International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 1, February 2023, pp. 134~143 ISSN: 2088-8708, DOI: 10.11591/ijece.v13i1.pp134-143  134 Journal homepage: http://ijece.iaescore.com A constrained model predictive control for the building thermal management with optimal setting design Noureddine Boutchich, Ayoub Moufid, Najib Bennis EODIC Team Research, STIS Center, ENSAM of Rabat, Mohammed V University, Rabat, Morocco Article Info ABSTRACT Article history: Received Dec 25, 2021 Revised Aug 8, 2022 Accepted Aug 20, 2022 Today, the building sector is the most important consumer of energy. The main challenge in building management is to obtain the desired performance taking into account many aspects such as comfort requirements, variation of building physical characteristics, system constraints, and energy management. For this purpose, a predictive control approach applied to the building thermal has been designed to achieve desired performances combined with an energy optimization approach based on intrinsic system parameters. The developed approach is applied with an online identification system for effective predictive control to take into account the reel building characteristics and to choose the optimal tuning parameters. The simulation results show good performances in terms of accuracy and robustness face to internal and external disturbances with respect to system constraints. Keywords: Building Model predictive control constraints Thermal control Tuning parameters This is an open access article under the CC BY-SA license. Corresponding Author: Noureddine Boutchich EODIC Team Research, STIS Center, ENSAM of Rabat, University Mohammed V ENSAM, B.P 6207 Avenue of the Royal Armed Forces, Rabat 10100, Morocco Email: noureddine_boutchich@um5.ac.ma 1. INTRODUCTION The building sector is the most important consumer of energy. It accounts for approximately 40% of the worldwide and contributes over 30% of the CO2 emissions, more than 50% of this energy used in buildings is dedicated to cooling, heating, and ventilation [1], [2]. In order to reduce CO2 emissions and energy consumption, several solutions for building thermal have been developed over the past decades by improving the physical efficiency of construction materials to help reduce energy demand and developing decentralized production solutions to ensure the energy need [3]. Many controls and conventional approaches have been designed to meet optimization and thermal regulation needs. Proportional integral derivative (PID) control strategy remains an efficient tool in regulation [4], but its limitations are mainly related to the conceding of system constraints and predicting the systems events such as intermittent occupancy and unexpected climate variations. Other intelligent approaches have been proposed within the literature like fuzzy logic or neural networks [5]–[7], aimed toward reducing energy consumption while maintaining the specified performances, but they are computationally complex. The complexity of the problem is mainly due to the building being controlled whose thermal and physical behavior depends on various factors. Indeed, the building is subject to intrinsic factors such as the surface to be heated, the insulation characteristics which undergo variations due to natural degradation, etc. uncontrollable extrinsic factors such as meteorology, solar flux, thermal contributions due to the presence of individuals impacting the thermal behavior of the building. Furthermore, given recent rises in global temperatures as a result of climate change, it has become increasingly vital to offer acceptable comfort levels in indoor spaces, allowing for the growth of research in heating and cooling regulation and control. Model predictive control (MPC) has been one of the potentials for control schemes strategies to address these