Artificial Intelligence Utilization in Renewable Energy System Modeling: Comprehensive Review of Techniques, Applications, and Future Directions
再生可能エネルギーシステムモデリングにおける人工知能の活用：技術、応用、将来の方向性に関する包括的レビュー (AI 翻訳)
Salinas JVR, Calderon AD, Macayan JV, Ofalia BC
🤖 gxceed AI 要約
日本語
本レビューは、太陽光、風力、水力発電システムにおいて機械学習、深層学習、強化学習、ファジー論理などのAI技術がどのように応用されているかを統合的にまとめた。深層学習は最大50%の予測誤差削減を達成し、強化学習とハイブリッドモデルは適応制御とリアルタイム意思決定を可能にする。課題としてデータ可用性や計算コストを指摘し、説明可能AIやスケーラブルな展開の重要性を強調している。
English
This review comprehensively synthesizes AI techniques (ML, DL, RL, fuzzy logic) applied to solar, wind, and hydropower systems. Deep learning achieves up to 50% error reduction in forecasting, while RL and hybrid AI-physics models enable adaptive control and decision-making under uncertainty. Key challenges include data availability and computational cost, with future directions emphasizing explainable AI and scalable deployment.
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
日本の再生可能エネルギー導入拡大（特に太陽光・風力）において、AIによる予測精度向上や系統安定化は不可欠。本レビューは導入障壁となる変動性対策にAIがどう貢献できるかを整理しており、国内の事業者や政策立案者にとって参考になる。
In the global GX context
This review directly supports global energy transition efforts by mapping AI techniques to renewable energy forecasting, optimization, and control. It aligns with trends in smart grid and digitalized energy systems, offering a structured guide for researchers and utilities seeking to integrate AI into renewable operations.
👥 読者別の含意
🔬研究者:Provides a structured taxonomy of AI methods for renewable modeling, highlighting performance metrics and cross-sector transferable principles.
🏢実務担当者:Offers insights into selecting AI techniques for forecasting and control, potentially improving operational efficiency and grid integration.
🏛政策担当者:Highlights how AI can accelerate renewable deployment and reliability, informing R&D funding and grid modernization policies.
📄 Abstract（原文）
Artificial Intelligence (AI) has emerged as a transformative paradigm for enhancing the modeling, optimization, and operational control of renewable energy systems characterized by intermittency, nonlinearity, and stochastic behavior. Despite significant advances, existing modeling approaches remain fragmented across individual technologies and algorithmic domains, limiting cross-sector applicability and system-level insight. This review addresses this gap by providing a comprehensive synthesis of AI techniques—including machine learning, deep learning, reinforcement learning, and fuzzy logic—applied across solar, hydropower, and wind energy systems. The analysis reveals that deep learning-based approaches dominate forecasting applications, achieving reductions in error of up to 50%, while reinforcement learning and hybrid AI–physics models enable adaptive control and real-time decision-making under uncertainty. Furthermore, hybrid frameworks demonstrate superior trade-offs between predictive accuracy, interpretability, and computational feasibility. By aligning AI techniques with core functional roles—forecasting, optimization, and control—this review identifies transferable modeling principles and deployment constraints across energy sectors. The findings highlight that AI-driven energy system models significantly enhance forecasting accuracy, operational reliability, and system adaptability, enabling more resilient and intelligent energy infrastructures. Finally, key challenges related to data availability, computational cost, and governance are critically assessed, with future directions emphasizing explainable AI, hybrid modeling architectures, and scalable deployment strategies, particularly for data-constrained environments.
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.20944/preprints202607.0346.v1first seen 2026-07-10 04:46:40
🔔 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。