in the city of Gothenburg, where modelled results for space heating and domestic hot water are compared to data from measurements, both on an individual building level and for the entire portfolio. Calculated energy use based on the building-stock description of the portfolio differ less than 3% from measured values, with 42% of the individual buildings being within a 20% margin of measured energy use indicating further work is needed to reduce or quantify the uncertainty on a building level

The European building stock was renewed at a rapid pace during the period 1950–1975. In many European countries the building stock from this time needs to be renovated. There is an opportunity to introduce energy efficiency measures in the renovation process, but in this process social aspects should also be taken into account. The purpose of this article is to provide an estimate of the economic and societal challenge of renovating and energy retrofitting the aging building stock. Building specific data on energy usage and previous renovation investments made in the multi-family dwellings in Gothenburg (N=5 098) is aligned with data on tenure type and average income. Based on conducted energy retrofitting projects, costs are estimated for renovating and energy retrofitting multi-family dwellings that will reach the service life of 50 years before 2026. It is found that the pace of renovation needs to increase and that there is risk of increasing societal inequity due to rent increases in renovated buildings

Building databases are important assets when estimating and planning for national energy savings from energy retrofitting. However, databases often lack information on building characteristics needed to determine the feasibility of specific energy conservation measures. In this paper, machine learning methods are used to enrich the Swedish database of Energy Performance Certificates with building characteristics relevant for a chosen set of energy retrofitting packages. The study is limited to the Swedish multifamily building stock constructed between 1945 and 1975, as these buildings are facing refurbishment needs that advantageously can be combined with energy retrofitting. In total, 514 ocular observations were conducted in Google Street View of two building characteristics that were needed to determine the feasibility of the chosen energy retrofitting packages: (i) building type and (ii) suitability for additional façade insulation. Results showed that these building characteristics could be predicted with an accuracy of 88.9% and 72.5% respectively. It could be concluded that machine learning methods show promising potential to enrich building databases with building characteristics relevant for energy retrofitting, which in turn can improve estimations of national energy savings potential.