We present a data-driven modeling and control framework for physics-based building emulators. Our approach comprises: (a) Offline training of differentiable surrogate models that speed up model evaluations, provide cheap gradients, and have good predictive accuracy for the receding horizon in Model Predictive Control (MPC) and (b) Formulating and solving nonlinear building HVAC MPC problems. We extensively verify the modeling and control performance using multiple surrogate models and optimization frameworks for different available test cases in the Building Optimization Testing Framework (BOPTEST). The framework is compatible with other modeling techniques and customizable with different control formulations. The modularity makes the approach future-proof for test cases currently in development for physics-based building emulators and provides a path toward prototyping predictive controllers in large buildings.

翻译：我们提出了一种针对基于物理的建筑仿真器的数据驱动建模与控制框架。该方法包含以下两部分：（a）离线训练可微分的代理模型，该模型能够加速模型评估、提供廉价梯度，并在模型预测控制（MPC）的滚动时域内具有良好的预测精度；（b）构建并求解非线性建筑暖通空调MPC问题。我们利用建筑优化测试框架（BOPTEST）中多个可用测试案例，通过多种代理模型与优化框架对建模与控制性能进行了广泛验证。该框架兼容其他建模技术，并支持不同的控制公式定制。其模块化设计使该方法能够适应当前正在开发的基于物理的建筑仿真器测试案例，并为大型建筑中预测控制器的原型设计提供了一条可行路径。