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The increasing global emphasis on sustainability and reducing carbonemissions is pushing governments and corporations to rethink their approach todata center design and operation. Given their high energy consumption andexponentially large computational workloads, data centers are prime candidatesfor optimizing power consumption, especially in areas such as cooling and ITenergy usage. A significant challenge in this pursuit is the lack of aconfigurable and scalable thermal data center model that offers an end-to-endpipeline. Data centers consist of multiple IT components whose geometricconfiguration and heat dissipation make thermal modeling difficult. This paperpresents PyDCM, a customizable Data Center Model implemented in Python, thatallows users to create unique configurations of IT equipment with custom serverspecifications and geometric arrangements of IT cabinets. The use of vectorizedthermal calculations makes PyDCM orders of magnitude faster (30 times) thancurrent Energy Plus modeling implementations and scales sublinearly with thenumber of CPUs. Also, PyDCM enables the use of Deep Reinforcement Learning viathe Gymnasium wrapper to optimize data center cooling and offers auser-friendly platform for testing various data center design prototypes.

PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability