From theory to practice: A critical review of model predictive control field implementations in the built environment

While the potential of model-based predictive control (MPC) to improve building operation is widely acknowledged, its implementation has not yet become a mainstream practice in the building operation industry. This review paper explores the scientific literature documenting MPC field implementations in actual buildings. The goal is twofold: (a) to identify critical features in the deployment of MPC strategies, including the targeted building types and applications, systems controlled, expected benefits, software used, as well as common issues encountered (and successful measures to overcome these issues); (b) to evaluate the benefits of MPC based on the reported information from real-life implementations. Aspects analyzed include drivers and energy contexts, control-oriented modelling approaches, optimization routines, and performance evaluation methods. Results show that most practical studies focussed on buildings with a floor area under 10,000 m², often even less than 1000 m². MPC applications were varied, ranging from setpoint tracking and building conditioning to the optimization of the operation of thermal energy storage and photovoltaic panels and/or battery systems. MPC consistently yields significant benefits, with average savings of 30 % for thermal energy, 25 % for electricity use, 25 % for energy costs, 26 % for peak power and 17 % for GHG emissions, obtained under an average field-testing duration of 41 days.