Advanced Machine Learning Approaches for Fault Detection, Prognostics, and Optimization in Smart HVAC Systems

Abstract

The evolution of heating, ventilation, and air conditioning (HVAC) systems toward intelligent, data-driven frameworks has necessitated the integration of advanced machine learning methodologies for fault detection, diagnosis, prognostics, and system optimization. This research investigates contemporary and emergent techniques in smart HVAC operations, emphasizing convolutional neural networks, semi-supervised learning, transfer learning, and predictive control strategies. Through critical synthesis of literature, the study highlights the importance of interpretability, limited data handling, and occupant-centered adaptive controls in achieving enhanced energy efficiency and operational reliability. Key contributions include a detailed exposition of fault diagnosis frameworks that leverage 2-D convolutional neural networks for multizone HVAC environments (Elnour & Meskin, 2022), the utilization of semi-supervised and transfer learning paradigms for rooftop units and air-handling systems (Albayati et al., 2023; Martinez-Viol et al., 2022), and the integration of physiological and environmental signals for real-time adaptive control (Deng & Chen, 2020). The study also examines the economic and practical implications of early adoption of intelligent fault detection and predictive maintenance systems in commercial and residential contexts. Emphasis is placed on bridging the gap between high-fidelity data-driven models and real-world deployment constraints, including data scarcity, false alarms, and heterogeneous system configurations. The findings demonstrate the transformative potential of harmonizing IT/OT infrastructure in HVAC systems for achieving sustainable energy performance while maintaining occupant comfort. The research provides a comprehensive theoretical and applied framework for future developments in predictive HVAC control, energy optimization, and intelligent fault management.