  We present a data-driven modeling and control framework for physics-based
building emulators. Our approach consists of: (a) Offline training of
differentiable surrogate models that accelerate model evaluations, provide
cost-effective gradients, and maintain good predictive accuracy for the
receding horizon in Model Predictive Control (MPC), and (b) Formulating and
solving nonlinear building HVAC MPC problems. We extensively evaluate the
modeling and control performance using multiple surrogate models and
optimization frameworks across various test cases available in the Building
Optimization Testing Framework (BOPTEST). Our framework is compatible with
other modeling techniques and can be customized with different control
formulations, making it adaptable and future-proof for test cases currently
under development for BOPTEST. This modularity provides a path towards
prototyping predictive controllers in large buildings, ensuring scalability and
robustness in real-world applications.
