再生可能エネルギー予測における機械学習：発展・課題・将来展望のレビュー
Machine Learning in Renewable Energy Forecasting: A Review of Developments, Challenges and Future Prospects (原題)
Abdullah, Al Hadi, Siad Jibril, Mohammed, Islam Saky, S M Asiful, Islam, Rabiul
🤖 gxceed AI 要約
日本語
太陽光・風力など再エネの変動性に対応するため、機械学習を用いた発電量予測の研究動向をレビューした論文。教師あり学習（ANN・SVM）、PCA・K-Meansによる特徴抽出、深層強化学習（DQN）によるエネルギー管理最適化を整理する。MLは予測精度と系統運用の改善に寄与する一方、大規模データ需要とスケーラビリティが普及の障壁だと指摘する。
English
A review of machine learning approaches to renewable energy forecasting, covering supervised models (ANN, SVM), PCA/K-Means feature extraction, and reinforcement learning (DQN) for energy management. Evidence shows ML improves forecast accuracy and grid performance, but data demands and scalability remain barriers to wider 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
再エネ大量導入と系統安定化が課題となる日本の電力業界にとって、予測精度向上は出力制御や需給調整の実務に直結する。SSBJや開示の文脈ではないが、再エネ調達・脱炭素電源活用を進める企業の系統連系リスク評価に示唆を与える。
In the global GX context
As grids worldwide integrate higher shares of variable renewables, ML-based forecasting underpins grid stability and dispatch optimization—relevant to transition planning and decarbonization pathways, though it sits outside disclosure frameworks like TCFD/ISSB.
👥 読者別の含意
🔬研究者:再エネ予測におけるML手法の比較と残されたスケーラビリティ課題を俯瞰できる。
🏢実務担当者:再エネ調達や系統運用における予測精度向上の技術選択肢を検討する材料になる。
🏛政策担当者:再エネ大量導入下の系統安定化に向け、予測技術とデータ基盤整備の政策的必要性を示す。
📄 Abstract（原文）
Accurate renewable energy forecasting is indispensable for addressing the inherent variability of sources such as solar and wind, both of which are pivotal to the global shift toward sustainable energy systems. Well-calibrated forecasts support grid stability, optimize energy distribution, and reduce unnecessary reliance on conventional power generation. Prevailing forecasting approaches exhibit well-documented limitations that constrain their effectiveness across heterogeneous regional conditions and evolving grid architectures. The research examines how machine learning technologies impact renewable energy development in the power industry. Supervised learning models such as ANNs and SVMs are explored for their predictive capabilities. Both PCA and K-Means clustering are evaluated for their effectiveness in handling feature extraction and trend analysis. Our research studies how Deep Q-Networks (DQN) and other reinforcement learning systems optimize energy management. Evidence consistently demonstrates that machine learning frameworks enhance predictive precision for energy trends and contribute to improved overall power grid performance. Nevertheless, substantial data demands and constrained scalability pathways continue to represent significant barriers to broader deployment. The findings of this review indicate that next-generation algorithms and robust data infrastructure are prerequisites for enabling efficient and reliable power distribution in modern grids. Bridging these gaps is essential to fulfilling long-term grid reliability objectives and advancing the broader trajectory toward sustainable energy infrastructure.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/23545964first seen 2026-10-22 04:13:41 · last seen 2026-10-23 04:35:58
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