Abstract
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.
Abstract (translated)
我们提出了一个基于物理的建筑模拟仿真的数据驱动建模和控制框架。我们的方案包括:(a) offline 训练不同的替代模型,以加快模型评估的速度,提供廉价的梯度,并在模型预测控制(MPC)中提高未来预测精度,以及(b) 设计和解决非线性建筑热工控制(HVAC) MPC 问题。我们广泛验证建模和控制性能,使用多个替代模型和在建筑优化测试框架(BOPTEST)中不同的可用测试案例的优化框架,对不同的测试案例进行优化。框架与其他建模技术兼容,可以定制不同的控制 formulation。模块化使该方法适用于目前正在开发的物理建筑模拟仿真测试案例,并为大型建筑的原型预测控制器提供了一条道路。
URL
https://arxiv.org/abs/2301.13447
