Lilis, GN;
Giannakis, G;
Katsigarakis, K;
Rovas, D;
(2018)
District-aware Building Energy Performance simulation model generation from GIS and BIM data.
In:
Proceedings of the BSO 2018: 4th Building Simulation and Optimization Conference.
BIPSA: Emmanuel College, University of Cambridge.
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Session 2B-5-Lilis-A district-aware Building Energy Performance simulation model generation.pdf - Published Version Download (2MB) | Preview |
Abstract
The assessment of district retrofit scenarios performed by the online platform OptEEmAL, requires an automatic and district-aware building energy performance simulation model generation operation, applied on certain buildings of interest. Such operation involves fusion of BIM, GIS and other data, under a common scheme suitable for building energy performance simulations and transformation of the fused data into appropriate simulation input data files. The above processes, which should conform to certain data quality conditions, are performed by data checking and semantic enrichment software tools, which are described in detail and demonstrated on specific building and district examples.
| Type: | Proceedings paper |
|---|---|
| Title: | District-aware Building Energy Performance simulation model generation from GIS and BIM data |
| Event: | BSO 2018: 4th Building Simulation and Optimization Conference |
| Location: | Cambridge, UK |
| Dates: | 11 September 2018 - 12 September 2018 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.conftool.pro/bso2018/index.php?page=br... |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10056593 |
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