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Results are presented from a nearly week-long experimental evaluation of a scalable control algorithm for a commercial building HVAC system based on real-time measurements of occupancy obtained from motion detectors. The control algorithm decides air flow rate and amount of reheat for each variable air volume terminal box based on real-time measurements of occupancy and space temperature. It is a rule-based controller, so the control computations are simple. The experiments showed that the proposed controller resulted in 37% energy savings over baseline on average without sacrificing indoor climate. In contrast to prior work that reports energy savings without a careful measure of the effect on indoor climate, it is verified that the controller indeed maintains indoor climate as well as the building's baseline controller does. This verification is performed from measurements of a host of environmental variables and analysis of before/after occupant survey results. A complete system required to retrofit existing buildings with the controller is presented, which includes a wireless sensor network and a software execution platform. Two useful observations from this work are: (i) considerable energy savings-along with compliance with ASHRAE ventilation standards-are possible with simple occupancy-based control algorithms that are easy to retrofit; and (ii) these savings are attained with binary occupancy measurements from motion detectors that do not provide occupancy-count measurements. Results also show that there is a large variation in energy savings from zone to zone and from day to day.

We propose a method that simultaneously identifies a dynamic model of a building's temperature in the presence of large, unmeasured disturbances, and a transformed version of the unmeasured disturbance. Our method uses 1-regularization to encourage the identified disturbance to be approximately sparse, which is motivated by the piecewise constant nature of occupancy that determines the disturbance. We test our method using both open-loop and closed-loop simulation data. Results show that the identified model can accurately identify the transfer functions in both scenarios, even in the presence of large disturbances, and even when the disturbance does not satisfy the piecewise constant property.

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