Multi-Agent Systems and Digital Twins as a Basis for Smart Buildings with Integrated Sustainable Efficient Ventilation

Ventilation management is a key component of smart building performance, directly affecting indoor air quality, occupant comfort, and energy consumption during operation. The increasing complexity of building systems and variability in occupancy and environmental conditions challenge conventional static or centralised ventilation strategies. This study presents a conceptual and methodological framework for intelligent ventilation management based on the integration of distributed environmental sensorisation, multi-agent systems, and digital twins. The proposed approach focuses on structuring the architecture and decision-making mechanisms that enable adaptive and predictive ventilation strategies, including multi-source air intake selection (6D ventilation). Rather than providing experimental or simulation-based validation, the study defines a coherent framework intended to support future quantitative evaluation and implementation. The expected benefits of the approach, in terms of improved energy efficiency, indoor environmental quality, and life-cycle performance, are discussed in relation to existing research. The framework contributes to the development of smart buildings by providing a structured basis for advanced, adaptive, and sustainable ventilation management.