Chiu, Chun Wai, Efstratiou, Christos, Nikolopoulou, Marialena, Barker, Matthew, Baldwin, Andrew, Clarke, Malcolm (2024) A Machine Learning Framework for Optimising Indoor Thermal Comfort and Air Quality through Sensor Data Streams. In: Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. BuildSys '24: Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. . pp. 329-332. ACM ISBN 979-8-4007-0706-3. (doi:10.1145/3671127.3699531) (KAR id:107759)
|
PDF
Publisher pdf
Language: English 
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
|
|
|
Download this file (PDF/1MB) |
Preview |
| Request a format suitable for use with assistive technology e.g. a screenreader | |
| Official URL: https://doi.org/10.1145/3671127.3699531 |
|
Abstract
Optimising thermal comfort and air quality in indoor environments presents a complex, dynamic challenge that traditional static systems struggle to address effectively. We propose a novel real-time framework that tackles this multifaceted problem by leveraging stream clustering and time-series forecasting techniques. Our system continuously analyses sensor data to summarise comfort conditions and predict future indoor states. Simulations based on the stream clustering model indicate potential for significant improvements in indoor comfort, increasing comfort duration from 6% to 74%. Furthermore, the time-series forecasting model demonstrated strong performance, achieving mean absolute errors of 0.026 and 0.034 on test and demonstration datasets, respectively. This resource-efficient approach demonstrates promise for real-time indoor environment management, effectively balancing thermal comfort and air quality considerations.
| Item Type: | Conference or workshop item (Proceeding) |
|---|---|
| DOI/Identification number: | 10.1145/3671127.3699531 |
| Uncontrolled keywords: | thermal comfort, air quality, built environments, machine learning |
| Subjects: |
N Visual Arts > NA Architecture Q Science > QA Mathematics (inc Computing science) |
| Divisions: |
Divisions > Division of Arts and Humanities > Kent School of Architecture and Planning Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
| Funders: | Innovate UK (https://ror.org/05ar5fy68) |
| SWORD Depositor: | JISC Publications Router |
| Depositing User: | JISC Publications Router |
| Date Deposited: | 12 Feb 2025 15:22 UTC |
| Last Modified: | 16 Feb 2025 19:21 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/107759 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Chiu, Chun Wai.
| Creator's ORCID: |
 https://orcid.org/0000-0002-3157-8943
|
|---|---|
| CReDIT Contributor Roles: |
Efstratiou, Christos.
| Creator's ORCID: |
https://orcid.org/0000-0001-6288-9579
|
|---|---|
| CReDIT Contributor Roles: |
Nikolopoulou, Marialena.
| Creator's ORCID: |
https://orcid.org/0000-0002-0528-2145
|
|---|---|
| CReDIT Contributor Roles: |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):
Altmetric
Altmetric
