A stochastic optimization framework for AI-driven commissioning processes in data centers: Enhancing lifecycle efficiency and cost reduction

Odinaka, Nnadozie and Dillum, Martin and Wash-Anigboro, Oghnetega Deborah (2025) A stochastic optimization framework for AI-driven commissioning processes in data centers: Enhancing lifecycle efficiency and cost reduction. International Journal of Science and Research Archive, 16 (1). pp. 567-574. ISSN 2582-8185

This study introduces a new AI-driven framework for commissioning hyperscale data centers, replacing traditional checklist methods with a more efficient, autonomous process. By integrating Bayesian optimization with a real-time digital twin, the system dynamically plans and adjusts performance tests, aiming to maximize efficiency while minimizing cost and risk. The approach uses Gaussian-process models to update its understanding as data is collected, enabling smarter decisions with less testing. Results show significant benefits over conventional methods, including 15–25% faster commissioning, lower upfront costs, and 8–12% energy savings over the data center facility’s lifetime. The proposed framework therefore offers a scalable, AI-driven pathway to accelerate deployment, cut costs, and embed continuous optimization capabilities from day one in modern data-center infrastructure.