Open Access

ARTICLE

AI-Based Power Distribution Optimization in Hyperscale Data Centers

Chirag Devendrakumar Parikh^*

Computer Engineering, California State University, Fullerton, CA 92831, USA

* Corresponding Author: Chirag Devendrakumar Parikh. Email:

Journal on Artificial Intelligence 2025, 7, 571-584. https://doi.org/10.32604/jai.2025.073765

Received 25 September 2025; Accepted 10 November 2025; Issue published 01 December 2025

Abstract

With the increasing complexity and scale of hyperscale data centers, the requirement for intelligent, real-time power delivery has never been more critical to ensure uptime, energy efficiency, and sustainability. Those techniques are typically static, reactive (since CPU and workload scaling is applied to performance events that occur after a request has been submitted, and is thus can be classified as a reactive response.), and require manual operation, and cannot cope with the dynamic nature of the workloads, the distributed architectures as well as the non-uniform energy sources in today’s data centers. In this paper, we elaborate on how artificial intelligence (AI) is revolutionizing power distribution in hyperscale data centers, making predictive load forecasting, real-time fault detection, and autonomous power optimization possible. We explain how ML (machine learning) and RL (reinforcement learning)-based models have been introduced in PDN (power delivery networks) for load balancing in three-phase systems, overprovisioning reduction, and energy flow optimization from the grid to the rack. The paper considers the architectural pieces of the AI-led systems, such as data ingestion pipelines, anomaly detection frameworks, and control algorithms to manage the power switching, cooling synchronization, and grid/microgrid interaction. Practical use cases show the value of these systems on PUE, infrastructure reliability, and environmental footprint. Key implementation challenges, including data quality, legacy system integration, and AI decision-making governance, are also discussed. Last, the paper speculates on the future of autonomous DC power infrastructure where AI becomes not only an assistive resource to the operator but really takes control over infrastructure behavior end-to-end, from procuring energy, to phase balancing, to predicting maintenance. Integrating technology innovation with operational sustainability, AI-powered power distribution is emerging as a core competence for the Smart Digital Power Facility of the Future.

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Keywords

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