Simone Fantini
Development of a hybrid process for fault detection and diagnosis in HVAC systems integrating data-driven and knowledge-driven approaches.
Rel. Alfonso Capozzoli, Marco Savino Piscitelli. Politecnico di Torino, Master of science program in Energy And Nuclear Engineering, 2025
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Abstract
Despite significant progress in improving the energy efficiency of Italy’s building stock, the building sector remains the country’s largest consumer of electricity, accounting for approximately 40% of national demand. HVAC systems are responsible for nearly half of this consumption. In this context, faults in HVAC operation can further increase energy use, accelerate equipment degradation, and negatively affect occupant comfort and indoor environmental quality. To improve reliability and performance, Fault Detection and Diagnosis (FDD) systems are receiving growing attention as a strategy to support more efficient and resilient HVAC operations. Traditional FDD methods, primarily based on rule sets or physical models, remain prevalent but are often limited in flexibility and scalability across different systems.
In recent years, data-driven approaches have demonstrated strong potential by learning behavioral patterns directly from operational data
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