スマートグリッドと再生可能エネルギー管理のための人工知能：技術・応用・今後の方向性に関するレビュー
Artificial Intelligence for Smart Grid and Renewable Energy Management: A Review of Techniques, Applications, and Future Directions (原題)
Om Zile Udit Vaibhav Pant, Anurag Banerjee Sahil Kumar Singh
🤖 gxceed AI 要約
日本語
再エネの出力変動や系統安定性課題に対し、AI・機械学習を活用した研究を幅広くレビューした論文。太陽光・風力発電予測、需要応答、蓄電池管理、価格予測、デジタルツインなどを対象に、深層学習・強化学習・連合学習・説明可能AIの手法と限界を整理する。エッジ計測・連合予測制御・説明可能な運用支援からなる三層フレームワークを概念提示するが、実証検証は行っていない。
English
A review of AI/ML techniques applied across smart grids and renewable energy: solar and wind forecasting, load forecasting, demand response, storage and battery management, price forecasting, resilience, and digital twins. It surveys deep learning (LSTM, Transformers), explainable AI, multi-agent systems, reinforcement learning, and federated learning, with strengths and limitations. The authors propose a conceptual three-layer framework (edge sensing, federated forecasting/control, explainable operator support) that is not empirically validated, and discuss privacy, interpretability, scalability, and cybersecurity challenges.
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
日本のGX文脈では、再エネ大量導入に伴う系統安定化・需給調整は第7次エネルギー基本計画やGX2040ビューの中心課題であり、AI活用の技術動向として参考になる。ただしESG開示やSSBJ対応との直接の接続はなく、実務的には系統運用・再エネ調達の技術基盤として位置づけられる。
In the global GX context
For global GX scholarship, this sits on the energy-transition side rather than disclosure: it maps how AI/ML supports grid integration of variable renewables, a prerequisite for credible corporate renewable procurement and transition planning. Its unvalidated framework and India-specific policy framing limit direct transferability, but the technique taxonomy is useful for practitioners designing forecasting and demand-response systems.
👥 読者別の含意
🔬研究者:AI/MLを電力系統・再エネ運用に適用する研究の手法マップとして、予測・制御・説明可能性の研究ギャップを把握できる。
🏢実務担当者:再エネ調達や需給管理を担う企業が、予測・需要応答・蓄電池管理へのAI導入の選択肢と限界を把握する材料になる。
🏛政策担当者:系統安定化と再エネ大量導入に向けたAI活用の政策支援策を検討する際の技術的論点整理に資する。
📄 Abstract（原文）
Intermittent renewable generation, volatile loads, and concerns over grid stability have become defining features of modern power systems. Addressing these challenges with Artificial Intelligence (AI) and Machine Learning (ML) is attracting growing attention, including through better forecasting, automated demand response, optimized energy storage, and smart grid fault detection. This paper highlights the various AI-based techniques developed across the smart grid landscape, ranging from solar and wind forecasting to short-term load forecasting, demand-side management, electricity price forecasting, energy storage and battery management, grid resilience, and digital twins. It offers an overview of recently published work, including the major approaches, namely deep learning (LSTM and Transformer models), explainable AI, multi-agent systems, reinforcement learning, and federated learning, together with their strengths and limitations. The paper also presents a conceptual three-layer framework comprising edge sensing, federated forecasting and control, and explainable operator support at the distribution-utility level; the framework is not validated empirically. Challenges such as data privacy, model interpretability, scalability, and cybersecurity are explored, together with future research directions and the relevance of these technologies to India’s energy transition goals.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.48175/ijarsct-38915first seen 2026-11-04 04:42:20 · last seen 2026-11-06 04:36:49
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