Improving the energy-efficiency of heating, ventilation, and air-conditioning (HVAC) systems has the potential to realize large economic and societal benefits. This paper concerns the system identification of a hybrid system model of a building-wide HVAC system and its subsequent control using a hybrid system formulation of learning-based model predictive control (LBMPC). Here, the learning refers to model updates to the hybrid system model that incorporate the heating effects due to occupancy, solar effects, outside air temperature (OAT), and equipment, in addition to integrator dynamics inherently present in low-level control. Though we make significant modeling simplifications, our corresponding controller that uses this model is able to experimentally achieve a large reduction in energy usage without any degradations in occupant comfort. It is in this way that we justify the modeling simplifications that we have made. We conclude by presenting results from experiments on our building HVAC testbed, which show an average of 1.5MWh of energy savings per day (p = 0.002) with a 95% confidence interval of 1.0MWh to 2.1MWh of energy savings.
We report on an experimental case study of personalized lighting controls built on top of an infrastructure designed to enable rapid development of applications in commercial buildings. Our personalized lighting controls (PLC) use an existing standard commercial building lighting automation system and require no new hardware to deploy. PLC presents occupants with a "shared virtual light switch" accessible online and easily viewable on smart phones by scanning a QR code. It embodies three important design principles: individual empowerment with localized human-centered resolution, token effort for energy consumption and return to a low-power state when inactive. After deploying our lighting controls on two new floors of a large research building on campus, we show a sustainable reduction in lighting energy of 50% to 70% on both floors over 12 weeks, continuing to this day. These savings are found to come from a combination of reducing brightness and keeping lights on less often, especially during evenings and weekends.
Many commercial buildings have digital controls and extensive sensor networks that can be used to develop novel applications for saving energy, detecting faults, improving comfort, etc. However, buildings are custom designed, leading to differences in functionality, connectivity, controls and operation. As a result today's building applications are hard to write and non-portable. What is required is a form of mass customization that allows applications to automatically adapt to differences in buildings.We present BAS, an application programming interface and runtime for portable building applications. BAS provides a fuzzy query interface allowing application authors to describe the building components they require in terms of functional and spatial relationships. The resulting queries implicitly handle multiple building designs. BAS also incorporates a hierarchical driver model, exposing common functions of building components through standard interfaces.We demonstrate and evaluate BAS by implementing two novel applications -an occupant HVAC control app and a ventilation optimization app -on two different buildings using raw building control protocols and then again using BAS. We show that the BAS code is much shorter, easier to understand and does not change for each building.
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