Model Predictive Control for Smart Buildings: Applications and Innovations in Energy Management

• Published in: Buildings (Basel) Vol. 15; no. 18; p. 3298
• Main Authors: Michailidis, Panagiotis, Michailidis, Iakovos, Minelli, Federico, Coban, Hasan Huseyin, Kosmatopoulos, Elias
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2025
• Subjects: Algorithms; Alternative energy sources; Benchmarks; building energy management; Buildings; Electric vehicle charging; Electric vehicles; Emission standards; Energy consumption; Energy efficiency; Energy industry; Energy management; Energy management systems; Energy storage; Energy use; Forecasting; Heuristic methods; HVAC; HVAC control; Interactive control; Machine learning; Middleware; model predictive control; Multiagent systems; Multiple objective analysis; Optimization; Optimization techniques; Predictive control; Real time; Renewable energy; Simulation methods; Smart buildings; smart homes; Solvers; Support systems; Thermal comfort; Trends; United Kingdom
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings15183298

The integration of advanced control strategies into building energy management systems (BEMS) is essential for achieving energy efficiency and sustaining thermal comfort. Model predictive control (MPC) has gained significant traction as a model-based approach capable of optimizing control actions by predicting future system behavior under dynamic conditions. The current review offers an in-depth analysis of MPC, combining its core theoretical foundations with a broad survey of impactful applications in buildings, for extracting key breakthroughs and trends that have defined the field over the past decade. Emphasis is placed on multiverse MPC configurations and their application across various BEMS frameworks integrating HVACs, energy storage, renewable energy, domestic hot water, electric vehicle charging, and lighting systems. A detailed evaluation of MPC key attributes is then conducted, based on essential aspects of MPC, such as algorithms, optimization solvers, baselines, performance indexes, and building types, as well as simulation tools that support system modeling and real-time validation. The study concludes by outlining key research trends and proposing future directions, with a strong emphasis on addressing real-world deployment challenges and advancing scalable, interoperable solutions on smart building ecosystems. According to the evaluation, MPC research is shifting from simple white-box setups to gray- and black-box models paired with metaheuristic or hybrid solvers, leveraging machine learning for forecasting and multi-objective optimization, but still lacking robustness, benchmarks, and real-world validation. Consequently, next-generation MPC is anticipated to evolve into adaptive, hybrid, and multi-agent frameworks that integrate forecasting and control, embed occupant behavior, enable grid-interactive flexibility, and support lightweight, explainable deployment in real building environments.