@article{35517,
  keywords = {Model predictive control (MPC), Building HVAC optimal control, Optimal control problem formulations comparison, Mixed integer nonlinear optimization},
  author = {Ettore Zanetti and Donghun Kim and David Blum and Rossano Scroccia and Marcello Aprile},
  title = {Performance comparison of quadratic, nonlinear, and mixed integer nonlinear MPC formulations and solvers on an air source heat pump hydronic floor heating system},
  abstract = {<p>There is a gap in literature on comparisons between different MPC optimal control formulations</p><p>and solver choices for the same building HVAC system. Mixed Integer Nonlinear (MINL) formu-<br />lations are rarely considered, despite being the most physically accurate way to represent HVAC</p><p>systems. This work compares several MPC formulations, including Quadratic, Nonlinear, and MINL,</p><p>applied to a case study building and investigates benefits and challenges of MINL MPCs from prac-<br />tical perspectives. Ten different MPC formulations were developed and implemented using Pyomo.</p><p>Then, a detailed emulator model was developed using open-source Modelica libraries and used<br />with BOPTEST to assess the performance of each MPC. Results show that convergence and control<br />switching behaviours of MINL MPCs are sensitive to formulations, initialization approaches, solver<br />selections, and solver parameters. Thus, they require significant effort for tuning. However, a very<br />well-tuned MINL MPC performed similarly to successful Nonlinear MPC formulations.</p>},
  year = {2022},
  journal = {Journal of Building Performance Simulation},
  pages = {1 - 19},
  month = {09/2022},
  issn = {1940-1493},
  url = {https://www.tandfonline.com/doi/full/10.1080/19401493.2022.2120631},
  doi = {10.1080/19401493.2022.2120631},
  language = {eng},
}