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Optimizing HVAC Systems using Occupant Detection and User Thermal Preferences


Buildings are a crucial part of our daily lives and people spend 87% of their time

inside buildings. To maintain thermal comfort in buildings a significant amount

of energy is used to condition these spaces. In the US buildings account for 40%

of energy usage and of that 50% of energy goes to heating, ventilation, and air

conditioning (HVAC). Often this energy is wasted by conditioning empty rooms

or by leaving building occupants unsatisfied with the temperature of their room.

In this thesis we present several ways to reduce energy usage while improv-

ing user comfort. First, we reduce energy consumption by incorporating a new

thermal-based occupancy sensor. Energy can be saved by using these thermal

based sensors to detect occupancy and predict movements between rooms and

only conditioning rooms which are occupied. Second, we focus on improving oc-

cupant’s thermal comfort by giving them a method of participatory voting and

influencing how they vote by using several feedback mechanisms which can in-

crease user engagement and reduce HVAC energy usage. And finally, we combine

the previous concepts into an optimization problem that finds the optimal control

sequences based on occupancy, user voting, and several other inputs.

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