Data Center Cooling Control Using Causal Learning Signals

The system performs preliminary actions by injecting randomized control signals and collecting operational data proactively before optimization is needed. This allows the causal model to be pre-trained on diverse operational conditions, reducing the need for extensive retraining when infrastructure changes occur. The causal model learns relationships between control settings and outcomes in advance, enabling faster adaptation to new equipment configurations.

The patent introduces a causal model as an intermediary that learns fundamental relationships between control signals and operational outcomes. This causal model serves as a mediator between raw operational data and optimization decisions, requiring significantly less training data than traditional predictive models. The causal model captures invariant relationships that transfer across different data center configurations, reducing the need for years of operational data while maintaining high energy management efficiency.

The system implements dynamics by continuously updating the causal model with new operational data and injecting randomized exploration signals. This allows the model to adapt dynamically to infrastructure changes such as equipment refreshes or reconfigurations. The causal model's structure enables it to learn new relationships while retaining knowledge of fundamental causal mechanisms, preventing catastrophic forgetting and maintaining reliability across different infrastructure configurations.