Urban Buildings Energy Consumption Estimation Using HPC: A Case Study of Bologna

TL;DR

The paper presents a scalable UBEM workflow that combines EnergyPlus, HPC, and open geospatial data to estimate city-scale building energy demand for Bologna, Italy. By fusing LiDAR-derived geometry, TABULA archetypes, and regulatory inputs, the pipeline generates ~25,000 per-building EnergyPlus inputs and runs simulations on the Leonardo HPC cluster to achieve a near-real-time city-scale energy assessment. Calibration relies on proxy archetype data (due to privacy-limited consumption records) and qualitative cross-checks against TABULA, while scenario analyses explore neighborhood effects and Pareto-optimal retrofit strategies. The study demonstrates a replicable, data-driven framework capable of informing urban energy planning and decarbonization strategies, with future work aimed at integrating real consumption data, temporal dynamics, and multi-layer city context.

Abstract

Urban Building Energy Modeling (UBEM) plays a central role in understanding and forecasting energy consumption at the city scale. In this work, we present a UBEM pipeline that integrates EnergyPlus simulations, high-performance computing (HPC), and open geospatial datasets to estimate the energy demand of buildings in Bologna, Italy. Geometric information including building footprints and heights was obtained from the Bologna Open Data portal and enhanced with aerial LiDAR measurements. Non-geometric attributes such as construction materials, insulation characteristics, and window performance were derived from regional building regulations and the European TABULA database. The computation was carried out on Leonardo, the Cineca-hosted supercomputer, enabling the simulation of approximately 25,000 buildings in under 30 minutes.

Figures (17)

• Figure 1: A set of shapefile polygons representing building footprints in Bologna from Edifici Particellari dataset.
• Figure 2: Example of $2.5D$, or LoD1, geometry of a building in Bologna using building plan vertices taken from its Geo Shape and the height found by LiDAR. The windows were added respecting the legal regulation between $\frac{windowed_{area}}{plan_{area}} = \frac{1}{8}$.
• Figure 3: Example of buildings height extraction. a) Building shapes from Open Data. b) Building perimeter footprint used to extract the height.
• Figure 4: Age-based archetypes distribution in the city center of Bologna.
• Figure 5: Annual normalized simulated energy consumption for buildings in Bologna center.