Real-time temperature distribution reconstruction via linear parameter-varying state-space model and Kalman filter in rack-based cooling data centers

Rapidly growing data centers (DCs) are facing challenges with energy consumption increase and thermal environmental security. Thus, this paper proposed a novel real-time temperature distribution prediction and reconstruction model for rack-based cooling DCs, in order to facilitate efficient energy consumption and thermal environment management. The parameter identification method coupling with inherent physical relationships was employed to obtain the linear parameter-varying state-space model, rapidly and approximately predicting the nonlinear thermal dynamic processes within rack-based cooling DC. Then, Kalman filter was adopted to reconstruct the temperature distribution in real-time based upon the developed linear parameter-varying statespace temperature prediction model and measurement information. This study comprehensively analyzed the temperature estimation performance of the proposed model under various model input biases, different sensor numbers and layouts. The results show that the input biases have significant impacts on the accuracy of the temperature prediction model, e.g., & PLUSMN;5% bias in IT workload resulting in an increase of 0.6 times in the mean absolute error (MAE) of the temperature prediction. The proposed temperature reconstruction model reveals excellent performance: compared to the original temperature prediction model, the MAE was decreased by about 5%, 10%, and 13% for reconstruction scenarios using one, two, and three sensors, respectively.