Rapid advancements in artificial intelligence (AI) and machine learning (ML) are fundamentally reshaping the data center landscape. Sectors such as healthcare, finance, manufacturing and transportation are beginning to take advantage of sophisticated predictive algorithms. As AI models continue to mature and organizations strive to process ever-increasing data sets, data centers must rapidly scale to support increasingly complex workflows.
Data centers require large amounts of energy, due to the large amount of computing needed to operate efficiently. Some executives have expressed concerns about the high power consumption of AI workloads in particular, citing reservations about the environmental impact. However, many experts believe these fears may be overblown, with estimates suggesting AI workloads would only amount to 15% to 20% of data center capacity by 2028.
In fact, technological advancements, including AI, can significantly improve data center energy management by streamlining operations, refining cooling technologies, and integrating renewable energy sources into data center operations. data center.
Optimize energy management
As data centers adapt their operations to meet the growing demand from organizations across industries, leaders must identify new ways to streamline data center operations and reduce their environmental impact.
Cooling Technologies
A notable advancement is the shift from traditional air cooling systems to more energy efficient liquid cooling solutions. Cooling is essential in data centers to prevent servers from overheating, but traditional air-based cooling is known for its significant impact on data center power consumption, often accounting for nearly half of energy consumption total of an installation. New liquid cooling technologies can transfer heat from data center components more efficiently, significantly reducing the energy consumption required for cooling functionality.
AI Monitoring
AI can also improve data center operations by analyzing hundreds or thousands of IoT sensing devices across domains and correlating that with real-time insights, opportunities for facility optimization and/or IT, as well as overall risk management and prevention. For example, AI can predict maintenance needs before they arise, avoiding unnecessary downtime.
Future and more advanced AI models will be able to automatically adjust cooling or power distribution based on real-time data center operations, avoiding unnecessary energy waste, risks, etc. Continuous optimization through AI-powered energy management software enables data centers to improve efficiency. , reduce risks and waste and align operational practices with environmental objectives.
Smart Grids and Microgrids
To efficiently manage energy demand, data centers are increasingly adopting smart grid technologies and deploying microgrids. Smart grid technologies facilitate real-time communication and control between energy suppliers and consumers, improving the reliability and efficiency of energy distribution. Microgrids, on the other hand, are localized energy systems that can operate independently or in conjunction with the main electricity grid. Together, they enable better management of distributed energy resources, such as solar and wind, relieving pressure on the main electricity grid and improving energy resilience.
Integration of renewable energy sources
AI can also help optimize site selection for renewable energy projects, helping to integrate these renewable energy sources into data center operations. By analyzing large data sets, AI identifies the optimal location for installations, predicts energy production, and facilitates the seamless integration of intermittent sources like wind and solar into the grid. This approach not only improves the scalability of renewable energy initiatives, but also allows data centers to meet performance requirements while reducing their carbon footprint.
Embrace innovation and improve energy efficiency
As AI continues to transform data center operations, growing demand for high-performance computing and increased data traffic from digital services and cloud computing present both challenges and opportunities. While the need for more scalable infrastructure to support AI-based applications may be seen as problematic by some, it also opens the door to innovation and operational efficiencies. AI-powered autonomous systems can streamline routine tasks, reduce manual oversight and increase productivity, all while pushing the boundaries of infrastructure design.
Fundamentally, this shift isn’t just about managing energy consumption: it’s also about leveraging AI to optimize resources, improve sustainability, and build more efficient data centers. The future of data centers will be defined by their ability to adapt to the needs of AI, which drives technological progress and environmental responsibility.
About the author
Joe Reele is Vice President, Solutions Architects at Schneider Electricresponsible for bringing together the company’s full suite of products and services to provide comprehensive solutions to its customers. Joe has over 22 years of experience in strategic planning, business development, operations management and systems engineering. He began his career in the US Air Force nuclear program and later led one of the largest financial data center portfolios in the world. Joe has strong technical and business leadership skills with a proven ability to analyze an organization’s business requirements, identify potential gaps and opportunities, and develop innovative solutions to meet the client’s business objectives.
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