# From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing

> Source: <https://arxiv.org/abs/2608.20423>
> Published: 2026-08-24 04:00:00+00:00

# Computer Science > Machine Learning

[Submitted on 19 Aug 2026]

# Title:From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing

[View PDF](/pdf/2608.20423)

[HTML (experimental)](https://arxiv.org/html/2608.20423v1)

Abstract:Personalised thermal comfort is essential for occupant wellbeing and for the development of more responsive building-control strategies, yet conventional Heating, Ventilation, and Air Conditioning (HVAC) systems rely on static setpoints and population-level comfort models that fail to capture individual physiological variability. This paper presents a two-stage personalised thermal comfort approach integrating multimodal physiological and environmental sensing with reinforcement learning-based decision-making.

## Submission history

From: Isibor Kennedy Ihianle [[view email](/show-email/8e7b6e66/2608.20423)]

**[v1]** Wed, 19 Aug 2026 09:39:15 UTC (2,437 KB)

### References & Citations

Loading...

# Bibliographic and Citation Tools

Bibliographic Explorer

*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))
Connected Papers

*(*[What is Connected Papers?](https://www.connectedpapers.com/about))
Litmaps

*(*[What is Litmaps?](https://www.litmaps.co/))
scite Smart Citations

*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article

alphaXiv

*(*[What is alphaXiv?](https://alphaxiv.org/))
CatalyzeX Code Finder for Papers

*(*[What is CatalyzeX?](https://www.catalyzex.com))
DagsHub

*(*[What is DagsHub?](https://dagshub.com/))
Gotit.pub

*(*[What is GotitPub?](http://gotit.pub/faq))
Hugging Face

*(*[What is Huggingface?](https://huggingface.co/huggingface))
ScienceCast

*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos

# Recommenders and Search Tools

Influence Flower

*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))
CORE Recommender

*(*[What is CORE?](https://core.ac.uk/services/recommender))
IArxiv Recommender

*(*[What is IArxiv?](https://iarxiv.org/about))# arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).