Application of Data-driven Techniques for Thermal Management in Data Centers

Abstract

This thesis mainly addresses the problems of thermal management in data centers (DCs) through data-driven techniques. For thermal management, a temperature prediction model in the facility is very important, while the thermal modeling based on first principles in DCs is quite difficult due to the complicated air flow and heat transfer. Therefore, we employ multiple data-driven techniques including statistical methods and deep neural networks (DNNs) to represent the thermal dynamics. Then based on such data-driven models, temperature estimation and control are implemented to optimize the thermal management in DCs. The contributions of this study are summarized in the following four aspects: 1) A data-driven model constructed through multiple linear Autoregression exogenous (ARX) models is adopted to describe the thermal behaviors in DCs. On the basis of such data-driven model, an observer of adaptive Kalman filter is proposed to estimate the temperature distribution in DC. 2) Based on the data-driven model proposed in the first work, a data-driven fault tolerant predictive controller considering different actuator faults is developed to regulate the temperature in DC. 3) To improve the modeling accuracy, a deep input convex neural network (ICNN) is adopted to implement thermal modeling in DCs, which is also specifically designed for further control design. Besides, the algorithm of elastic weight consolidation (EWC) is employed to overcome the catastrophic forgetting in continual learning. 4) A novel example reweighting algorithm is utilized to enhance the robustness of ICNN against noisy data and avoid overfitting in the training process. Finally, all the proposed approaches are validated in real experiments or experimental-data-based simulations.