Assessing the existing building stock’s hourly energy demand and predicting its variation due to energy efficiency measures are fundamental for planning strategies towards renewable-based Smart Energy Systems. However, the need for accurate methods for this purpose in the literature arises. The present article describes a GIS-based procedure developed for estimating the energy demand profiles of urban buildings based on the definition of the volumetric consistency of a building stock, characterized by different ages of construction and the most widespread uses, as well as dynamic simulations of a set of Building Energy Models adopting different energy-related features. The simulation models are based on a simple Building Energy Concept where selected thermal zones, representative of different boundary conditions options, are accounted. By associating the simulated hourly energy density profiles to the geo-referenced building stock and to the surveyed thermal system types, the whole hourly energy profile is estimated for the considered area. The method was tested on the building stock of Milan (Italy) and validated with the data available from the annual energy balance of the city. This procedure could support energy planners in defining urban energy demand profiles for energy policy scenarios.

In communities with a high tourist influx and warm climate, such as Mediterranean small islands, the energy demands for space cooling and domestic hot water are expected to significantly increase during summer. Considering the current energy context, for succeeding energy transition and reducing the dependency on not endogenous fossil fuels, it is paramount to increase the deployment of renewable sources of energy, especially wind and solar which, however, are aleatory and unpredictable. Hence, to reduce the high costs for energy supply in these contexts, the analysis of the variation of energy consumption is fundamental. Moreover, mapping the spatial distribution of energy profiles can be useful to have an overview at a large scale of the considered building stock. Within this frame, a Geographic-Information-System-based procedure was implemented to estimate the residential buildings energy demand profiles, focusing on the seasonal variation. The adopted method can provide a valid supporting tool for decision makers that have to implement smart energy strategies in contexts with a high variation of the energy demand and evident electricity summer peaks. The method for mapping the energy demand profiles, implemented on the small island of Pantelleria, can be applied to other similar contexts, also supporting energy policies in the implementation of renewable energy communities.