The large share of energy consumption in the residential sector has necessitated better understanding and evaluation of the energy needs in this sector, with the objective of identifying possible pathways for improvement. A series of approaches have been used in the literature to evaluate the current situation and predict the future energy and service needs of residential centres. High-level approaches evaluate the impact of long-term changes in the residential sector on the energy consumption and focus on determination of the energy supply requirements. Other approaches use input data with a higher level of detail and are used to estimate the energy consumption of individual users as representatives of the residential stock. This work uses heat signature models and climate data to build a parametrized residential sector profile for different climatic zones. The energy and service profile constructed herein is well-suited for exploring the best technologies for supplying residential requirements, drawing from the domain of process integration. This work demonstrates the usefulness of the residential profile by applying process integration techniques within a mixed integer linear programming (MILP) formulation to evaluate optimal energy conversion technologies for two different district energy networks (DENs): the current network in place and a potential low-temperature refrigerant-based network. The results show that the refrigerant-based network, compared to the network in place, reduces energy consumption and operating cost by approximately 70 % and CO2 emissions by up to 100 %, depending on the mix of electricity used.