Development of an adaptive artificial neural network model and optimal control algorithm for a data center cyber–physical system

This study aimed to develop an adaptive artificial neural network model (AAM) for the prediction of the rack inlet temperature and cooling system energy, and the optimal control algorithm for cooling system of a containment-type data center. A cyber-physical system (CPS) framework, that incorporated the AAM and control algorithm, was also proposed for the precise control of the data center cooling system. To develop the AAM model and control algorithm, mathematical modeling of a reference physical model was conducted, and training data were acquired from this model. The performance of the proposed AAM and control algorithm was then compared with that of a non-adaptive ANN model (NAAM) in terms of prediction accuracy and control stability. The analysis results indicated that the optimal control algorithm with the AAM exhibited superior prediction accuracy and control stability than the algorithm with the NAAM. In particular, for the AAM-based algorithm under conditions representing a novel data center environment, the root mean square error (RMSE) and coefficient of variation of the RMSE (CV(RMSE)) for the predicted and actual values were 0.22 °C and 1.02%, respectively, for the inlet rack temperature and 0.19 kW and 0.76% for the cooling system energy. The control was also stable, with an MAE of 0.08 °C and a maximum error of 1.17 °C. Based on this analysis, a CPS-based control strategy incorporating an ANN-based optimal control algorithm is expected to be an effective energy efficiency solution for existing data center without changing IT equipment or cooling systems. •An adaptive ANN model and optimal control algorithm for data centers were proposed.•A CPS framework was also proposed for advanced data center control.•Comparison simulations were conducted for the performance evaluation.•The proposed work outperformed non-adaptive ANN model due to real-time training.