An efficient thermal comfort delivery in workplaces

Prevailing mechanisms that deliver thermal comfort in workplaces are energy-hungry and yet provide a sub-optimal thermal comfort. Indeed, they are based on flawed premises and purposely ignore decisive precursors to thermal comfort. This research proposes to estimate a person's thermal comfort level from the fluctuations in his physiological signals and to use appropriate constrained optimization algorithms to provide an optimum and personalized thermal comfort using the least possible energy. The preliminary findings strongly suggest the feasibility and practicality of such an approach.