Modelling the Temperature of a Data Centre Cooling System Using Machine Learning Methods

Adam Kula, Daniel Dąbrowski, Marcin Blachnik (), Maciej Sajkowski, Albert Smalcerz and Zygmunt Kamiński
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Adam Kula: Joint Doctorate School, Department of Industrial Informatics, Faculty of Materials Engineering, Silesian University of Technology, Krasińskiego 8, 40-019 Katowice, Poland
Daniel Dąbrowski: Department of Industrial Informatics, Faculty of Materials Engineering, Silesian University of Technology, Krasińskiego 8, 40-019 Katowice, Poland
Marcin Blachnik: Department of Industrial Informatics, Faculty of Materials Engineering, Silesian University of Technology, Krasińskiego 8, 40-019 Katowice, Poland
Maciej Sajkowski: Department of Industrial Informatics, Faculty of Materials Engineering, Silesian University of Technology, Krasińskiego 8, 40-019 Katowice, Poland
Albert Smalcerz: Department of Industrial Informatics, Faculty of Materials Engineering, Silesian University of Technology, Krasińskiego 8, 40-019 Katowice, Poland
Zygmunt Kamiński: KAMSOFT S.A., 1 Maja 133, 40-235 Katowice, Poland

Energies, 2025, vol. 18, issue 10, 1-24

Abstract: Reducing the energy consumption of a data centre while maintaining the requirements of the compute resources is a challenging problem that requires intelligent system design. It even becomes more challenging when dealing with an operating data centre. To achieve that goal without compromising the working conditions of the compute resources, a temperature model is needed that estimates the temperature within the hot corridor of the cooling system based on the properties of the external weather conditions and internal conditions such as server energy consumption, and cooling system state. In this paper, we discuss the dataset creation process as well as the process of evaluating a model for forecasting the temperature in the warm corridor of the data centre. The proposed solution compares two new neural network architectures, namely Time-Series Dense Encoder (TiDE) and Time-Series Mixer (TSMixer) with classical methods such as Random Forest and XGBoost and AutoARIMA. The obtained results indicate that the lowest prediction error was achieved by the TiDE model allowing to achieve 0.1270 of N-RMSE followed by the XGBoost model with 0.1275 of N-RMSE. The additional analysis indicates a limitation of the use of the XGBoost model which tends to underestimate temperature as it approaches higher values, which is particularly important in avoiding safety conditions violations of the compute units.

Keywords: machine learning; data centre; energy consumption; intelligent system; smart buildings (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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