Real-time temperature predictions via state-space model and parameters identification within rack-based cooling data centers

Current rack-based cooling architecture of data centers (DCs) is a promising method since it simplifies airflow distribution and provides fast cooling regulation. Real-time on-demand control of the cooling system is an effective way to improve its operational energy efficiency without sacrificing the thermal security of IT equipment. Accurate and fast temperature distribution prediction serves as one of the bases for ensuring the superior performance of advanced control algorithms. Thus, this study proposed a novel grey-box state-space model, to rapidly predict the dynamic temperature distribution for rack-based cooling DCs. Various heat transfer physics in the rack-based cooling DCs, including heat production caused by servers and heat movements by airflows, were modeled as a state-space structure using the zonal modeling approach. The coef-ficient matrices were identified through the prediction-error method (PEM), in order to avoid the extremely time-consuming process of obtaining accurate physical parameters regarding the sys-tem. This developed model was validated with an experimentally validated mechanistic model and computational fluid dynamics (CFD) simulations. Additionally, the impact of the prediction horizon's size and IT workload transient changes on the proposed model's prediction accuracy were investigated as well. Through simulation, the developed model achieves sufficient accuracy with an average root mean square error (RMSE) equal to 0.19 degrees C and less than 3% relative error for predicting 1-min dynamic temperature evolutions. Also, the developed model shows satisfying performances for the long-horizon prediction and transient scenario, which will facilitate advanced control techniques for DC cooling systems.

Automated summary

This study proposed a novel grey-box state-space model for rapidly predicting the dynamic temperature distribution of rack-based cooling data centers. Various heat transfer physics in the rack-based cooling were modeled as a state-space structure, and the coefficient matrices were identified through the prediction-error method. The developed model achieved sufficient accuracy for predicting dynamic temperature evolutions and showed satisfying performances for long-horizon prediction and transient scenarios.