Energy Audit on Campus Data Center for Digital Twin-Based Energy Efficiency

Abstract

The research was developed using digital twin techniques to predict thermal loads through real-time data on the HVAC system in the data center. The physical device system was digitalized using IoT (Internet of Things) technology, and through this technology, a digital space was created to represent the prediction model. Instrumentation for data acquisition and real-time monitoring systems was created using IoT techniques, as well as an analysis of the performance of the data center cooling system. The aim of this research was to obtain thermal load predictions for the data center energy system and then analyze them using the heat balance method to determine the ratio of thermal load to the performance (cooling capacity) of the existing data center cooling devices. This was done to determine the potential for energy savings. The average predicted thermal load was 30.66 kW/h on October 25, 2022, and 29.88 kW/h on October 26, 2022. Therefore, the heat balance value against the nominal cooling capacity of the installed cooling devices was 40.95% for PAC 1 and 49.21% for PAC 2.