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<p>Data centers are among the fastest-growing consumers of electricity, with rising demand driven by cloud services, AI workloads, and global digitalization. Integrating renewable energy sources, particularly solar power, offers a sustainable pathway but introduces variability and operational uncertainty. This paper presents a hybrid investigation into intelligent energy governance for solar-powered data centers, combining literature synthesis, mathematical modeling, simulation, and architectural foresight. We propose a multi-stage governance framework that leverages Dynamic Voltage and Frequency Scaling (DVFS), server consolidation, and Quality of Service (QoS) adaptation to balance workload performance with fluctuating solar availability. Simulation results indicate that up to 50% energy savings are achievable with minimal (&lt; 9%) service degradation. Building on this foundation, this study proposes an AI/ML-enhanced Software-Defined Networking (SDN) energy governance architecture for solar-powered data centers, enabling predictive power optimization, intelligent workload orchestration, and autonomous demand response under renewable variability. The proposed framework advances resilient and energy-efficient data center operation while directly supporting the United Nations Sustainable Development Goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action).</p>

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AI enhanced energy governance for solar powered data centers toward intelligent sustainable and resilient architectures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Qutaiba I. Ali

摘要

Data centers are among the fastest-growing consumers of electricity, with rising demand driven by cloud services, AI workloads, and global digitalization. Integrating renewable energy sources, particularly solar power, offers a sustainable pathway but introduces variability and operational uncertainty. This paper presents a hybrid investigation into intelligent energy governance for solar-powered data centers, combining literature synthesis, mathematical modeling, simulation, and architectural foresight. We propose a multi-stage governance framework that leverages Dynamic Voltage and Frequency Scaling (DVFS), server consolidation, and Quality of Service (QoS) adaptation to balance workload performance with fluctuating solar availability. Simulation results indicate that up to 50% energy savings are achievable with minimal (< 9%) service degradation. Building on this foundation, this study proposes an AI/ML-enhanced Software-Defined Networking (SDN) energy governance architecture for solar-powered data centers, enabling predictive power optimization, intelligent workload orchestration, and autonomous demand response under renewable variability. The proposed framework advances resilient and energy-efficient data center operation while directly supporting the United Nations Sustainable Development Goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action).