Humans spend 90% of their lives inside buildings, but often the Heating, Ventilation, and Air Conditioning (HVAC) systems of commercial buildings do not properly maintain occupant comfort. Use of feedback through comfort voting applications has been shown to improve the quality of service, but the effects of application feedback and user interface design has not been investigated. In this work, we present several methods of feedback that use data presentation and environmental interaction in comfort voting applications. Through a 40 week user study of 61 University employees across 3 buildings, we show that feedback systems can be used to increase user satisfaction with thermal conditions from 33.9% to 93.3% and reduce energy consumption up to 18.99% compared to a system without voting. In addition, we find that by including a drifting control strategy, we find energy savings up to 37% can be realized without a significant reduction in satisfaction.

Buildings are responsible for more than 40% of US primary energy consumption. Of that, nearly 50% goes to heating, ventilation and air-conditioning (HVAC) systems. Savings made in HVAC systems therefore have a major impact in energy consumption and are of critical importance. We present a model predictive control framework (MPC) for smart building control. The framework has several components, including (a) occupancy sensing in real time, (b) occupancy prediction models based on historical occupancy data, (c) thermodynamic building models, (d) weather forecasting data, and (e) a model predictive control optimization that tries to minimize monetary costs in energy use while maintaining quality comfort bounds for the building's users. We tested the system in a real building with over 40 workers, and we show that we could obtain monetary savings of more than 50% without sacrificing user comfort.

Buildings are often inefficiently conditioned. Rooms that are empty are needlessly conditioned and partially filled rooms are conditioned assuming maximum occupancy. In this demonstration, we describe a system that reduces energy consumption by opportunistically reducing energy consumption based on room usage; we only condition rooms currently occupied and condition the space based on real-time occupancy measurements. We will show how a thermal sensor array can be used measure occupancy in real-time and how this occupancy information can be integrated with a real building management system in order to control the heating, cooling, ventilation and lighting of a building to optimize energy usage.