Abstract This study investigates the use of thermal mass within a smart sustainable district (SSD) to facilitate the renewables’ integration. The aim is to pinpoint the most suitable archetypes in a SSD and the most optimal energy flow to each of them; moreover, to establish a relation between the suitability to activate thermal mass and the properties of the archetypes, namely Heat Loss Coefficient (HLC), Structural mass (SM) and time constant (TC). First a dummy residential district is defined, then using construction and envelope properties, archetypes are defined. Based on the archetypes, the district is clustered and then it is modelled using Artificial Neural Networks (ANN). The energy optimization and renewable integration is casted as an Optimal Control Problem (OCP). Solving this OCP ensures thermal comfort within the building clusters and the renewable energy is stored optimally through the thermal mass. Finally, conclusions are drawn on which clusters of buildings offer the most opportunities to store the renewables through their thermal mass within a district and which criteria best reflect on that. The results signify that HLC and TC are more suitable criteria.