With the expansion of artificial intelligence, data center energy consumption has surged, making its optimization critical for achieving carbon neutrality. Traditional fixed-rule controls rely too heavily on manual experience to explore the highest efficiency, while standard AI black-box models often lack interpretability and present evaluation challenges. This study introduces a Bayesian optimization-based methodology to explore the energy optimization potential of data center cooling systems. We enhanced the Resource Allocation and Power Simulator (RAPS) to generate dynamic workloads reflecting the authentic statistical characteristics of supercomputers for rigorous stress testing. This ge... More >