  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.
