Al-Saegh, Salam;
Kourgiozou, Vasiliki;
Korolija, Ivan;
Tang, Rui;
Tahmasebi, Farhang;
Mumovic, Dejan;
(2025)
Investigating building stock energy and occupancy modelling approaches for district-level heating and cooling energy demands estimation in a university campus.
Energy and Buildings
, 329
, Article 115269. 10.1016/j.enbuild.2024.115269.
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Abstract
The urgency of decarbonizing the built environment requires precise modeling of building stock energy per formance for effective large-scale planning and retrofitting. Despite advancements in data and modeling tech niques, uncertainties persist in balancing model complexity and accuracy, especially in representing occupancy patterns and their impact on energy demand at district and urban scales. This study examines various approaches to building stock energy simulation and occupancy modeling for district-level heating and cooling energy de mand, using 19 buildings at a Central London campus as a case study. Five scenarios were evaluated: Scenario A employs THERMOS, a data-driven approach; Scenario B uses a single dynamic thermal simulation model for the entire inventory; Scenario C applies a thermal model with a uniform occupancy schedule across all buildings; Scenario D uses a thermal model with five distinct occupancy profiles; and Scenario E assigns unique occupancy profiles based on energy use data. Results showed that Scenario E’s annual heating demand estimation closely matched metered data (12 % difference), while Scenario A underestimated by 44 %. Complex occupancy models improved peak heating load predictions, with Scenario E showing only a 4 % difference from metered data, though it may not always be feasible due to data and computational constraints. Scenario D emerged as a promising balance between accuracy and efficiency. For cooling demand, significant differences among scenarios (56.43 to 6.1 kWh/m2 /Y) underscored the importance of accurate occupancy modeling. This research identifies the optimal balance between model complexity and prediction accuracy, introduces the Energy Data-Driven Occupancy Schedule (EDDOS) method, and highlights the potential of data-driven approaches to enhance en ergy demand assessments.
| Type: | Article |
|---|---|
| Title: | Investigating building stock energy and occupancy modelling approaches for district-level heating and cooling energy demands estimation in a university campus |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.enbuild.2024.115269 |
| Publisher version: | https://doi.org/10.1016/j.enbuild.2024.115269 |
| Language: | English |
| Additional information: | © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Building Stock Energy Simulation, Occupancy Modelling, District-Level Energy Demand, Energy Data-Driven Occupancy Schedule (EDDOS), Heating and Cooling Energy Demand, Dynamic Thermal Simulation, Energy Performance, Urban Scale Energy Modelling, District Energy Systems (DES), Energy Demand Assessment |
| 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.ucl.ac.uk/id/eprint/10203205 |
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| 3. |  China | 1 |
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