Prediction of Air-Conditioning Outlet Temperature in Data Centers Based on Graph Neural Networks

This study addresses the issue of excessive cooling in data center server rooms caused by the sparse deployment of server cabinets. A precise air-conditioning control strategy based on the working temperature response of target cabinets is proposed. CFD software is used to establish the server room model and set control objectives. The simulations reveal that, under the condition of ensuring normal operation and equipment safety in the data center, the supply air temperature of the CRAC (computer room air conditioner) system can be adjusted to provide more flexibility, thereby reducing energy consumption. Based on this strategy, the dynamic load of the server room is simulated to obtain the supply air temperature of the CRAC system, forming a simulation dataset. A graph structure is created based on the distribution characteristics of the servers, and a regression prediction model for the supply air temperature of the CRAC system is trained using graph neural networks. The results show that, in the test set, 95.8% of the predicted supply air temperature errors are less than 0.5 °C, meeting ASHRAE standards. The model can be used to optimize the parameter settings of CRAC systems under real load conditions, reducing local hotspots in the server room while achieving energy-saving effects.