Kourgiozou, Vasiliki;
Al-Saegh, Salam;
Korolija, Ivan;
Dowson, Mark;
Commin, Andrew;
Tang, Rui;
Rovas, Dimitrios;
(2023)
Development of a dynamic building stock model for smart energy transition decision support: university campus stock case study.
In:
Proceedings of the 18th Conference of the International Building Performance Simulation Association, BS 2023.
(pp. 3759 -3767).
IBPSA: Shanghai, China.
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Abstract
University campuses present a unique opportunity for decarbonisation through intelligence integration for smart-energy campuses. So far, the evidence-base for smart energy campuses focuses on building-level demonstrations or archetypal approaches and the university campus stock lacks a common assessment framework to characterise and evaluate smart-energy transition pathways.This paper presents a methodological framework that leverages automated computational methods (3DStock, SimStock) to produce building-by-building dynamic thermal models. The modelling method can benefit the evaluation of smart-energy campus and decarbonisation strategies and simulate the dynamics of complex HVAC under demand-response where data availability is more granular. Instead of using archetypal approaches to represent the heterogeneity of building stocks, this work developed an automated building-by-building stock modelling approach based on a case study. HVAC systems are also modelled based on information from Display Energy Certificates. Model calibration is performed at stock level against actual data from Building Monitoring Systems and operational energy performance data following the CIBSE TM63 protocol. Geometry checks showed that 63% of the models matched actual geometry sufficiently, whereas energy use intensity was overestimated by around 35% across the campus in the baseline partially calibrated building models. For a typology, initial comparisons with a fully calibrated model signified lighting, cooling and heating setpoints as potential factors. A major advantage of the method is that it can be flexibly used depending on the data granularity available and, therefore, eliminates a significant barrier that Urban Energy Modelling presents in terms of data availability.
| Type: | Proceedings paper |
|---|---|
| Title: | Development of a dynamic building stock model for smart energy transition decision support: university campus stock case study |
| Event: | 18th Conference of the International Building Performance Simulation Association, BS 2023 |
| Location: | Shanghai, China |
| Dates: | 4th-6th Sep 2023 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.26868/25222708.2023.1686 |
| Publisher version: | https://doi.org/10.26868/25222708.2023.1686 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | smart-energy campus, automated modelling method, building-by-building stock model |
| UCL classification: | UCL 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-pp.ucl.ac.uk/id/eprint/10178836 |
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