Predictive modeling of thermal parameters inside the raised floor plenum data center using Artificial Neural Networks

Data centers are large facilities housing numerous IT equipment and supporting infrastructure. Frequent variations in IT load, continuous removal/addition/replacement of IT equipment for business requirement, cooling equipment, air supply settings, design layout, etc. make Data centers dynamic. Such complexities lead to overcooling and increased energy consumption. 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 the 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 validated CFD data. The optimum delay for each case was found using cross-correlation between the input and output parameters of the ANN. The response of the multi-input multi-output ANN model was validated using R-value and mean square error (MSE). R-value for all the cases was approximately 0.99. This study recommends the use of ANN models for fast and accurate prediction of thermal parameters for real-time energy-efficient control of the data center system.