  The large amount of data collected in buildings makes energy management
smarter and more energy efficient. This study proposes a design and
implementation methodology of data-driven heating, ventilation, and air
conditioning (HVAC) control. Building thermodynamics is modeled using a
symbolic regression model (SRM) built from the collected data. Additionally, an
HVAC system model is also developed with a data-driven approach. A model
predictive control (MPC) based HVAC scheduling is formulated with the developed
models to minimize energy consumption and peak power demand and maximize
thermal comfort. The performance of the proposed framework is demonstrated in
the workspace in the actual campus building. The HVAC system using the proposed
framework reduces the peak power by 16.1\% compared to the widely used
thermostat controller.
