Modeling Data Center Temperature Profile in Terms of a First Order Polynomial RBF Network Trained by Particle Swarm Optimization

In this paper a polynomial radial basis function neural network is trained to model and predict the temperature profile-energy proxy of a highly complex data center located at the University of the Aegean, Greece. A number of input variables are identified that directly quantify the rack’s air temperature. The corresponding data set is generated through an experimental monitoring system used over a two-week period. The network’s structure encompasses three distinct levels. The first level involves a number of hidden nodes with Gaussian activation functions, while the second level generates first order polynomial functions of the input variables. Finally, the third level aggregates the outputs of the above two levels and generates the network’s output. The network’s training process is based on using the particle swarm optimization algorithm. For comparative reasons, a typical radial basis function and a feed-forward network were developed. The results indicate that the proposed network is very effective in predicting the server rack’s air temperature, outperforming the other two networks.