Pan, J;
Cho, TY;
Bardhan, R;
(2022)
Occupancy level prediction based on a sensor-detected dataset in a co-working space.
In:
Proceedings of the 9th ACM International Conference on Systems for Energy Efficient Buildings Cities and Transportation.
(pp. pp. 340-347).
ACM: Boston, MA, USA.
Preview |
Text
Pan_3563357.3566133.pdf Download (2MB) | Preview |
Abstract
Hybrid working has reshaped people's routines and working habits, while the workplace needs to evolve with the new working pattern. Co-working space is seen as an alternative work environment, for cost-effectiveness, the opportunity for flexible design and multi-use. This study investigates the occupancy patterns and occupants' behaviour using multiple occupancy sensor data with a twelvemonths sample. Data-driven AutoRegressive Integrated Moving Average (ARIMA) time series model is applied to predict office occupancy in a co-working space in London. The results reveal some spatial-temporal variations in the number of occupants based on the detected locations. The spatial distribution of occupants around different working areas in the co-working space is plotted to demonstrate the seat preferences and its temporal occupancy density variation.
| Type: | Proceedings paper |
|---|---|
| Title: | Occupancy level prediction based on a sensor-detected dataset in a co-working space |
| Event: | BuildSys '22: The 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3563357.3566133 |
| Publisher version: | https://doi.org/10.1145/3563357.3566133 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| 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/10215721 |
Archive Staff Only
 |
View Item |
