Artificial intelligence-enabled predictive energy saving planning of liquid cooling system for data centers

As significant sources of energy consumption and carbon emissions, data centers have become a focal point for improving energy efficiency worldwide. To address the challenges of high computational resource demands and limited adaptability of traditional prediction models to complex conditions, this paper proposes an artificial intelligence-enabled predictive energy saving planning based on the Transformer-GRU model for predicting coolant temperature in the liquid cooling system of data centers. By integrating the self-attention mechanism of the Transformer and the time-series prediction strengths of GRU, the model performs correlation analysis and feature extraction of key parameters to achieve high-precision predictions of coolant return temperature. Experimental results demonstrate the model's superior accuracy compared to traditional prediction models, achieving an MSE of 1.349, RMSE of 1.157, MAPE of 0.0244, and R2 of 81.07 %, significantly outperforming baseline models such as Transformer-LSTM (MSE = 1.355), Informer (MSE = 1.356), Reformer (MSE = 1.353), DeepAR (MSE = 1.385), LSTM (MSE = 1.351), GRU (MSE = 1.366), and CNN-GRU (MSE = 1.363). The model maintains high predictive accuracy under fluctuating environments and complex cooling conditions, effectively reducing the operational energy consumption of the liquid cooling system. This advancement not only enhances cooling efficiency but also drives data centers toward greater intelligence and sustainability. By leveraging realtime monitoring data and predictive control, the model dynamically optimizes cooling strategies, reducing coolant and energy usage while promoting sustainable resource utilization. Additionally, this study offers implementation insights for high-performance computing environments, laying the groundwork for future research on extending model capabilities and integrating multimodal data.