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Thermal comfort in shared spaces is essential to occupants well-being andnecessary in the management of energy consumption. Existing thermal controlsystems for indoor shared spaces adjust temperature set points mechanically,making it difficult to intelligently achieve thermal comfort for all. Recentstudies have shown that thermal comfort in a shared space is difficult toachieve due to individual preferences and the inability of occupants to reach athermal compromise on temperature set points. This paper proposes a thermalcomfort system to automatically adjust the temperature set-points in a sharedspace whilst recognising individual preferences. The control strategy of theproposed system is based on an algorithm to adjust the temperature set point ofthe shared space using the individual thermal preferences and predicted thermalcomfort value of the occupants. The thermal preferences of the occupants aredetermined first and used as part of the occupants profile, which is mapped tothermal comfort values predicted from the occupants measured physiological dataand environmental data. A consensus is reached by the algorithm to find theoptimal temperature set-point, which takes into account individual thermalpreferences and their physiological responses.

Towards Achieving Thermal Comfort through Physiologically Cloud based controlled HVAC System