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

TL;DR

This paper presents a high-performance computing-based urban building energy modeling pipeline that efficiently estimates Bologna's city-scale energy demand using detailed geospatial and building data.

Contribution

It introduces an integrated UBEM pipeline combining EnergyPlus, HPC, and open data, enabling rapid city-scale energy simulations.

Findings

Simulated approximately 25,000 buildings in under 30 minutes.

Integrated diverse data sources for detailed energy modeling.

Demonstrated feasibility of large-scale urban energy simulations.

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.