近年來，隨著人工智慧(Artificial Intelligence, AI)與大數據技術的迅速擴展，大型語言模型(LLM)、生成式AI與深度學習應用也跟著快速成長，推動企業對高效能運算資源的需求急遽上升，導致全球AI算力中心的興建速度加快。然而，AI負載相較於傳統業務運算對電力密度、冷卻設計與資源配置的要求更為嚴苛，衍生出高能耗、高碳排與建置期拉長等複合性管理挑戰。
在此背景下，如何提升能源使用效率、降低運營碳足跡，已成為AI基礎建設管理的首要課題。節能效益不僅攸關企業運營成本與碳足跡，更與系統建置方式密切相關。過去傳統數據中心強調規模與擴充性，但在AI算力中心的需求下，反而暴露出電力供應不及、機房冷卻不足與部署時間過長等瓶頸。因此，本研究提出一項管理假設：精簡型AI算力中心的高效率節能設計，不僅有助於減碳與降低營運成本，更能有效縮短施工建置週期，提升專案整合與交付效率。
本研究以「AI算力中心的節能效益與建置效率之整合性關聯」為主軸，探討精簡型AI算力中心的模組化設計特性與能源管理功能，以H科技公司的精簡型AI算力中心為研究對象，並透過在V國部署建置的個案分析，探討精簡型AI算力中心在能源消耗、碳排放強度(CUE)與運營成本等層面的異同，進行估算其投資報酬率(ROI)與潛在減碳效益之影響，以及透過 ISO 50001能源管理與ISO 14064溫室氣體排放標準，評估其節能效益，再延伸至建置效率與營運管理層面的綜合分析。
精簡型AI算力中心因具備模組化設計、靈活配置與高效率能源管理系統，可成為AI算力中心實現節能減碳目標的重要解決方案。此類架構可針對AI工作負載進行動態調整，降低不必要的能源浪費，進而提升單位能效與PUE(Power Usage Effectiveness)表現，有助於企業邁向永續營運與ESG指標的達成。更重要的是，高效率的節能設計往往與系統預組裝、空間整合及冷卻優化等工程手法相輔相成，使得整體AI算力中心建置流程更加簡化。透過預製化模組與標準化配置，可大幅減少現地施工時間與錯誤率，縮短從規劃到啟用的時程，降低建置過程中的協調與整合壓力。本研究從建設成本、運營效益、能源效率與碳排放角度，評估精簡型AI算力中心在未來高算力需求下的可行性與管理價值，其研究結果可成為日後AI算力中心永續規劃與建設效率優化的參考依據。

In response to the exponential growth of Artificial Intelligence (AI) applications and big data analytics, enterprises are rapidly expanding their demand for high-performance computing (HPC) infrastructure. Emerging technologies such as large language models (LLMs), generative AI, and deep learning require robust, scalable, and energy-efficient computing environments. This demand has accelerated the construction of AI computing centers worldwide. However, conventional data center models struggle to meet the unique requirements of AI workloads-particularly in power density, cooling capacity, and rapid deployment-resulting in significant challenges such as excessive energy consumption, elevated carbon emissions, and prolonged construction schedules.
This study explores the potential and benefits of the modular AI computing centers. It is assumed that integrated relationship between energy efficiency and construction performance in AI computing centers, with a particular focus on modularized architectures, could be expected. Through the application of international standards such as ISO 50001 and ISO 14064, the empiral study aims to assess how streamlined, prefabricated AI computing solutions could support energy savings, enhance carbon management, and improve deployment efficiency in a representative case study. A practical implementation case in Country V is used to demonstrate real-world applicability.

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