General practice in current building HVAC control is to select building temperature setpoints that comply with ASHRAE Standard 55. By meeting this standard, based on the PMV comfort model, 80% of building occupants should be satisfied with their thermal environment. However, unfortunately, this is rarely the case. One possible reason for this is the variation in occupant activity and clothing that are usually assumed default values using this standard. In this work, we present an iterative-based algorithm to solve this problem. The algorithm solves the PMV inverse model equation to determine the optimal temperature setpoint while inferring human activity level from the biometric data of wearable fitness devices. The new algorithm is also designed to handle multi-occupants with conflicting comfort preferences scenario. Using this new algorithm, our results show a significant increase in occupant comfort, specifically when occupant activity is high.
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