## 2 State of the Art
### AI and Fuzzy Logic Applications in Smart Buildings  
Artificial intelligence (AI) has emerged as a transformative paradigm in the design and deployment of intelligent building systems, enabling context-aware, data-driven energy optimization strategies. Numerous studies have applied machine learning (ML) and deep learning (DL) techniques for occupancy prediction, HVAC optimization, and fault detection. For instance, reinforcement learning has been widely explored to dynamically manage HVAC operations while maintaining thermal comfort and minimizing energy use [31]. Similarly, convolutional, recurrent neural networks and Large Language Models (LLMs) have been used to forecast indoor environmental conditions and user behavior, contributing to anticipatory control strategies in smart buildings [32; 33]. AI-based anomaly detection has also proven effective in identifying irregular consumption patterns, system faults, and sensor failures [34].  
Despite the remarkable advances of AI in smart buildings, these models often operate as opaque black boxes, raising concerns about transparency, interpretability, and user trust [35]. In this context, explainable AI (XAI) techniques and hybrid approaches are being investigated to improve the traceability of control decisions. Fuzzy logic offers a compelling alternative or complement to purely data-driven methods by embedding expert knowledge into interpretable rule-based systems. The integration of fuzzy inference systems (FISs) with AI techniques is gaining attention as a powerful framework for handling uncertainty in decision-making processes, making it a suitable approach for optimizing energy efficiency in smart buildings [17; 19; 20].  
One of the primary applications of fuzzy logic in smart buildings is HVAC control, where fuzzy controllers adjust HVAC settings based on factors such as occupancy levels, indoor temperature, and external weather conditions. These systems have demonstrated energy savings of up to 30% while maintaining occupant comfort [36; 37]. Similarly, fuzzy logic-based lighting systems dynamically adjust brightness based on occupancy detection and daylight availability, leading to savings of approximately 38-47% [38].  
Fuzzy logic has also been integrated into adaptive ventilation control, where fuzzy controllers analyze indoor air quality parameters such as CO\({}_{2}\), humidity, and volatile organic compounds (VOCs) to optimize airflow dynamically [39]. This approach enhances DCV by ensuring fresh air supply while minimizing HVAC energy consumption. Additionally, hybrid fuzzy-ML models are being developed to improve occupancy detection and predictive control, combining fuzzy rule-based reasoning with machine learning algorithms [40].  
Another emerging area is the integration of fuzzy logic into security and fire monitoring systems. Fuzzy-based fire detection models analyze temperature, smoke, and gas concentration levels to improve response times and reduce false alarms [41]. Additionally, fuzzy logic has been employed in noise management strategies, enhancing soundproofing and active noise control in buildings [42].  
Despite its advantages, fuzzy logic applications in smart buildings face limitations [43; 44; 45]. Many implementations require extensive manual rule definition, making scalability challenging. Moreover, the computational complexity of large-scale FISs limits real-time processing on low-power IoT devices. To address these challenges, ongoing research focuses on hybrid approaches combining fuzzy logic with machine learning and edge computing to enhance adaptability and computational efficiency [46; 47].