Urban Buildings Energy Consumption Estimation Using HPC: A Case Study of Bologna
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911284320010240 |
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| author | Canfora, Aldo Bergamaschi, Eleonora Mioli, Riccardo Battini, Federico Esposti, Mirko Degli Pedrazzi, Giorgio Dellacasa, Chiara |
| author_facet | Canfora, Aldo Bergamaschi, Eleonora Mioli, Riccardo Battini, Federico Esposti, Mirko Degli Pedrazzi, Giorgio Dellacasa, Chiara |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19463 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Urban Buildings Energy Consumption Estimation Using HPC: A Case Study of Bologna Canfora, Aldo Bergamaschi, Eleonora Mioli, Riccardo Battini, Federico Esposti, Mirko Degli Pedrazzi, Giorgio Dellacasa, Chiara Distributed, Parallel, and Cluster Computing Applied Physics 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. |
| title | Urban Buildings Energy Consumption Estimation Using HPC: A Case Study of Bologna |
| topic | Distributed, Parallel, and Cluster Computing Applied Physics |
| url | https://arxiv.org/abs/2511.19463 |