AI-Based Controls for Thermal Comfort in Adaptable Buildings: A Review

Simple item page
dc.citation.issue11
dc.citation.volume14
dc.contributor.authorAhsan M
dc.contributor.authorShahzad W
dc.contributor.authorArif K
dc.date.accessioned2024-11-06T22:30:12Z
dc.date.available2024-11-06T22:30:12Z
dc.date.issued2024-11-04
dc.description.abstractDue to global weather changes and pandemics, people are more likely to spend most of their time in indoor environments. In this regard, indoor environment quality is a very important aspect of occupant well-being, which is often ignored in modern building designs. Based on our research, thermal comfort is one of the essential items in building environments that can improve the mental stability and productivity of the occupants if the building’s indoor environment is created in a way that meets the occupants’ comfort requirements. Buildings nowadays operate on adaptive or stationary models to attain thermal comfort, which is based on Fanger’s model of the Predicted Mean Vote (PMV). Based on the literature review, limited work has been carried out to enhance the quality of the inside environment, and most research work has been devoted to building energy management. Moreover, there have been no definite solutions so far that have the capability to detect the thermal comfort requirements of multiple occupants in real time. Modern buildings tend to operate on predefined set point parameters to control the indoor environment based on the measured room temperature, which can be different from the thermal comfort requirements of the occupants. This paper discusses the limitations and assumptions that are associated with the existing thermal comfort solutions and emphasises the importance of having a real-time solution to address the thermal requirements of occupants.
dc.description.confidentialfalse
dc.identifier.citationAhsan M, Shahzad W, Arif K. (2024). AI-Based Controls for Thermal Comfort in Adaptable Buildings: A Review. Buildings. 14. 11.
dc.identifier.doi10.3390/buildings14113519
dc.identifier.elements-typejournal-article
dc.identifier.issn2075-5309
dc.identifier.number3519
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71949
dc.publisherMDPI AG
dc.relation.isPartOfBuildings
dc.relation.urihttps://www.mdpi.com/2075-5309/14/11/3519
dc.titleAI-Based Controls for Thermal Comfort in Adaptable Buildings: A Review
dc.typeJournal article
massey.relation.uri-descriptionPublished version
pubs.elements-id492139
pubs.organisational-groupCollege of Sciences
Files
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
9.22 KB
Format:
Plain Text
Description:
Download
Collections
Journal Articles