低炭素電力・計算シナジーのためのAI
AI for low-carbon electricity–computing synergy (原題)
Tong Qian, Yang Liu, Yunlin Huang, Zeyu Zhang, Wenhu Tang
🤖 gxceed AI 要約
日本語
本レビューは、データセンターの電力消費増大に対し、AIが再生可能エネルギーの予測精度向上と計算タスクの柔軟なスケジューリングを通じて電力系統の安定化と低炭素化に貢献する可能性を論じる。深層学習と深層強化学習の応用が従来手法より優れるとし、将来の研究方向として物理融合AIやマルチエージェント学習を挙げる。
English
This review examines how AI enables electricity–computing synergy to address data center energy demands and renewable energy variability. It highlights deep learning for renewable forecasting and deep reinforcement learning for flexible workload scheduling, outperforming traditional methods, and outlines future directions including physics-enhanced AI and multi-agent coordination.
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
日本ではデータセンターの電力消費増加が課題であり、再生可能エネルギーの導入拡大に伴う需給調整にAIを活用する本稿の知見は、日本の電力系統運用やGX投資判断に示唆を与える。
In the global GX context
Globally, this review contributes to the discourse on AI-enabled grid flexibility and renewable integration, relevant for ISSB-aligned climate transition planning and energy sector decarbonization strategies.
👥 読者別の含意
🔬研究者:Provides a structured overview of AI methods for renewable forecasting and flexible computing, useful for identifying research gaps in AI×ESG applications.
🏢実務担当者:Offers insights into how AI can optimize energy use in data centers, supporting corporate decarbonization and energy cost management.
🏛政策担当者:Highlights the role of AI in grid stability and renewable integration, informing policies for digital economy and energy transition.
📄 Abstract（原文）
The rapid expansion of the digital economy has made data centres major energy consumers, creating urgent challenges related to carbon emissions and power system stability. In response, electricity–computing synergy has emerged as a critical paradigm for harmonising computing demand with sustainable power system operation. This paper reviews the transformative role of artificial intelligence as a key enabler in this domain, serving as a bridge between the uncertainty of renewable energy generation on the supply side and the flexibility of computational workloads on the demand side. Two core dimensions are considered: high-precision renewable energy forecasting through deep learning to mitigate generation volatility, and adaptive flexible computing task scheduling through deep reinforcement learning. Compared with traditional statistical and operations research-based methods, AI-driven approaches show stronger capability in modelling nonlinear dynamics and achieving global optimisation under time-varying constraints. Finally, this paper outlines future directions for electricity–computing synergy, including physicsenhanced AI models, cross-regional coordination through multi-agent learning, and comprehensive green performance evaluation mechanisms.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1049/icp.2026.3533first seen 2026-08-27 05:00:31
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