再生可能エネルギーシステムの効率的統合のためのAI駆動予測
AI-driven forecasting for efficient integration of renewable energy systems (原題)
Olatunde Ibiyinka, Tolu Omotoso, Ndubuisi Ekekwe
🤖 gxceed AI 要約
日本語
本レビューは、再生可能エネルギーの系統統合を支援するAI予測技術の進展を体系的に整理する。機械学習、深層学習、強化学習、ハイブリッドモデルが発電量・需要・蓄電・系統最適化に応用され、予測精度向上や運用コスト削減に寄与することを示す。一方、データ品質や解釈可能性、規制などの課題も指摘し、今後の研究方向を提示する。
English
This review systematically examines AI-driven forecasting techniques for integrating renewable energy into power grids. It covers machine learning, deep learning, reinforcement learning, and hybrid models applied to generation, demand, storage, and grid optimization, showing improved accuracy and cost reduction. Challenges such as data quality, interpretability, and regulation are discussed, along with future directions like explainable AI and digital twins.
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
日本では再生可能エネルギーの大量導入に伴う系統安定化が喫緊の課題であり、AI予測はFIT後の市場統合や需給調整に貢献し得る。本レビューは、日本の電力会社や系統運用者がAI技術を導入する際の俯瞰的な知見を提供する。
In the global GX context
Globally, AI-driven forecasting is key to enabling higher renewable penetration and grid stability, aligning with net-zero targets. This review synthesizes state-of-the-art methods and challenges, offering a reference for utilities and policymakers advancing clean energy transitions.
👥 読者別の含意
🔬研究者:AI予測手法の最新動向と課題を俯瞰し、研究ギャップを特定するのに有用。
🏢実務担当者:再生可能エネルギー事業者や系統運用者がAI予測導入の効果と課題を理解するための参考になる。
🏛政策担当者:再生可能エネルギー統合を促進する政策立案において、AI技術の可能性と規制課題を認識する材料となる。
📄 Abstract（原文）
The global transition to renewable energy is critical for climate change mitigation, energy security, and the achievement of sustainable development goals. Nevertheless, the intermittent and unpredictable characteristics of renewable energy sources, particularly solar and wind, pose substantial challenges for reliable power grid operation and energy management. Artificial Intelligence (AI) has emerged as a transformative technology that enhances forecasting accuracy and enables intelligent decision-making for renewable energy integration. This paper reviews recent advances in AI-driven forecasting techniques that facilitate the efficient integration of renewable energy systems into power grids. Relevant studies were identified through a systematic search of peer-reviewed literature published in major scientific databases over the past decade, with an emphasis on works that applied AI methods to renewable energy forecasting and energy management. Studies were selected and synthesized based on criteria including methodological rigor, relevance to grid integration, and demonstrated impact on operational performance. The review examines the application of machine learning, deep learning, reinforcement learning, and hybrid AI models in forecasting renewable energy generation, electricity demand, energy storage management, and grid optimization. The review also discusses data acquisition and preprocessing methods, forecasting architectures, performance evaluation metrics, and real-world case studies that demonstrate the effectiveness of AI-enabled energy management systems. The findings indicate that AI-based forecasting substantially improves prediction accuracy, enhances grid stability, optimizes energy dispatch, reduces operational costs, and supports greater penetration of renewable energy resources. Despite these advancements, challenges related to data quality, computational complexity, cybersecurity, model interpretability, and regulatory frameworks continue to impede large-scale deployment. The paper concludes by identifying emerging research directions, such as explainable AI, federated learning, digital twins, edge intelligence, and physics-informed machine learning, as promising strategies for developing resilient, intelligent, and sustainable future power grids.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.30574/gjeta.2026.28.2.0204first seen 2026-08-27 04:56:21
🔔 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。