Data driven modeling advancements for air temperature predictions in data centers

Abstract

While data center cooling energy usage optimization studies have been performed through computational fluid dynamics/heat transfer (CFD/HT), and heuristic methods, data driven modeling techniques are now also being used for these applications. This paper investigates the air temperature prediction capabilities of static artificial neural network (ANN), Gaussian progress regression (GPR), support vector regression (SVR), relevance vector machine (RVM), linear regression, and regression trees; and transient long-short term memory (LSTM), and nonlinear autoregressive neural network with external input (NARX)) data driven modeling frameworks. The static study compared various models and found that GPR provided the best results (average error of 0.56 °C), closely followed by the ANN and SVR (average error of 0.60 °C and 0.68 °C respectively) methods. The transient study compared models based on an experimental data set and found that NARX outperforms LSTM for normal operations (0.83 °C and 1.07 °C average error respectively), and that data driven models are able to provide relatively good predictions, even if the input variables are slightly outside the training domain.