再生可能エネルギー・モデリングとエネルギー転換経路における持続可能性の実現要因としての人工知能に関するシステム志向レビュー
Systems oriented review on artificial intelligence as a sustainability enabler in renewable energy modeling and energy transition pathways (原題)
John Vincent Salinas, Aldrin D. Calderon, Jonathan Veran Macayan, Bernardino C. Ofalia
🤖 gxceed AI 要約
日本語
AIの再エネ応用を太陽光・水力・風力にわたり、予測・最適化・保守・制御の機能別に整理したシステム志向レビュー。深層学習が予測で優位、強化学習とAI–物理ハイブリッドが適応制御やデジタルツインで有望と示す。AIを単なる計算ツールでなく持続可能なエネルギー転換の実現要因として位置づけ、研究者・政策担当・系統技術者向けの実務的ロードマップを提示する。
English
A systems-oriented review organizing AI applications across solar, hydropower, and wind energy by function: forecasting, optimization, maintenance, and control. Deep learning dominates forecasting, while reinforcement learning and hybrid AI-physics models show promise for adaptive control, digital twins, and intelligent energy management. It frames AI as a sustainability enabler for decarbonized energy transitions and offers a practical roadmap for researchers, policymakers, and power-system engineers.
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、再エネ大量導入と系統安定化に向けたAI活用（発電予測・需給調整）の政策的意義が大きい。SSBJ開示や投資家対応への直接接続は弱いが、電力・エネルギー企業の脱炭素投資判断の参考になる。
In the global GX context
For global GX, this connects AI innovation to renewable integration and decarbonization pathways relevant to transition planning and grid resilience. It offers limited direct linkage to TCFD/ISSB disclosure but supports the technical backbone of corporate renewable procurement and energy-transition strategy.
👥 読者別の含意
🔬研究者:AI手法を機能・展開性・持続可能性成果の軸で整理する枠組みとして参照できる。
🏢実務担当者:再エネ調達や系統運用におけるAI導入の実現性・解釈性の判断材料になる。
🏛政策担当者:再エネ大量導入と系統安定化に向けたAI活用の政策的優先順位づけに示唆を与える。
📄 Abstract（原文）
Artificial intelligence (AI) has emerged as a critical enabler of renewable energy system development, offering new capabilities for forecasting, optimization, predictive maintenance, and real-time control. However, existing review studies remain fragmented across individual renewable-energy technologies or algorithmic domains, limiting the transferability of knowledge and the development of system-level insights. Unlike traditional, purely technical algorithm reviews that primarily evaluate computational performance in isolation, this review adopts a systems-oriented framework that examines AI techniques according to their operational functions, deployment considerations, and contributions to long-term sustainability outcomes. Through a structured synthesis of AI applications across solar, hydropower, and wind energy systems, this study examines machine learning, deep learning, reinforcement learning, fuzzy logic, and hybrid AI–physics frameworks. The review evaluates AI techniques across key functional roles, including forecasting, optimization, maintenance, and control, while considering deployment factors such as scalability, interpretability, computational requirements, and implementation readiness. Crucially, the analysis connects these operational capabilities with broader sustainability outcomes, including renewable-energy integration, resource efficiency, system resilience, and decarbonization. The synthesis indicates that deep learning approaches dominate forecasting applications and achieve substantial improvements in predictive performance, whereas reinforcement learning and hybrid AI–physics models demonstrate strong potential for adaptive control, digital twins, and intelligent energy management. Across renewable-energy applications, hybrid frameworks provide a balanced trade-off among predictive accuracy, transparency, and practical deployment feasibility. By framing AI as a sustainability-enabling technology rather than merely a computational tool, this study provides a systems-oriented perspective that connects AI innovation with sustainable energy transitions and offers a practical roadmap for researchers, policymakers, power system engineers, and energy planners pursuing intelligent, resilient, and decarbonized renewable-energy infrastructures.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s43621-026-04850-xfirst seen 2026-10-25 04:37:47 · last seen 2026-11-04 04:38:30
🔔 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。