Data centers are critical infrastructure supporting the digital economy, but they are also among the largest consumers of electrical energy in modern societies. As the demand for cloud computing, big data, and high-performance computing continues to grow, optimizing energy consumption in data centers has become a crucial challenge. AI-powered techniques, including machine learning, predictive analytics, and intelligent control systems, offer significant potential to improve energy efficiency, reduce operational costs, and minimize the environmental footprint of data centers.
The primary goal of this Special Issue is to provide a platform for high-quality research and reviews that advance the design, implementation, and application of AI-driven energy optimization strategies in data centers. We invite contributions that explore innovative solutions for energy-efficient operation, workload scheduling, cooling management, renewable energy integration, and predictive maintenance, with a focus on sustainable and intelligent energy management.
We welcome original research articles, review articles, and case studies. Through this Special Issue, we aim to highlight cutting-edge research, promote interdisciplinary collaboration, and provide practical insights that contribute to reducing energy consumption and carbon emissions in data centers. Researchers from diverse disciplines, including electrical engineering, computer science, energy systems, and environmental engineering, are encouraged to contribute.
Potential topics include, but are not limited to:
- AI-based energy management and optimization in data centers
- Machine learning for predictive workload scheduling and resource allocation
- Intelligent cooling and thermal management systems
- Integration of renewable energy sources and storage systems
- Energy-efficient hardware and infrastructure design
- Case studies and industrial applications of AI in data centers
