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Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study

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Experimental testbed uncovers how causal ML improves heat recovery in data centers, suggesting better sustainability practices.

Causal machine learning enhances predictions for optimizing energy use and heat recovery in data centers, aiming for sustainability.
Experimental results reveal that causal ML can outperform traditional approaches in modeling interventions for data center operations.
A physical testbed of a miniature data center enabled controlled experiments, collecting real data for evaluating machine learning methods.
The study highlights significant strengths and limitations of causal and conventional machine learning in improving energy efficiency.

Gonzalez et al. (2025) studied this question.

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View Full Paper

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Authors

Experimental testbed uncovers how causal ML improves heat recovery in data centers, suggesting better sustainability practices.

Causal machine learning enhances predictions for optimizing energy use and heat recovery in data centers, aiming for sustainability.
Experimental results reveal that causal ML can outperform traditional approaches in modeling interventions for data center operations.
A physical testbed of a miniature data center enabled controlled experiments, collecting real data for evaluating machine learning methods.
The study highlights significant strengths and limitations of causal and conventional machine learning in improving energy efficiency.

Gonzalez et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark