+Urban Building Energy Modelling
+Aldo Canfora
+1
+, Armando Bazzani
+1
+, Mirko Degli Esposti
+1
+1. Department of Physics and Astronomy, University of Bologna, Via Irnerio 46, 40126 Bologna, Italy;
+Introduction
+For the European Commission, the
+Urban Digital Twin
+is crucial to address challenges such as
+decarbonization and achieving the
+Green Deal
+goals and to develop Smart Cities.
+Infrastructures
+energy management
+makes it possible to reduce consumption, integrate renewable energy and plan
+sustainable cities. This technology is a strategic lever for creating greener, more resilient cities
+aligned with European sustainability goals.
+Fig 1. Above: 3D representation of Bologna, from Comune di
+Bologna. Open Data Bologna has a dataset with a series
+of information about buildings such as height, volumes,
+plan area, geo-shape. Below: A tile of the Bologna 2023
+LiDAR campaign. In addition to Open Data, there are
+LiDAR data from which it is possible to extract
+information, using QGIS software, to make comparisons
+and supplement missing Open Data Bologna.
+Fig 3. Tridimensional representation of a building in Bologna,
+from Energy Plus (EP). This kind of representation is
+made with the creations of idf file (the input file for EP),
+a file with all information about the building such as
+height, volumes, plan area, materials.
+Fig 2. Empirical simulation of a fictitious urban heat island
+(UHI). Left: Each cell can affect its 8 neighbors; the light
+green cells are energy class A-B buildings, the orange cells
+for class C-D-E, the red class for class G-F, the green cells
+for vegetation, the dark grey cells for street. Right: Heat
+map generated by the simulation when the outside
+temperature is 32° C.
+Data
+Methods
+Conclusion
+The workflow starts from collecting all geometrical parameters (such as plan area, volume, height) that
+EP needs for the energy consumption simulation. In order for the simulation to be plausible, it is
+important to have some non-geometrical data on the building (e.g. materials and wall thickness). Due
+to privacy issues, such data sets are not available, but it is possible to cluster (using DBSCAN)
+buildings into various types of archetypes (such as year of construction) and use normative or
+TABULA-related data to fill in the gaps: some data obtained from legal regulations are windows-to-
+surface ratio and energy efficiencies. Each cluster can have one building, representative for the entire
+cluster, of which some data are already known and use these for the other buildings in the cluster. A
+further step could be an analysis of UHI with satellite thermal data to assess the impact of buildings in
+urban settings. The finale objective is to have, for each building, the energy estimation in KWh/m
+2
+.
+Fig 4. Buildings shapefile, each color represents a different
+period when the last infrastructure change occurred. The
+idea is to clusterize, using DBSCAN, these buildings to
+have clusters with similar features such as materials, wall
+thickness.
+