Modelling and Forecasting of Photovoltaic Generation for Renewable Energy Communities: A Narrative Review
再生可能エネルギーコミュニティのための太陽光発電のモデリングと予測：ナラティブレビュー (AI 翻訳)
Anabel Díaz-Labrador, José M. González-Cava, Héctor Quintián, Juan Albino Méndez Pérez
🤖 gxceed AI 要約
日本語
本レビューは、エネルギーコミュニティにおける太陽光発電の予測手法を概観し、物理モデルから機械学習・深層学習などのデータ駆動型手法への移行を整理する。短期的予測が主流で、リアルタイム推定やピアツーピア電力融通との統合が不足しているという研究ギャップを指摘し、知的技術の統合が管理改善に有望と結論づける。
English
This narrative review surveys photovoltaic generation forecasting methods for energy communities, highlighting a shift from physical models to data-driven machine learning and deep learning approaches. It identifies a gap: short-term forecasting dominates, while real-time estimation and integration with peer-to-peer exchange are limited. The review concludes that integrating intelligent techniques is promising for improving renewable energy community management.
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導入拡大に伴い、コミュニティ単位での需給管理が重要になっている。本レビューは、データ駆動型の予測手法が地域のエネルギー管理にどう活用できるかを示唆し、今後のスマートコミュニティ政策やFIP制度下での発電予測精度向上に参考となる。
In the global GX context
Globally, renewable energy communities are central to the energy transition, and accurate PV forecasting is critical for grid stability and market integration. This review synthesizes advances in ML/DL forecasting and peer-to-peer exchange, offering a roadmap for improving community energy management, relevant to EU energy community directives and global decarbonization efforts.
👥 読者別の含意
🔬研究者:Provides a structured overview of PV forecasting methods and identifies research gaps in real-time estimation and P2P integration.
🏢実務担当者:Offers insights into selecting appropriate forecasting techniques for managing renewable energy communities, potentially improving operational efficiency.
🏛政策担当者:Highlights the importance of supporting data-driven forecasting and P2P schemes in renewable energy policy.
📄 Abstract（原文）
This narrative review examines the state of the art in modelling solar energy production in energy communities, with a particular focus on photovoltaic systems. It explores a wide range of approaches, from classical parametric models to intelligent techniques such as machine learning and deep learning. It identifies key methods, their applications and limitations, with an emphasis on the transition from static models linked to physical system parameters to dynamic and data-driven approaches using weather data and historical data inputs. It further identifies a specific gap in the current literature: the predominance of short-term forecasting over real-time estimation and the limited integration of intelligent techniques with dynamic sharing coefficients and peer-to-peer exchange schemes. Synthesising advances in peer-to-peer energy exchange models, distributed generation frameworks, and predictive optimisation systems, this review highlights the integration of intelligent techniques as a promising direction for improving the management of renewable energy communities, since such techniques are data-driven and decoupled from the physical structure of the photovoltaic system.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/en19153527first seen 2026-08-15 04:45:31
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