Energy Audit on Campus Data Center for Digital Twin-Based Energy Efficiency
Keywords:
digital twin, data center, HVAC, heat balance, energy managementAbstract
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.
References
E. Oró, V. Depoorter, A. Garcia and J. Salom, “Energy Efficiency and Renewable Energy Integration in Data Centres. Strategies and Modelling Review”, Renewable and Sustainable Energy Reviews, Vol. 42, pp. 429-445, 2015.
B. Whitehead, D. Andrews, A. Shah and G. Maidment “Assessing the Environmental Impact of Data Centres Part 1: Background, Energy Use and Metrics”, Building and Environment, Vol 82, pp 151-159, 2014.
M. Dayarathna, Y. Wen and R. Fan, “Data Center Energy Consumption Modeling: A Survey”, IEEE Communications Surveys & Tutorials, Vol. 18, 2016.
https://www.iea.org/data-and-statistics/charts/global-data-centre-energy-demand-by-data-centre-type-2010-2022 (accessed: 16 March 2023) .
M. Koot, and F. Wijnhoven, “Usage Impact on Data Center Electricity Needs: A System Dynamic Forecasting Model,” Applied Energy, vol 291, 116798, 2021.
B. Hadid, S. Lecoeuche, D. Gille, and C. Labarre, “Energy Efficiency of Data Centers: A Data-Driven Model-Based Approach”, IEEE International Energy Conference (ENERGYCON), pp. 1-6, 2016.
Y. Weiping, Z. Wang, Y. Xue, L. Guo and L. Xu, “A Combined Neural and Genetic Algorithm Model for Data Center Temperature Control”, Science and Technology Program of State Grid, 2017.
R. Snijders, P. Pileggi, J. Broekhuijsen, J. Verriet, M. Wiering and K. Kok, "Machine Learning for Digital Twins to Predict Responsiveness of Cyber-Physical Energy Systems," 8th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems, Sydney, NSW, Australia,pp. 1-6, 2020.
D. Jones, C. Snider, A. Nassehi, J. Yon, and B. Hicks, “Characterizing the Digital Twin: A Systematic Literature Review,” CIRP J. Manuf. Sci. Techno.l, vol. 29, pp 36-52, 2020.
A. Fuller, Z. Fan, C. Day, and C. Barlow, “Digital Twin: Enabling Technologies, Challenges and Open Research,” IEEE Access, vol. 8, pp. 108952-108971, 2020.
A. Rasheed, O. San and T. Kvamsdal, "Digital Twin: Values, Challenges and Enablers from a Modeling Perspective," IEEE Access, vol. 8, pp. 21980-22012, 2020.
R. Stark and T. Damerau, “Digital Twin”. in CIRP Encyclopedia of Production Engineering , Chatti, S., Tolio, T. (eds), Springer, pp. 1–8, 2019.
X. Xie, Q. Lu, A.K. Parlikad, and J. Schooling, “Digital Twin Enabled Asset Anomaly Detection for Building Facility Management,” IFAC PaperOnLine, vol.53(3), pp. 380-385, 2020.
E. Vanderhorn and S. Mahadevan, “Digital Twin: Generalization, Characterization, and Implementation,” Decision Support Systems, Vol. 145, no. 113524, pp. 0167-9236, 2021.
D. Jones, C. Snider, A. Nassehi, J. Yon, and B. Hicks, “Characterizing the Digital Twin: A Systematic Literature Review,” CIRP Journal of Manufacturing Science and Technology, vol. 29, pp. 36-52, 2020.
W. Kritzinger, M. Karner, G. Traar, J. Henjes, and W. Sihn, “Digital Twin in Manufacturing : A Categorical Literature Review And Classification,” IFAC-PapersOnLine, vol. 51, pp. 1016-1022, 2018.
T. Bergs, S. Gierlings, T. Auerbach, A. Klink, D. Schraknepper, and T. Augspurger, “The Concept of Digital Twin and Digital Shadow in Manufacturing,” Procedia CIRP, vol. 101, pp. 81-84, 2021.
M. Grieves and J. Vickers, “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems,” in Transdisciplinary Perspectives on Complex Systems : Springer, pp. 85–113, 2017.
Published
How to Cite
Issue
Section
Copyright (c) 2023 Rizal Faris Mustaram, Teguh Solavide Gulo, Edi Leksono, Justin Pradipta

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
An author who publishes in the Jurnal Otomasi Kontrol dan Instrumentasi agrees to the following terms:
- The author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-ShareAlike 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal
- Author can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.