  Model predictive control (MPC) is a widely used technique for temperature
set-point tracking and energy optimization of Heating Ventilation and Air
Conditioning (HVAC) systems in buildings. Unfortunately, a nonlinear thermal
building model leads to a computationally expensive nonlinear MPC problem that
is not suitable for real-time control and optimization. This paper presents a
novel approximate linearized model for building's thermal dynamics and the HVAC
system power consumption that leads to a computationally efficient linear model
predictive controller (LMPC) for the buildings' HVAC systems. We employ
feedback linearization technique to obtain an equivalent linearized model for
the nonlinear thermal building dynamics and use constraint mapping approach to
obtain a linearized formulation for new control variables. Next, using
piecewise linearization, we obtain a linearized analytical model for the HVAC
system power consumption. The proposed LMPC technique is validated using
multiple simulation case studies. We demonstrate that the proposed LMPC
technique is not only computationally efficient but also accurately
approximates the nonlinear optimal control decisions.
