Abstract:
Data centers are large facilities housing numerous IT equipment and supporting infrastructure. The raised floor plenum data centers are highly dynamic because of cold-hot aisle arrangements and time-dependent server heat generation. To reduce the energy consumption of the data center, a real-time control framework based on various thermal parameters inside the data center is imperative. Accurate prediction of various variables affecting the thermal behavior of the data center, especially for small-time horizon, using computational fluid dynamics (CFD) simulations requires a large number of computational resources and physical time, making them unfeasible for real-time control of the data centers. Data-driven modeling especially, the Artificial Neural Networks (ANN) can be potentially helpful in such cases. This study aims to examine the ANN-based model with Multi-Layer Perceptron (MLP) to predict thermal variables such as rack air temperature inside data centers. The ANN-based models for the rack and facility-level system were trained and validated on the experiments and CFD data. Optimum delay for each case was found using cross-correlation between the input and output parameters of the ANN. The response of the ANN model was validated using R-value and mean square error (MSE). This study recommends the use of ANN models for fast and accurate prediction of thermal parameters to achieve real-time control of the data center system.