Learning and Model Predictive Control Applied to Energy Optimization of Chiller Plants for Data Centers
Buildings are responsible for approximately 30% of global energy use, with HVAC systems accounting for 40% of that demand. Among these, data centers have become significant energy consumers, requiring highly efficient and stable cooling solutions. This project aims to explore data-driven control methods to improve the energy efficiency of data center cooling systems as well as reduce the effort an
