User-centric urban energy modelling: A reduced order model approach based on pre-calibrated archetypes dataset

Significant efforts have been directed by governments and international organizations towards establishing sustainable development programs for cities and communities to facilitate the urban energy transition, encountering challenges in evolving towards a cleaner and more resilient energy system. In support of designing and operating such sustainable energy systems, the development of appropriate modelling frameworks becomes crucial. These frameworks should enable the identification of renovation priorities and the formulation of innovative energy management schemes, business models, and insights for self-sustaining urban districts or communities. The core of implementing sustainable energy hubs in an urban context is undergoing as well as modelling the end-users’ energy demand and their interaction with the hub. This study presents a modelling approach to unlock some of the main challenges encountered in urban energy demand simulation with a perspective to user-centric modelling framework. Specifically, a urban energy demand modelling framework integrating building Reduced Order Models with end-user occupancy dynamics to provide a scalable and comprehensive solution is developed. The method leverages archetype end-user categories derived from detailed white-box building simulations and Time-of-Use Survey data, creating a framework for generating Resistance-Capacitance-based datasets applicable to urban-scale simulations. By capturing complex heat transfer dynamics, the approach facilitates scalability from individual buildings to large clusters. The approach is tested through a proof-of-concept simulation, and it is considered a valid tool to conduct multi-domain analysis and to support the development of building clusters and community energy renovation.