ARTIFICIAL NEURAL NETWORK BASED PREDICTION AND COOLING ENRGY OPTIMIZATION OF DATA CENTERS

ARTIFICIAL NEURAL NETWORK BASED PREDICTION AND COOLING ENRGY OPTIMIZATION OF DATA CENTERS

ABSTRACT

Thermal management of data centers remains a challenge because of their everincreasing power densities and decreasing server footprints. Current lack of dynamic control over global provisioning and local distribution of cooling resources often result in wasteful overcooling. These trends motivate this thesis research, which focuses on the development of a reliable and energy-efficient framework for allocating cooling resources to meet thermal management requirements, while minimizing energy consumption and adverse environmental impacts. A key component of energy-efficient thermal management is real-time accurate prediction of temperature distribution in data centers. This first section of this dissertation focuses on development and comparison of four Data Driven Modeling (DDM) methods, namely Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gaussian Process Regression (GPR) and Proper Orthogonal Decomposition (POD). These DDM methods were trained on datasets generated from offline Computational Fluid Dynamics/Heat Transfer (CFD/HT) simulations for real-time prediction of temperature and airflow distributions in a data center. Using CFD simulation results to train DDMs transfers computational complexity from model execution (in CFD) to model setup and development. To generate the training data, a physics-based and experimentally validated room-level CFD/HT model was developed using the commercial software Future Facilities 6Sigma Room.

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