This thesis seeks to investigate the integration of machine learning techniques in the realm of Heating, Ventilation, and Air Conditioning (HVAC) systems. By harnessing the power of data-driven approaches, the research aims to optimize the performance, energy efficiency, and overall effectiveness of HVAC systems. Through a comprehensive analysis of relevant data sets and the implementation of advanced algorithms, this study endeavors to uncover novel insights and methodologies for enhancing HVAC system design, operation, and maintenance. Ultimately, this research aims to contribute to the development of sustainable and intelligent HVAC solutions that meet the evolving needs of modern buildings and occupants.

For info and inquiries (please attach a CV with exams): gianantonio.susto@unipd.it