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

arXiv:2301.13447v2 Announce Type: replace-cross
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 …

abstract accuracy algorithms arxiv benchmarking building control cost cs.ai cs.lg cs.sy data data-driven differentiable eess.sy framework good horizon modeling mpc offline optimization physics predictive testing training type