  We present a solution for modeling and online identification for heating,
ventilation, and air conditioning (HVAC) control in buildings. Our approach
comprises: (a) a resistance-capacitance (RC) model based on first order energy
balance for deriving the zone temperature dynamics, and (b) a neural network
for modeling HVAC dynamics. State estimation and model identification are
simultaneously performed using nonlinear moving horizon estimation (MHE) with
physical constraints for system states. We leverage the identified model in
model predictive control (MPC) for occupant comfort satisfaction and HVAC
energy savings and verify the approach using simulations. Our system relies
only on building management system data, does not require extensive data
storage, and does not require a detailed building model. This can significantly
aid the large scale adoption of MPC for future occupant-centric control of
grid-interactive buildings.
