Artificial intelligence-driven short-term energy forecasting of an off-grid solar PV/hydrogen fuel cell-powered AI-data center: AI-energy Nexus
AI駆動型のオフグリッド太陽光/水素燃料電池AIデータセンター向け短期エネルギー予測：AI-エネルギーネクサス (AI 翻訳)
C. Ghenai
🤖 gxceed AI 要約
日本語
本研究は、AIデータセンターを再生可能水素で電力供給するためのグリーン水素発電システムの性能評価と、AIベースの短期エネルギー予測モデルの開発を目的とする。太陽光発電と水素燃料電池を組み合わせたオフグリッドシステムをモデル化し、技術的・経済的・環境的側面から分析する。AIによる予測モデルはグリーン水素生産の最適化に寄与する。
English
This study assesses the performance of green hydrogen power systems for AI data centers and develops AI-based short-term energy forecasting models. It models an off-grid solar PV/hydrogen fuel cell system and evaluates technical, financial, and environmental aspects. The forecasting models aim to optimize green hydrogen production and reduce operational costs.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では電力需要の増加とカーボンニュートラル目標の達成が求められており、AIデータセンターのエネルギー消費対策が急務となっている。本研究のオフグリッド水素システムとAI予測技術は、再生可能エネルギー導入の課題解決に貢献する可能性があり、日本のデータセンター事業者やエネルギー政策にとって重要な示唆を与える。
In the global GX context
Globally, the rapid growth of AI data centers poses significant energy and carbon challenges. This paper's integration of green hydrogen and AI-driven forecasting offers a scalable solution for decarbonizing high-energy computing infrastructure, aligning with net-zero targets and corporate renewable energy procurement strategies.
👥 読者別の含意
🔬研究者:Researchers can explore AI forecasting models for renewable hydrogen systems and gain insights into integrated energy management for data centers.
🏢実務担当者:Data center operators and energy managers can use the forecasting models to optimize hydrogen production and reduce costs, enabling reliable green power.
🏛政策担当者:Policymakers should consider incentives for green hydrogen infrastructure for data centers to promote carbon-neutral computing.
📄 Abstract（原文）
Abstract. Powering AI data centers with renewable hydrogen is a promising energy option. This strategy has the potential to satisfy the high energy requirements of AI-data centers while simultaneously reducing the amount of carbon emissions produced. In order to keep up with the demand for green hydrogen, well-planned infrastructure, investments in renewable energy, electrolyzes, fuel cells, and hydrogen storage are essential. In order to maximize green hydrogen production, decrease operational costs, increase energy efficiency, precise solar power forecasting and green hydrogen production are crucial. The main objective of this study is to assess the performance of green hydrogen power systems to meet the load of AI-data centers and to develop AI-based short term energy forecasting models. This study models and simulates the proposed clean power system to evaluate the performance of AI-data centers. This study will examine the technological, financial, and environmental elements of the proposed green hydrogen-based power system for the AI-data center's massive power requirement and the proposed energy forecasting models. The challenges associated with power and heat management of AI data center and emerging and sustainable solutions for incorporating clean energy technologies and energy efficiency will be discussed in more detail.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.21741/9781644904176-8first seen 2026-06-16 05:09:56
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。