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 >

Accurate prediction of the internal temperature field in batteries is of critical importance for ensuring safe operation, extending battery lifespan, and ultimately accelerating the global transition to electric vehicles (EVs). Unsafe thermal conditions not only degrade battery performance but also hinder EV adoption. However, conventional 3D thermal models involve complex computations that struggle to meet the fast computational speed requirements of Battery Management Systems (BMS), and traditional neural network models are not accurate enough for temperature field prediction, especially for low-gradient fields. To achieve precise and rapid prediction of the internal battery temperature fi... More >