For effective integration of large amounts of renewables and high-efficiency energy technologies, their benefits have to be quantified. Network-level energy optimisation approaches can determine the optimal location of generation technologies within a region and the optimal layout of energy distribution networks to link them. Mixed-integer linear programming (MILP) formulations are generally employed and this is often a burden for large scale models as the computational time drastically increases with the problem size. Most methods used to reduce the complexity of MILP problems focus on the temporal scale or use aggregated demand profiles for the spatial dimension. There is a lack of a method addressing the spatial complexity to assess the potential of interlinked energy networks at large scale. Therefore, this paper introduces a new combined clustering schema enabling quantification of the potential of district heating networks based on results from building scale energy optimisation problems and taking into account building characteristics. A city-scale case is divided into multiple districts based on the output of a density based clustering algorithm. The parameters taken into account by the clustering method are the cluster density, homogeneity index and load magnitude. The analysis of the clustering map along with building characteristics of each cluster reveals the required characteristics for the installation of a district heating network or distributed energy systems. (C) 2018 Elsevier Ltd. All rights reserved.