Towards Achieving Thermal Comfort through Physiologically Cloud based controlled HVAC System

TL;DR

This paper introduces a physiologically cloud-based HVAC control system that personalizes indoor temperature settings by predicting individual thermal comfort, aiming to improve shared space comfort and energy efficiency.

Contribution

It presents a novel algorithm that personalizes thermal control by integrating physiological data and preferences to optimize shared space temperature settings.

Findings

Effective personalization of thermal comfort achieved

Improved occupant satisfaction demonstrated

Potential energy savings indicated

Abstract

Thermal comfort in shared spaces is essential to occupants well-being and necessary in the management of energy consumption. Existing thermal control systems for indoor shared spaces adjust temperature set points mechanically, making it difficult to intelligently achieve thermal comfort for all. Recent studies have shown that thermal comfort in a shared space is difficult to achieve due to individual preferences and the inability of occupants to reach a thermal compromise on temperature set points. This paper proposes a thermal comfort system to automatically adjust the temperature set-points in a shared space whilst recognising individual preferences. The control strategy of the proposed system is based on an algorithm to adjust the temperature set point of the shared space using the individual thermal preferences and predicted thermal comfort value of the occupants. The thermal preferences of the occupants are determined first and used as part of the occupants profile, which is mapped to thermal comfort values predicted from the occupants measured physiological data and environmental data. A consensus is reached by the algorithm to find the optimal temperature set-point, which takes into account individual thermal preferences and their physiological responses.