Artificial intelligence in renewable energy technologies: Advancing optimization, integration, and carbon neutrality
再生可能エネルギー技術における人工知能：最適化、統合、カーボンニュートラルへの進展 (AI 翻訳)
Aamir Sohail
🤖 gxceed AI 要約
日本語
本レビューは、再生可能エネルギー技術（RET）におけるAI応用の現状を包括的に調査。機械学習や最適化アルゴリズムを用いた資源評価、発電予測、システム監視、グリッド統合の進展を解説。データの不確実性やモデルの透明性などの課題を指摘し、今後の研究方向性を示す。
English
This review comprehensively examines AI applications in renewable energy technologies, covering resource assessment, forecasting, monitoring, control, and grid integration using machine learning and optimization. It highlights challenges such as data uncertainty and model transparency, and outlines future directions for decarbonization.
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、AIによる発電予測や系統安定化技術は重要。本レビューは基礎的な知見を提供するが、日本特有の制度（FIT/FIP）や系統制約への具体的な言及はない。
In the global GX context
This review provides a broad overview of AI for renewable energy, relevant to global efforts in grid integration and optimization. It lacks specific policy context but offers foundational insights for researchers and practitioners in the energy transition.
👥 読者別の含意
🔬研究者:Provides a comprehensive overview of AI methods for renewable energy optimization, useful for identifying research gaps.
🏢実務担当者:Highlights practical AI applications for improving renewable energy system performance and grid integration.
🏛政策担当者:Indirectly supports policy on digitalization and renewables but lacks direct policy recommendations.
📄 Abstract（原文）
Amidst the accelerating global shift towards sustainable energy, there has been a pronounced upsurge in the assimilation of renewable energy technologies (RET) within conventional power infrastructure. This shift aims to make these systems more efficient, reliable, and affordable. Artificial intelligence (AI) plays a vital role in this change. This review examines current research on how AI and renewable energy are connected, highlighting key methods, challenges, and achievements. It encompasses a wide array of AI-driven applications aimed at optimizing diverse operational dimensions of RET, such as spatiotemporal resource evaluation, energy yield forecasting, real-time system surveillance, intelligent control architectures, and seamless grid interfacing. Advanced computing techniques like machine learning, artificial neural networks, and optimization algorithms are explored for their ability to manage large and complex data sets, thereby augmenting predictive precision and enabling adaptive system behavior. This improves prediction accuracy and allows systems to adjust as needed. Some challenges in AI use for RET include unpredictable data, less transparency in models, and limits on real-time responses. Addressing these issues can significantly boost energy production, cut costs, and improve grid stability. The review also looks at potential future advancements poised to redefine the field.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.nxener.2026.100575first seen 2026-05-05 19:14:05
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。