Abstract Research studies provided evidence on the energy efficiency of integrating personal thermal comfort profiles into the control loop of Heating, Ventilation, and Air-Conditioning (HVAC) systems (i.e., comfort-driven control). However,… Click to show full abstract
Abstract Research studies provided evidence on the energy efficiency of integrating personal thermal comfort profiles into the control loop of Heating, Ventilation, and Air-Conditioning (HVAC) systems (i.e., comfort-driven control). However, some conflicting cases with increased energy consumption were also reported. Addressing the limited and focused nature of those demonstrations, in this study, we have presented a comprehensive assessment of the energy efficiency implications of comfort-driven control to (i) understand the impact of a wide range of contextual factors and their combinatorial effect and (ii) identify the operational conditions that benefit from personal comfort integration. In doing so, we have proposed an agent-based modeling framework, coupled with EnergyPlus simulations. We considered five potentially influential parameters and their combinatorial arrangements including occupants’ thermal comfort characteristics, diverse multi-occupancy scenarios, number of occupants in thermal zones, control strategies, and climate. We identified the most influencing factor to be the variations across occupants’ thermal comfort characteristics - reflected in probabilistic models of personal thermal comfort - followed by the number of occupants that share a thermal zone, and the control strategy in driving the collective setpoint in a zone. In thermal zones, shared by fewer than six occupants, we observed potentials for average energy efficiency gain in a range between −3.5% and 21.4% from comfort-driven control. Accounting for a wide range of personal comfort profiles and number of occupants, the average (±standard deviation) energy savings for a single zone and multiple zones were in ranges of [−3.7 ± 4.8%, 5.3 ± 5.6%] and [−3.1 ± 4.9%, 9.1 ± 5.1%], respectively. Across all multi-occupancy scenarios, a range between 0.0% and 96.0% of combinations resulted in energy savings.
               
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