On the interaction between personal comfort systems and centralized HVAC systems in office buildings

Abstract

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.

Using personal environmental comfort systems to mitigate the impact of occupancy prediction errors on HVAC performance

“…In this paper, we extend the work in [10] and in [12] by first analyzing the effect of prediction errors in occupancy and later exploring the benefits of PECs in mitigating (or minimizing) the influence of prediction errors on HVAC operations. Our study indicates that predictive control strategies make HVAC operations highly unreliable.…”

“…Unlike conventional centrally-controlled HVAC system, where people share the same set point temperature [31,32], PEC systems can meet the comfort requirements of all occupants, albeit at the cost of additional energy expenditure. Kalaimani et al [12] merged PEC with model predictive control to further minimize the HVAC energy consumption and maximize the user comfort.…”

“…Recently, Kalaimani et al [12] proposed a hybrid HVAC controller and combined MPC with a personal environmental control system. In the study, [12] used SPOTan off-the-shelf desktop fan/heater with local temperature sensing and a computercontrolled actuator to provide individual thermal comfort.…”

“…Recently, Kalaimani et al [12] proposed a hybrid HVAC controller and combined MPC with a personal environmental control system. In the study, [12] used SPOTan off-the-shelf desktop fan/heater with local temperature sensing and a computercontrolled actuator to provide individual thermal comfort. Assuming perfect prediction of occupancy and outside temperature, the study shows that combining MPC with SPOT is effective in reducing the total energy consumption by choosing appropriate thermal setbacks during the intervals of sparse occupancy.…”

“…Our contributions are as follows: 1 We present the design and development of a building thermal simulator that models conventional schedule-based, reactive occupancy-based, and predictive MPC-based HVAC controllers. 2 We extend the MPC-based control strategy proposed by Kalaimani et al [12] and allow PEC to react between any two consecutive states of the system. 3 We quantify the impact of occupancy prediction errors on two MPC-based control strategies -with and without PEC.…”