| id |
caadria2022_352 |
| authors |
Duran, Ayca, Iseri, Orcun Koral, Meral Akgul, Cagla, Kalkan, Sinan and Gursel Dino, Ipek |
| year |
2022 |
| title |
Compiling Open Datasets to Improve Urban Building Energy Models with Occupancy and Layout Data |
| doi |
https://doi.org/10.52842/conf.caadria.2022.2.669
|
| source |
Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 669-678 |
| summary |
Urban building energy modelling (UBEM) has great potential for assessing the energy performance of the existing building stock and exploring various actions targeting energy efficiency. However, the precision and completeness of UBEM models can be challenged due to the lack of available and reliable datasets related to building occupant and layout information. This study presents an approach that aims to augment UBEM with open-data sources. Data collected from open data sources are integrated into UBEM in three steps. Step (1) involves the generation of occupant profiles from census data collected from governmental institutions. Step (2) relates to the automated generation of building plan layouts by extracting data on building area and number of rooms from an online real-estate website. Results of Steps (1) and (2) are incorporated into Step (3) to generate residential units with layouts and corresponding occupant profiles. Finally, we make a comparative analysis between data-augmented and standard UBEM based on building energy use and occupant thermal comfort. The initial results point to the importance of detailed, precise energy models for reliable performance analysis of buildings at the urban scale. 0864108000 |
| keywords |
urban building energy modelling, occupancy, residential building stock, unit layout Information, open-source datasets, energy demand, indoor thermal comfort, SDG 11 |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (932,398 bytes) |
| references |
Content-type: text/plain
|
Biljecki, F., Ledoux, H., Stoter, J. & Zhao, J. (2014)
 Formalisation of the level of detail in 3D city modelling
, Computers, Environment and Urban Systems, 48, 1–15. https://doi.org/10.1016/j.compenvurbsys.2014.05.004
|
|
|
|
|
Crawley, D. B., Lawrie, L. K., Pedersen, C. O. & Winkelmann, F. C. (2000)
EnergyPlus: Energy Simulation Program
, ASHRAE Journal, 42
|
|
|
|
|
Happle, G., Fonseca, J. A. & Schlueter, A. (2018)
A review on occupant behavior in urban building energy models
, Energy and Buildings, 174, 276–292. Elsevier Ltd. https://doi.org/10.1016/j.enbuild.2018.06.030
|
|
|
|
|
Heydarian, A., McIlvennie, C., Arpan, L., Yousefi, S., Syndicus, M., Schweiker, M., Jazizadeh, F., Rissetto, R., Pisello, A. L., Piselli, C., Berger, C., Yan, Z. & Mahdavi, A. (2020)
What drives our behaviors in buildings? A review on occupant interactions with building systems from the lens of behavioral theories
, Building and Environment, 179. https://doi.org/10.1016/j.buildenv.2020.106928
|
|
|
|
|
Hong, T., Chen, Y., Luo, X., Luo, N. & Lee, S. H. (2020)
Ten questions on urban building energy modeling
, Building and Environment, 168. https://doi.org/10.1016/j.buildenv.2019.106508
|
|
|
|
|
Jeong, B., Kim, J. & de Dear, R. (2021)
Creating household occupancy and energy behavioural profiles using national time use survey data
, Energy and Buildings, 252. https://doi.org/10.1016/j.enbuild.2021.111440
|
|
|
|
|
Mitra, D., Steinmetz, N., Chu, Y. & Cetin, K. S. (2020)
Typical occupancy profiles and behaviors in residential buildings in the United States
, Energy and Buildings, 210. https://doi.org/10.1016/j.enbuild.2019.109713
|
|
|
|
|
Mosteiro-Romero, M., Hischier, I., Fonseca, J. A. & Schlueter, A. (2020)
A novel population-based occupancy modeling approach for district-scale simulations compared to standard-based methods
, Building and Environment, 181. https://doi.org/10.1016/j.buildenv.2020.107084
|
|
|
|
|
Putra, H. C., Andrews, C. & Hong, T. (2021)
Generating synthetic occupants for use in building performance simulation
, Journal of Building Performance Simulation, 14(6), 712–729. https://doi.org/10.1080/19401493.2021.2000029
|
|
|
|
|
Sadeghipour Roudsari, M. & Pak, M. (2013)
Ladybug: A parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design
, Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association (pp. 3128–3135)
|
|
|
|
|
Sun, L. & Erath, A. (2015)
A Bayesian network approach for population synthesis
, Transportation Research Part C: Emerging Technologies, 61, 49–62. https://doi.org/10.1016/j.trc.2015.10.010
|
|
|
|
|
Tahmasebi, F. & Mahdavi, A. (2017)
The sensitivity of building performance simulation results to the choice of occupants' presence models: a case study
, Journal of Building Performance Simulation, 10(5–6), 625–635. https://doi.org/10.1080/19401493.2015.1117528
|
|
|
|
|
Tian, W., Heo, Y., de Wilde, P., Li, Z., Yan, D., Park, C. S., Feng, X. & Augenbroe, G. (2018)
A review of uncertainty analysis in building energy assessment
, Renewable and Sustainable Energy Reviews, 93, 285–301. Elsevier Ltd. https://doi.org/10.1016/j.rser.2018.05.029
|
|
|
|
| last changed |
2022/07/22 07:34 |
|
