Self-Learning Framework for Personalised Thermal Comfort Model

Yiqiang Zhao, Kate Carter, Fan Wang, Ola Uduku, Dave Murray-Rust

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel self-learning framework for building personalised thermal comfort model. The framework is built with the understanding that each occupant has a unique thermal comfort preference. Current thermal comfort models focus on analysing average data for groups of people in different types of building, rather than considering individual thermal preference. We argue that building a personal level comfort model using learning algorithms may provide the basis to represent personalised dynamic thermal demands. By bringing more personal interest and data, the ground-up personalised model may help us better understand the internal links of personal factors from psychology, physiology and behavioural aspects. Furthermore, we developed an Smart Thermal Comfort (STC) environment sensors and mobile application to efficiently collect distributed personal data and make it open-sourced for other researchers to use. The aim of this paper is to rethink current comfort studies to standardize the methods in modelling personalised thermal comfort. By summarising the past five years' papers on personal thermal comfort model, this paper critically evaluates the methods used for personal data collection and learning algorithms. Finally, we conclude an Personal Thermal Comfort (PTC) framework including distributed personal measurement tools and machine learning algorithm for personalised thermal comfort study.
Original languageEnglish
Title of host publicationProceedings of the 10th Windsor Conference
Subtitle of host publicationRethinking Comfort
EditorsLuisa Brotas, Susan Roaf, Fergus Nicol, Michael Humphreys
PublisherNCEUB
Pages923-934
Number of pages12
ISBN (Print)9780992895785
Publication statusPublished - 2018
Event10th Windsor Conference: Rethinking Thermal Comfort - Windsor, United Kingdom
Duration: 12 Apr 201815 Apr 2018

Conference

Conference10th Windsor Conference
CountryUnited Kingdom
CityWindsor
Period12/04/1815/04/18

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  • Cite this

    Zhao, Y., Carter, K., Wang, F., Uduku, O., & Murray-Rust, D. (2018). Self-Learning Framework for Personalised Thermal Comfort Model. In L. Brotas, S. Roaf, F. Nicol, & M. Humphreys (Eds.), Proceedings of the 10th Windsor Conference: Rethinking Comfort (pp. 923-934). NCEUB. http://windsorconference.com/proceedings/