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

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...

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Veröffentlicht in:Buildings (Basel) Jg. 15; H. 18; S. 3298
Hauptverfasser: Michailidis, Panagiotis, Michailidis, Iakovos, Minelli, Federico, Coban, Hasan Huseyin, Kosmatopoulos, Elias
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.09.2025
Schlagworte:
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
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Abstract 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.
AbstractList 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.
Audience Academic
Author Michailidis, Panagiotis
Kosmatopoulos, Elias
Minelli, Federico
Michailidis, Iakovos
Coban, Hasan Huseyin
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  surname: Minelli
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  givenname: Hasan Huseyin
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  surname: Coban
  fullname: Coban, Hasan Huseyin
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  givenname: Elias
  orcidid: 0000-0002-3735-4238
  surname: Kosmatopoulos
  fullname: Kosmatopoulos, Elias
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SubjectTerms 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
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Title Model Predictive Control for Smart Buildings: Applications and Innovations in Energy Management
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