--- 格式版本: 2 标题: "How Data Centers Can Tame the AI Energy Beast While Boosting Performance" 原文链接: "https://www.datacenterknowledge.com/ai-data-centers/how-data-centers-can-tame-the-ai-energy-beast-while-boosting-performance" 发布日期: "2026-08-17" 发布时间校准状态: "found" 发布时间需复核: "否" 发布时间来源: "llm:scrape:strict_markdown_body" 发布时间证据: "AUG 17, 2026" 发布时间校准原因: "标题附近出现的最新日期AUG 17, 2026，且早于发现时间一天，符合发布日期特征。" 发布时间校准置信度: "1" 发布时间候选数量: 6 发布时间严格候选数量: 6 发布时间原页读取状态: "" 发布时间未找到原因: "" 发布时间校准时间: "2026-08-18T13:02:06+08:00" 发布时间仲裁状态: "confirmed" 发布时间仲裁尝试次数: 1 发布时间仲裁耗时毫秒: 6025 发现时间: "2026-08-18T12:56:22+08:00" 入库时间: "2026-08-18T05:02:36.008Z" 来源平台: "Data Center Knowledge 搜索" 搜索渠道: "source_template" 搜索词: "https://www.datacenterknowledge.com/search?q=performance" 匹配关键词: - "performance" - "AI" - "Liquid Cooling" 相关厂家: - "NVIDIA" - "Google" - "OpenAI" 相关专家: [] 内容类型: "网页" 抓取工具: "CDP Render" 清洗工具: "CDP Text + Defuddle/Readability 正文提取" 原始附件: [] AI优质: "否" AI打分: 44 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "文章聚焦数据中心能源效率与AI优化，仅泛泛提液冷节能，未涉及超节点/AI Rack具体架构、供电或互连技术，商业部署信号缺失，属于泛行业观点内容。" AI质检模型: "ali-deepseek-v4-flash" AI质检时间: "2026-08-18T13:03:09+08:00" AI主题相关性: 8 AI来源权威性: 10 AI新颖性: 10 AI技术细节: 5 AI商业部署信号: 3 AI完整性: 8 AI摘要: "施耐德电气专家Carsten Baumann撰文指出，面对AI带来的数据中心能耗激增，运营商可采用AI驱动的能源管理、自动化与集成液冷系统，在降低能耗的同时保持性能。" AI摘要模型: "ali-deepseek-v4-flash" AI摘要时间: "2026-09-07T03:23:20.432Z" 采集批次: "2026年8月18日10点53分10秒" 采集批次ID: "20260818-105310-367" 去重键: "https://www.datacenterknowledge.com/ai-data-centers/how-data-centers-can-tame-the-ai-energy-beast-while-boosting-performance" --- An Informa TechTarget Publication NEWSLETTER SIGN-UP AI DATA CENTERS ENERGY & POWER SUPPLY COOLING COMMENTARY Insight and analysis on the data center space from industry thought leaders. How Data Centers Can Tame the AI Energy Beast While Boosting Performance As AI drives unprecedented energy demands, smart data centers are using the very technology causing the surge to slash consumption and costs, writes Carsten Baumann. Carsten Baumann,Industry Perspectives July 31, 2025 4 Min Read IMAGE: ALAMY The demand for data centers is skyrocketing, driven by the rapid adoption of AI and the overall digitization across sectors and in everyday life. By 2030, data centers’ total energy demand is expected to more than double, reaching 945 terawatt hours (TWh), surpassing Japan’s total energy consumption. Projections show that AI-related energy consumption could skyrocket from 100 terawatt-hours (TWh) in 2025 to as high as 785 TWh by 2035. Data centers, which are the backbone of this transformation, must scale quickly while remaining energy-conscious, making a focus on energy efficiency both a business imperative and a societal responsibility. In this environment, efficiency is no longer just a metric – it’s a strategic advantage. To remain competitive and meet increasing performance demands, data centers must move beyond conventional energy management strategies. This shift requires adopting AI-driven technologies and integrated system designs to achieve the next level of operational efficiency. Related:OpenAI’s Proposed IPO: A Trifecta of Opportunities, But Don’t Lock In Yet AI-Optimized Energy Efficiency AI and cloud computing are driving an unprecedented demand for power in data centers. With workloads like large language model training or real-time inference, these systems require vast amounts of energy to operate efficiently. According to the International Energy Agency and reported by Goldman Sachs in 2024, a single query to ChatGPT can consume almost 10x of power compared to a traditional Google search, which underscores the energy intensity of modern AI applications. Now, consider the scale of that energy use across ChatGPT. Asking ChatGPT itself, it states that it processed over one billion messages daily. To meet these lofty compute demands, data centers must guarantee consistent uptime and performance, ensuring that services remain available without interruption. While AI is contributing to the surge in power requirements, it is also poised to be a key enabler in managing data center energy usage. AI-driven energy management solutions are transforming how the grid and data centers approach power distribution and efficiency. These systems leverage machine learning to dynamically adjust workloads, integrate renewable energy sources, and optimize cooling systems. This synergy between AI’s growing energy needs and its ability to optimize energy efficiency is critical for data center operators. As AI workloads become more prevalent, it is projected that by 2030, AI will account for over 35% of global data center workloads, driving a 160% increase in power demand. In response, AI-driven energy management systems enable operators to reduce energy consumption, lower operational costs, and reduce carbon footprints, all while ensuring that performance remains uncompromised. This makes AI not only essential for powering modern data centers but also for ensuring their sustainability in the face of increasing demand. Related:How Changing AI Workloads Are Redefining Data Center Design Automation and AI for Power Optimization AI not only enhances operational efficiency but also revolutionizes power management by combining advanced automation with predictive analytics and machine learning to reshape how energy is used and optimized in data centers. AI-enabled tools can predict power consumption and adjust workloads to minimize energy waste, making power systems more efficient. Power usage effectiveness (PUE), a metric that compares the total energy used by a data center to the energy delivered to computing equipment, was once the gold standard for measuring energy efficiency. PUE is now being supplemented by more sophisticated AI-driven power management models. For instance, AI can adjust energy consumption based on time-of-day pricing, grid constraints, or renewable energy availability, transitioning from reactive to predictive power management. Moreover, automation streamlines critical tasks like capacity planning, cooling adjustments, and fault detection. Smart sensors and digital twins provide real-time visibility into infrastructure performance, enabling operators to make faster, more informed decisions. The result is a data center that is more agile, resilient, and capable of maintaining high efficiency even as demand fluctuates. Related:Preparing Enterprise Data Centers for AI Adoption Integrated Power and Cooling Systems Efficiency gains cannot be achieved in isolation. Traditional, siloed approaches to power and cooling design often lead to inefficiencies, over-provisioning, and wasted energy. To address this, data centers must take a holistic approach by deploying integrated power and cooling systems. As AI and cloud workloads grow more power-intensive, operators are modernizing facilities with advanced liquid cooling technologies. Unlike air-based systems, liquid cooling delivers up to 40% in energy savings, making it an ideal solution for dense AI workloads. When paired with intelligent power distribution and AI-driven monitoring systems, liquid cooling can significantly improve energy efficiency and reliability. By synchronizing power and cooling infrastructure, data centers can reduce over-provisioning, streamline maintenance, and ensure optimal performance under varying conditions. This integration also helps increase resilience – data centers can better handle power fluctuations and extreme temperatures, thus minimizing unplanned downtime and operational disruptions. Meeting Demand Without Compromise To keep pace with surging energy demand from AI and cloud workloads, data centers must scale efficiently – without compromising reliability, performance, or sustainability. This requires becoming smarter, more automated, and more integrated. AI-powered energy management tools, predictive maintenance, and synchronized power and cooling systems are key to achieving that balance. As demand intensifies, investments in grid modernization, energy storage, onsite generation, and renewable energy integration will also be critical in managing costs and ensuring energy resilience. The benefits for data center operators are immediate and tangible: Reduced energy usage leads to lower operating costs without sacrificing performance or service reliability. By deploying AI-driven systems that continuously monitor, adapt, and optimize energy usage, data center operators can boost efficiency, strengthen competitiveness, and align operations with sustainability goals. About the Authors Carsten Baumann Director, Strategic Initiatives & Solution Architect, Schneider Electric Carsten Baumann is director of strategic initiatives and solution architect at Schneider Electric. Industry Perspectives Want more Data Center Knowledge stories in your Google search results? ADD US NOW Subscribe to the Data Center Knowledge Newsletter Get analysis and expert insight on the latest in data center business and technology delivered to your inbox daily. 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