Catherine Rosenberg

Rachel Kalaimani, Milan Jain, Srinivasan Keshav et al.

Most modern HVAC systems suffer from two intrinsic problems. First, inability to meet diverse comfort requirements of the occupants. Second, heat or cool an entire zone even when the zone is only partially occupied. Both issues can be mitigated by using personal comfort systems (PCS) which bridge the comfort gap between what is provided by a central HVAC system and the personal preferences of the occupants. In recent work, we have proposed and deployed such a system, called SPOT. We address the question, "How should an existing HVAC system modify its operation to benefit the availability of PCS like SPOT?" For example, energy consumption could be reduced during sparse occupancy by choosing appropriate thermal set backs, with the PCS providing the additional offset in thermal comfort required for each occupant. Our control strategy based on Model Predictive Control (MPC), employs a bi-linear thermal model, and has two time-scales to accommodate the physical constraints that limit certain components of the central HVAC system from frequently changing their set points. We compare the energy consumption and comfort offered by our SPOT-aware HVAC system with that of a state-of-the-art MPC-based central HVAC system in multiple settings including different room layouts and partial deployment of PCS. Numerical evaluations show that our system obtains, in average, 45% (15%) savings in energy in summer (winter), compared with the benchmark system for the case of homogeneous comfort requirements. For heterogeneous comfort requirements, we observe 51% (29%) improvement in comfort in summer (winter) in addition to significant savings in energy.

Milan Jain, Rachel K Kalaimani, Srinivasan Keshav et al.

Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive control (MPC) is one state of the art optimization technique for HVAC control which converts the control problem to a sequence of optimization problems, each over a finite time horizon. In a typical MPC, future system state is estimated from a model using predictions of model inputs, such as building occupancy and outside air temperature. Consequently, as prediction accuracy deteriorates, MPC performance--in terms of occupant comfort and building energy use--degrades. In this work, we use a custom-built building thermal simulator to systematically investigate the impact of occupancy prediction errors on occupant comfort and energy consumption. Our analysis shows that in our test building, as occupancy prediction error increases from 5\% to 20\% the performance of an MPC-based HVAC controller becomes worse than that of even a simple static schedule. However, when combined with a personal environmental control (PEC) system, HVAC controllers are considerably more robust to prediction errors. Thus, we quantify the effectiveness of PECs in mitigating the impact of forecast errors on MPC control for HVAC systems.

Yuan Quan, Haseen Rahman, Catherine Rosenberg

This paper studies radio resource management (RRM) for the uplink of a multi-channel cellular system with hybrid beamforming based on analog beamforming using predefined codebooks and zero-forcing digital beamforming. We first formulate a per-time slot joint RRM optimization problem, which includes beam selection, user selection, power allocation, modulation and coding scheme selection, and digital beamforming. A per-time slot formulation of the RRM problem is necessary because the power budget of a user equipment (UE) needs to be allocated per time slot to its assigned channels which are not known a priori, and because we consider the case where the number of radio frequency chains is not large enough to select all possible analog beams, thereby requiring per-slot beam selection. This problem can be solved for at most a few UEs because the number of variables grows exponentially with the number of UEs. In order to obtain results with more UEs, we propose an offline heuristic that reduces the runtime to obtain results by two orders of magnitude, while achieving performance close to the joint optimization. This offline heuristic allows us to obtain engineering insights on the impact of different system parameters as well as a target performance that we use to validate the low-complexity online heuristic that we propose.

D. Fooladivanda, G. Mancini, S. Garg et al.

A mathematical state-of-charge evolution equation is present for Flywheel Energy Storage Systems.