液冷与智算中心日报｜2026-10-03
追踪液冷技术、AI 智算中心、数据中心能效、学术论文、产品发布、政策标准、投融资与供应链动态的每日中文报告。
1. 今日一句话总结
24小时内，资本继续加码智算中心，但电力、审批与能效约束已前置，液冷和算电协同正转为项目准入项。
从公开信号看，资本并未因为约束而降温，资本开支仍向AI数据中心与液冷环节集中，说明头部厂商和基础设施资本仍在前置锁定园区、容量和交付窗口；但与此同时，电力并网与能源约束、AI 芯片供给与交付、液冷路线（冷板/浸没/两相）仍是主线，但基础设施约束已前置，意味着行业竞争的关键变量已不再只是“拿到多少 GPU”，而是“能否把 GPU 放进一个可并网、可散热、可控成本、可持续运行的系统”。技术侧技术侧继续围绕高带宽互连与服务器能效优化，论文侧论文侧继续指向算电协同、液冷优化与能效度量重构，共同指向同一个趋势：单点器件优化的边际价值在下降，网络、供电、储能、液冷和调度软件的系统级协同正在上升为真正的产能约束。对产业链而言，未来更稀缺的不是单一硬件，而是把算力、热管理和能源调度耦合起来的工程交付能力。
学术与产业速览
将论文、视频、产业动态和政策项压缩为可快速扫描的标签；每个标签只保留题目、摘要和来源入口。
学术
论文、研究趋势、学术视频与方法论线索。
Grid Demand Flexibility Assessment of AI Data Centers via Batch Workload …
The rapid growth of artificial intelligence (AI) data centers has introduced new challenges to power system operation. As their pow…
展开全文
Grid Demand Flexibility Assessment of AI Data Centers via Batch Workload Temporal Shifting
- 发布时间
- 2026-09-30
- 作者
- Suntao Su、Liang Du、Shengyi Wang
- 主题
- 算电协同
摘要
The rapid growth of artificial intelligence (AI) data centers has introduced new challenges to power system operation. As their power demand becomes larger and more variable, quantitatively characterizing their demand flexibility is increasingly important for effective power system coordination. However, heterogeneous workload characteristics and resource requirements make this flexibility difficult to characterize directly. This paper proposes a framework for assessing the grid-compatible demand flexibility of AI data centers via batch workload temporal shifting. An averaging-based resource usage processing method is developed to map fine-resolution CPU, GPU and memory usage into unified time intervals compatible with power system operation. A workload temporal scheduling model is then formulated to shift batch workloads while preserving execution continuity, delay constraints, and server resource capacities, and is coupled with a utilization-dependent server power model to translate workload scheduling decisions into server power demand. Two complementary flexibility metrics are evaluated: short-term peak demand shaving and the maximum duration of sustained power reduction. Numerical results based on real GPU cluster traces demonstrate that workload temporal shifting can provide quantifiable and grid-compatible demand flexibility for AI data centers with limited disruption to computing workloads.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Suntao Su, Liang Du, Shengyi Wang. Grid Demand Flexibility Assessment of AI Data Centers via Batch Workload Temporal Shifting[J/OL]. (2026-09-30)[2026-10-03]. http://arxiv.org/abs/2609.38020v1.
Integrated Thermal and Power Management for Wave-Powered Subsea Data Cent…
This paper develops an integrated modeling and nonlinear model predictive control (NMPC) framework for coordinating thermal managem…
展开全文
Integrated Thermal and Power Management for Wave-Powered Subsea Data Centers via Nonlinear Model Predictive Control
- 发布时间
- 2026-09-28
- 作者
- Wanqun Yang、Jun Chen
- 主题
- 芯片与算力
摘要
This paper develops an integrated modeling and nonlinear model predictive control (NMPC) framework for coordinating thermal management, flexible workload scheduling, wave-power utilization, and battery operation in a wave-powered subsea data center. Realistic data center workloads are constructed from job-level CPU, memory, and GPU measurements from the MIT Supercloud dataset and divided into interactive and delay-tolerant flexible jobs. Thermal behavior is represented by a three-node lumped model of the IT equipment, recirculating nitrogen, and pressure hull with surrounding seawater as the thermal boundary. The NMPC jointly optimizes the flexible workload power budget and cooling command subject to thermal, battery, and workload constraints. Closed-loop simulations under different workload, thermal, battery, and renewable-generation conditions demonstrate that the proposed framework maintains thermal safety while adapting cooling operation and flexible workload execution to wave-power availability and battery state-of-charge. The parametric studies show that battery capacity and wave-generation capacity strongly affect battery availability and flexible-workload queue accumulation, while excessive renewable generation capacity may lead to increased energy curtailment. Monte Carlo and distance-correlation analyses further show that flexible-job delay is relatively insensitive to the investigated system parameters, whereas terminal battery state-of-charge is primarily influenced by battery energy capacity and wave generation capacity.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，芯片、服务器和高密度算力部署正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向能效评价口径、运营指标和优化目标的系统化梳理。意义：对日报读者而言，它可用于判断芯片路线和服务器密度变化如何传导到机房设计。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Wanqun Yang, Jun Chen. Integrated Thermal and Power Management for Wave-Powered Subsea Data Centers via Nonlinear Model Predictive Control[J/OL]. (2026-09-28)[2026-10-03]. http://arxiv.org/abs/2609.34109v1.
Beyond PUE: A Local Impact Audit Framework for Data Center Environmental …
Standard data center sustainability metrics, including Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), and Carbon…
展开全文
Beyond PUE: A Local Impact Audit Framework for Data Center Environmental Accountability
- 发布时间
- 2026-09-20
- 作者
- Sharifa Sultana、Syed Ishtiaque Ahmed
- 主题
- 能效优化
摘要
Standard data center sustainability metrics, including Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), and Carbon Usage Effectiveness (CUE), measure a facility's resource use and emissions intensity, normalized to IT energy use, without directly representing local resource scarcity, infrastructure capacity, or social footprint. This gap has become politically consequential. In the first quarter of 2026 alone, local opposition delayed or canceled roughly $130 billion in projects across the United States, driven overwhelmingly by recurring concerns over water use, power demand, infrastructure capacity, and transparency rather than internal efficiency, matching the total for all of 2025 [11]. We propose a five-category local impact audit framework covering efficiency, water stewardship, carbon and renewables, regulatory compliance, and local disclosure. The framework is designed for recurring quarterly assessment and independent verification against public records. We illustrate its application using publicly available data from three Illinois facilities that are currently at the center of local policy disputes, and we examine the data-access barriers that constrain independent verification. We position this framework as both a research contribution and a practical instrument for county-level policymakers evaluating data center permitting and moratorium decisions.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，PUE/WUE、能效指标和运营成本控制正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用框架构建和频域/系统级分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向能效评价口径、运营指标和优化目标的系统化梳理。意义：对日报读者而言，它可用于判断不同能效指标是否真实反映节能和成本收益。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Sharifa Sultana, Syed Ishtiaque Ahmed. Beyond PUE: A Local Impact Audit Framework for Data Center Environmental Accountability[J/OL]. (2026-09-20)[2026-10-03]. http://arxiv.org/abs/2609.23421v1.
Grid-Forming E-STATCOMs for Stable Integration of Large-Scale Data Center…
The rapid expansion of large-scale AI data centers (AIDC) is introducing new stability challenges, particularly in weak or low-iner…
展开全文
Grid-Forming E-STATCOMs for Stable Integration of Large-Scale Data Centers: Modeling and Control
- 发布时间
- 2026-09-27
- 作者
- Prabhat Ranjan Bana、Novan Zakkia、Jean-Philippe Hasler、Christer Danielsson
- 主题
- 算电协同
摘要
The rapid expansion of large-scale AI data centers (AIDC) is introducing new stability challenges, particularly in weak or low-inertia networks characterized by fast, step-like demand variations and strict requirements on voltage and dynamic performance. This paper investigates the use of grid-forming (GFM) Enhanced STATCOMs (E-STATCOMs) to support reliable integration of such facilities. A power-admittance-based linear modelling framework is developed to capture system interactions and is validated through detailed EMT simulations. The results demonstrate that E-STATCOMs provide fast, well-damped responses to abrupt load changes while effectively mitigating low-frequency oscillations and interactions with network resonances. By enabling tunable dynamic behavior via a load balancer, virtual impedance, and coordinated active-reactive power support, the proposed approach allows precise shaping of system response and improved regulation at the point of connection. These features make E-STATCOMs a flexible and scalable solution for integrating large data centers into weak grids and long transmission systems, supported by a design-oriented framework that facilitates parameter selection and performance assessment without extensive reliance on EMT studies to meet grid codes and AIDC interconnection requirements.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用框架构建和频域/系统级分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Prabhat Ranjan Bana, Novan Zakkia, Jean-Philippe Hasler, 等. Grid-Forming E-STATCOMs for Stable Integration of Large-Scale Data Centers: Modeling and Control[J/OL]. (2026-09-27)[2026-10-03]. http://arxiv.org/abs/2609.33553v1.
Job Class Thermal Intent Aware Liquid Cooling Allocation for AI Data Cent…
GPU-dense AI data centers need to run on liquid cooling as air simply cannot shed the heat at these power densities. Yet the coolin…
展开全文
Job Class Thermal Intent Aware Liquid Cooling Allocation for AI Data Centers
- 发布时间
- 2026-09-25
- 作者
- Krishna Chaitanya Sunkara
- 主题
- 算电协同
摘要
GPU-dense AI data centers need to run on liquid cooling as air simply cannot shed the heat at these power densities. Yet the cooling loops themselves are blind to what workloads are about to run; they crank up flow only after a sensor catches a temperature climb, which can take 30 to 50 seconds. We built Job-Class Thermal Intent (JCTI) to close that window. The scheduler already knows a job is coming and what class it belongs to; JCTI feeds that information straight to the cooling controller so it can stage coolant before the heat shows up. We pulled the thermal signatures for each job class out of MLPerf GPU power traces and tuned arrival patterns against Alibaba cluster data. Over 120 paired Monte Carlo trials the numbers come out to 56.4% fewer thermal violations and 60.2% less cumulative overshoot than a straight PI loop. As AI data centers evolving towards gigawatt grid loads with highly fluctuating power swings, thermally-aware scheduling reduces sudden demand and improves load prediction in grid side. Cooling and scheduling have been running as two separate systems for years despite each one knowing something the other needs, JCTI wires them together.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Krishna Chaitanya Sunkara. Job Class Thermal Intent Aware Liquid Cooling Allocation for AI Data Centers[J/OL]. (2026-09-25)[2026-10-03]. http://arxiv.org/abs/2609.30785v1.
Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Fra…
As artificial intelligence (AI) becomes embedded in everyday life, its environmental footprint, particularly water consumption rema…
展开全文
Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Framework for Environmentally Accountable AI
- 发布时间
- 2026-09-25
- 作者
- Farnaz Farid、Tashfia Towkee、Sania Nasreen、Sami bin Azad
- 主题
- 算电协同
摘要
As artificial intelligence (AI) becomes embedded in everyday life, its environmental footprint, particularly water consumption remains largely invisible. While energy and carbon impacts are widely recognized, the substantial freshwater demands of data center cooling and electricity generation receive little attention. To address this gap, we introduce SustainAI, a water-aware, closed-loop framework incorporating environmental accountability into AI deployment. SustainAI integrates real-time water metering, a hallucination-aware penalty model, and a water-aware routing algorithm that accounts for regional water stress. Evaluated via Small Language Models (SLMs) extracting health misinformation, results reveal an 11-fold variation in water footprint across geographically distributed data centers (0.0477 mL to 0.5360 mL per inference). Across 1,335 inference runs, the system consumed approximately 399 mL of water but produced only 240 correct outputs, demonstrating that substantial resources are spent on inaccurate responses. Crucially, SustainAI extends beyond technical optimization through a Care by Design lens, framing AI sustainability around relational ethics, regional equity, and ecological stewardship. By combining water monitoring, adaptive accountability, and Care by Design principles, SustainAI provides a practical foundation for integrating ethical care and environmental responsibility into AI infrastructure design and lifecycle management.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Farnaz Farid, Tashfia Towkee, Sania Nasreen, 等. Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Framework for Environmentally Accountable AI[J/OL]. (2026-09-25)[2026-10-03]. http://arxiv.org/abs/2609.30747v1.
Data center cooling choices shift water impacts across the grid: An integ…
Data centers are being developed at an unprecedented pace, yet their energy and water impacts, and the spatial and temporal distrib…
展开全文
Data center cooling choices shift water impacts across the grid: An integrated water-energy model for sustainable data center development
- 发布时间
- 2026-09-22
- 作者
- Garrett Alston、Nancy Love、Rabab Haider
- 主题
- 算电协同
摘要
Data centers are being developed at an unprecedented pace, yet their energy and water impacts, and the spatial and temporal distribution of these impacts, remain poorly characterized. Data centers consume water for cooling (direct) and through electricity generation (indirect). Decisions on siting and cooling technology result in water-energy trade-offs that extend impacts beyond the facility's location. Existing assessment frameworks rely on facility efficiency metrics and average grid water intensity factors, suppressing the temporal impacts of data center load and generation availability. They also attribute indirect consumption to the facility's location rather than to the generators (and corresponding hydrologic regions) that respond to the added load, misattributing spatial impacts. To close this gap, we develop a computational model of the data center-energy-water nexus that links facility cooling and electricity demand with hourly economic dispatch, generator-level water consumption, and monthly subbasin depletion. Built on open-source data, the model resolves where and when water is consumed, and where this consumption compounds existing water risk or creates new risk. Using the model, we study different cooling configurations and proposed developments in the state of Michigan. Air-cooled data centers halve total water consumption relative to evaporative cooling, but increase electricity demand and raise indirect water consumption by one-third, shifting the water footprint from the facility to generators. Mapping these changes to subbasins reveals depletion increases beyond the data center sites, in regions that facility-level reporting may overlook. These results show that data center water and energy impacts cannot be assessed in isolation, motivating the need for integrated modeling to inform siting, design, and reporting practices.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用框架构建和频域/系统级分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Garrett Alston, Nancy Love, Rabab Haider. Data center cooling choices shift water impacts across the grid: An integrated water-energy model for sustainable data center development[J/OL]. (2026-09-22)[2026-10-03]. http://arxiv.org/abs/2609.25437v1.
Privacy-Preserving Coordinated Operation of Power Grids and AI Data Cente…
The rapid growth of large language model training and serving is driving AI data centers (AIDCs) toward gigawatt scale. Unlike conv…
展开全文
Privacy-Preserving Coordinated Operation of Power Grids and AI Data Centers: A Checkpoint-Aware Three-Phase Scheme
- 发布时间
- 2026-09-22
- 作者
- Ziang Liu、Ruizhang Yang、Xin Cui、Francis Yunhe Hou
- 主题
- 算电协同
摘要
The rapid growth of large language model training and serving is driving AI data centers (AIDCs) toward gigawatt scale. Unlike conventional commercial loads, AIDCs possess significant operational flexibility through dynamic voltage and frequency scaling (DVFS) of training and inference workloads, while periodic model checkpointing can induce abrupt power drops and rebounds that erode operating reserves and increase transmission congestion risks. Coordinating AIDC operation with grid scheduling under these unique operational characteristics is challenging because grid and AIDC operators are generally unwilling to share proprietary data and decision-making authority. This paper proposes a hierarchical privacy-preserving coordinated operation scheme between the power grid and AIDCs to address this gap. The proposed scheme contains three phases. In Phase I, the grid operator computes a certified inner approximation of the AIDCs security region for subsequent coordination. In Phase II, the AIDC operator coordinates training and inference AIDCs to optimize workload allocation within the certified security region and generate power schedules and checkpoint alerts. In Phase III, the grid operator solves a checkpoint-aware two-stage robust optimal power flow (OPF) considering renewable generation and checkpoint uncertainties. By exchanging only compact interface information, the framework preserves the privacy of both grid and AIDCs, avoids frequent iterative communication, and enables secure coordination with guaranteed feasibility. Numerical studies on a modified IEEE 14-bus system and a modified NYISO system demonstrate the effectiveness, robustness, and security of the proposed framework.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Ziang Liu, Ruizhang Yang, Xin Cui, 等. Privacy-Preserving Coordinated Operation of Power Grids and AI Data Centers: A Checkpoint-Aware Three-Phase Scheme[J/OL]. (2026-09-22)[2026-10-03]. http://arxiv.org/abs/2609.26365v1.
Musk: 🧠 "It's a no-brainer" to build solar-powered AI data centers in sp…
Yahoo Finance · 检索词：AI data center energy conference keynote。适合作为技术背景或研究趋势补充。
展开全文
Optimizing Data Centers: Energy Efficiency & Cloud Repatriation Strategies
IBM Technology · 检索词：IEEE data center energy efficiency lecture。适合作为技术背景或研究趋势补充。
展开全文
Process Engineer Explains: The Math Behind "Water-Efficient AI Data Centr…
Mide · 检索词：IEEE data center energy efficiency lecture。适合作为技术背景或研究趋势补充。
展开全文
Realizing Asymmetric Datarates via Energy Efficient Ethernet (EEE)
IEEE Standards Association · 检索词：IEEE data center energy efficiency lecture。适合作为技术背景或研究趋势补充。
展开全文
Webinar: Data Centre Liquid Cooling Technology
Park Place Technologies · 检索词：data center liquid cooling conference presentation。适合作为技术背景或研究趋势补充。
展开全文
Above the Cloud: Building Data Centers in Space - Richard Campbell - NDC …
NDC Conferences · 检索词：AI data center energy conference keynote。适合作为技术背景或研究趋势补充。
展开全文
ACM SIGEnergy WeCan'22: Opening Address
Noman Bashir · 检索词：ACM SIGEnergy data center energy talk。适合作为技术背景或研究趋势补充。
展开全文
电力并网与能源约束
本期命中 7 条，热度分 21。可作为论文检索、技术路线和后续研究跟踪关键词。
展开全文
AI 芯片供给与交付
本期命中 3 条，热度分 9。可作为论文检索、技术路线和后续研究跟踪关键词。
展开全文
液冷路线（冷板/浸没/两相）
本期命中 1 条，热度分 3。可作为论文检索、技术路线和后续研究跟踪关键词。
展开全文
产业
产业新闻、技术产品、政策标准、投融资、项目和产业视频。
2024 ASHRAE Webinar: Adiabatic Solutions for Data Centers
Condair USA/CA · 检索词：ASHRAE data center cooling webinar。用于补充产业、产品或工程部署观察。
展开全文
产业热度指数 6/10
产业热度指数为 6/10：本期自动化检索记录到 8 条候选条目，指数按候选条目数量、来源可信度和栏目覆盖度保守计算。
展开全文
NVIDIA Blackwell/GB200/GB300
昨日热度高，今日暂无新增高可信条目
展开全文
AI 芯片供给与交付
今日延续上榜
展开全文
智算中心 CapEx/扩建
昨日热度高，今日暂无新增高可信条目
展开全文
4. 最新视频观察
Musk: 🧠 "It's a no-brainer" to build solar-powered AI data centers in space.
学术会议报告 · Yahoo Finance · 检索词：AI data center energy conference keynote
在 YouTube 打开Optimizing Data Centers: Energy Efficiency & Cloud Repatriation Strategies
学术讲座 · IBM Technology · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开Process Engineer Explains: The Math Behind "Water-Efficient AI Data Centres" Is Laughably Wrong
学术讲座 · Mide · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开Realizing Asymmetric Datarates via Energy Efficient Ethernet (EEE)
学术讲座 · IEEE Standards Association · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开Webinar: Data Centre Liquid Cooling Technology
学术会议报告 · Park Place Technologies · 检索词：data center liquid cooling conference presentation
在 YouTube 打开2024 ASHRAE Webinar: Adiabatic Solutions for Data Centers
标准组织讲座 · Condair USA/CA · 检索词：ASHRAE data center cooling webinar
在 YouTube 打开Above the Cloud: Building Data Centers in Space - Richard Campbell - NDC Copenhagen 2026
学术会议报告 · NDC Conferences · 检索词：AI data center energy conference keynote
在 YouTube 打开ACM SIGEnergy WeCan'22: Opening Address
学术讲座 · Noman Bashir · 检索词：ACM SIGEnergy data center energy talk
在 YouTube 打开来源链接区
本次检索说明
- 公开 RSS/Atom：Data Center Dynamics：检索失败，原因：fetch failed
- 公开 RSS/Atom：The Register：检索失败，原因：fetch failed
- 公开 RSS/Atom：ServeTheHome：检索失败，原因：fetch failed
- 公开 RSS/Atom：Data Center Knowledge：检索失败，原因：fetch failed
- 公开 RSS/Atom：HPCwire：检索失败，原因：fetch failed
- 公开 RSS/Atom：NVIDIA Blog：检索失败，原因：fetch failed
- 论文池：已从本地论文池读取 18 条候选；池更新时间 2026-10-03 09:33。
- YouTube：检索失败，原因：fetch failed
- 视频推荐：当日未形成新候选，按上一日排序池顺延补位。
- 可选自动化增强服务不可用时，本页仅使用可追溯的公开来源与保守的规则化摘要，不补写无法核验的事实。