Benchmarking Model Predictive Control Algorithms in Building Optimization Testing Framework (BOPTEST)

Abstract

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.

References in corpus (4)

• Deep Learning in Neural Networks: An Overview
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Benchmarking Model Predictive Control Algorithms in Building Optimization Testing Framework (BOPTEST)
• Nonlinear Moving Horizon Estimation and Model Predictive Control for Buildings with Unknown HVAC Dynamics

Cited by in corpus (1)

• Benchmarking Model Predictive Control Algorithms in Building Optimization Testing Framework (BOPTEST)