Environmental Science and Engineering
環境科学と工学 (AI 翻訳)
Rohit Das
🤖 gxceed AI 要約
日本語
本論文は、太陽光や風力などの再生可能エネルギーグリッドにおけるAI活用の最適化手法を包括的にレビューする。予測分析、適応制御、リアルタイム最適化を通じた安定性・効率性向上の事例を示し、データ不足やサイバーセキュリティなどの課題も考察する。将来の方向性として連合学習や量子AIを挙げる。
English
This paper comprehensively reviews AI-powered optimization in renewable energy grids, covering forecasting, grid management, predictive maintenance, and energy trading. It presents case studies demonstrating tangible benefits, while critically examining challenges such as data scarcity and cybersecurity. Future directions include federated learning and quantum AI.
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
日本でも再エネ導入拡大に伴い電力グリッドの安定化が課題となっている。本論文のAI最適化手法は、日本の送配電網における需給調整や出力予測に応用可能だが、具体的な日本事例はない。
In the global GX context
This paper contributes to the global discourse on integrating AI into renewable energy grids, which is critical for the energy transition. While it offers a broad overview, it lacks region-specific insights that would be valuable for practitioners in specific regulatory environments like the EU or US.
👥 読者別の含意
🔬研究者:Provides a broad overview of AI applications in renewable grid optimization, useful as a starting point for literature review.
🏢実務担当者:Highlights potential AI benefits for grid management and predictive maintenance, but lacks implementation details.
🏛政策担当者:Empty
📄 Abstract（原文）
The global energy transition toward renewable sources such as solar, wind, hydro, and biomass have created unprecedented challenges for modern power grids.Renewable energy is inherently intermittent and variable, requiring advanced optimization strategies to ensure stability, reliability, and efficiency.Artificial Intelligence (AI) has emerged as a transformative tool, offering predictive analytics, adaptive control, and real-time optimization.This paper presents a comprehensive exploration of AIpowered optimization in renewable energy grids, covering forecasting, grid management, predictive maintenance, and energy trading.Case studies from diverse regions demonstrate the tangible benefits of AI integration, while challenges such as data scarcity, cybersecurity, and ethical considerations are critically examined.Future directions, including federated learning, quantum AI, and policy integration, are discussed to highlight pathways toward sustainable and resilient energy systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.46632/ese/5/1/1first seen 2026-06-27 04:44:47
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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。