Organizational and technological solutions for data centres construction under AI workloads: requirements, risks, and regulations

Introduction. The rapid growth of AI workloads has shifted the key constraints of data centres from floor space and air cooling to available connection power, heat removal capacity, and water infrastructure. AI-ready is becoming an inherent property of building design: decisions regarding liquid/immersion cooling schemes, heat recovery, redundancy, and commissioning programs must be made at early stages and formalized within design specifications and tolerances.

Materials and methods. A targeted review of publications was conducted, incorporating peer-reviewed papers and relevant standards (EN 50600, ASHRAE TC 9.9 guidelines, ISO/IEC 30134 series). Sources were thematically coded under the categories: “cooling/heat removal”, “power supply”, “water and ecology”, “site layout and configuration”, and “regulations and KPI”.
Analysis was performed as a mapping of “AI requirement → building solution → measurable parameter”, resulting in a correspondence matrix and a risk-mitigation framework structured as “risk → mitigation measure → KPI → acceptance method”. This approach effectively linked academic literature with practical engineering tolerances and testing procedures.

Results. The target architecture for high-density AI clusters requires implementation of liquid cooling circuits with readiness for immersion cooling, validated resilience of power supply against transient loads, establishment of water usage effectiveness (WUE) KPI on par with PUE, and design of interfaces for low-grade heat recovery. Efficiency is enhanced through modularity and factory-built modules, staged power ramp-up, and digital twin technologies. An integrated risk map and a regulatory compliance matrix are proposed, explicitly linking standards to design parameters and acceptance procedures.

Conclusions. “AI-readiness” emerges as the outcome of coordinated organizational and technological construction decisions. A management framework encompassing KPI budgets during design phase, a requirements-compliance matrix, and a stress-testing program ensures reproducibility of target metrics (PUE/WUE/ERE/ERF), control over schedule and cost, and accelerated achievement of designed computational capacity.

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