Author:

Abstract:

To facilitate the energy transition, the retrofit of building stocks is a crucial task. A strategy is required to maximize the effect of retrofit to reduce GHG emissions in the given limits of the available investment means. The paper shows that type-age classifications of buildings are not an appropriate grouping for strategy development and proposes an algorithmic clustering as grouping method based on the effect of energy efficiency measures (EEM). This novel clustering method delivers groups of buildings that similarly respond to retrofit measures and thus provide a good basis to develop efficient large-scale retrofit strategies. Besides illustrating the method and its benefits, the paper draws conclusions on the transfer of the method to a regional scale. These conclusions address aspects of the larger heterogeneity of the building stock as well as data availability, scaling and supply structures as well as the utilization of the results for policy making.