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

2025-11-21

AI Summary

To address the low computational efficiency and limited accuracy of city-scale building energy modeling, this study proposes a high-performance computing (HPC)-enabled urban energy simulation framework integrating multi-source geospatial data. Methodologically, the framework couples the EnergyPlus simulation engine with LiDAR-derived high-fidelity building geometries, the TABULA building typology library, region-specific regulatory parameters, and open geospatial datasets, all executed in parallel on an HPC platform. It estimates annual heating and cooling energy demand for approximately 25,000 buildings across Bologna, Italy, within 30 minutes—achieving over a 100× speedup relative to conventional single-machine simulations. Key contributions include: (1) the first implementation of city-wide, LiDAR-enhanced, real-time EnergyPlus simulation; (2) substantial improvements in geometric fidelity and predictive accuracy; and (3) empirical validation of the scalability and engineering applicability of HPC-driven urban energy modeling.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• Estimating urban building energy consumption at scale
• Integrating HPC with geospatial data for energy modeling
• Simulating thousands of buildings efficiently using supercomputing

Innovation

• EnergyPlus simulations for building energy modeling
• HPC supercomputing for rapid large-scale analysis
• Open geospatial and regulatory data integration