In-situ sensor correction method for data center cooling systems using Bayesian Inference coupling with autoencoder

Abstract

• In-situ sensor correction method based on Bayesian inference and autoencoder. • Impacts of the system model accuracy on the correction performance. • A series of case studies were designed to fully demonstrate the performance. • Result shows that deviation rates of sensor measurements are decreased to 7.67%. The quality of measurements from working sensors has a considerable influence on the system operation and energy usage in data center cooling systems. However, due to the malfunction, aging, and installation positions, it is very common phenomenon that the measurements are far away from their true values. Virtual sensor correction is a promising solution to correct erroneous measurements. This study proposed a novel sensor correction method for data center cooling systems using the Bayesian Inference coupling with autoencoder (AE) to eliminate the sensor errors. The correction performance of the proposed method was comprehensively investigated under a series of single/multiple sensor error scenarios in a typical computer room air handler (CRAH) unit. Furthermore, the impact of the system model accuracy on the correction performance was also quantified. The results show the excellent correction accuracies of the proposed method in the data center cooling systems: following correction, the sensor deviation rates are decreased to 7.67% and 3.00% for the single and multiple simultaneous sensor error scenarios, respectively. Additionally, the sensor correction error increases to 18% when the deviation rate of the heat transfer coefficient is set to 10%.