AI-Based Predictive Energy Management of a Hybrid Solar-Wind System for Green Hydrogen Production: Case Study of Tarfaya, Morocco
グリーン水素生産のためのハイブリッド太陽光・風力システムのAIベース予測エネルギー管理：モロッコ・タルファヤのケーススタディ (AI 翻訳)
Fatima Zahra El-Yazidi, H. Mounir
🤖 gxceed AI 要約
日本語
本論文は、太陽光・風力・バッテリー・水素製造を統合したハイブリッドシステムに対し、AIベースの予測エネルギー管理を提案する。機械学習による日次発電予測と強化学習によるエネルギー管理戦略を組み合わせ、再生可能エネルギーの変動性を緩和し、電解槽の連続運転を保証する。モロッコのタルファヤを対象に、実気象データを用いたシミュレーションで有効性を示す。
English
This paper proposes an AI-based predictive energy management system for a hybrid solar-wind-battery system producing green hydrogen. It combines machine learning for day-ahead solar and wind generation forecasting with a reinforcement learning-based energy management strategy to coordinate generation, storage, and grid interaction, aiming to reduce intermittency and ensure continuous electrolyzer operation. A case study in Tarfaya, Morocco, demonstrates the approach.
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、水素基本戦略やグリーンイノベーション基金により水素サプライチェーン構築が進むが、再エネ変動性への対応が課題。本論文のAIによる予測管理は、日本の再エネ水素プロジェクトや離島での自立型エネルギーシステムに応用可能な知見を提供する。
In the global GX context
Globally, the paper contributes to the growing literature on AI-enabled energy management for green hydrogen production, addressing the intermittency challenge that hinders electrolyzer efficiency. It aligns with international efforts to scale up green hydrogen and integrate renewable energy systems, offering a data-driven approach that can be adapted to other coastal or high-renewable-potential regions.
👥 読者別の含意
🔬研究者:Provides a concrete example of combining ML forecasting and RL for energy management in hybrid renewable-hydrogen systems, useful for further algorithmic improvements.
🏢実務担当者:Offers a framework for optimizing renewable-hydrogen systems to ensure continuous operation, which can inform project design and operational strategies.
🏛政策担当者:Highlights the potential of AI to enhance the viability of green hydrogen projects, supporting policies that promote renewable hydrogen and smart energy management.
📄 Abstract（原文）
Combining renewable energy systems and the production of green hydrogen presents a major opportunity to enable significant decarbonisation of both the energy and industrial sectors. However, due to the intermittent nature of both solar (PV) and wind resources, achieving and maintaining the necessary conditions for electrolyzers to operate continuously and reliably remains a major hurdle. To address this challenge, this paper proposes an artificial intelligence (AI)based predictive energy management system to manage a hybrid solar-wind-battery system that produces hydrogen. The target location for this application is the coastal city of Tarfaya (Morocco) where the authors conducted an earlier data-driven analysis that determined the location had a very high level of suitability for deploying hybrid renewable systems. Along with the system’s renewable energy harvesting component, on-site climatic data are employed for renewable energy production simulation and in AI-based prediction & intelligent control technique development. A forecasting machine learning model is developed to predict the solar and wind generation on a dayahead basis, and an RL-based energy management strategy is proposed in order to coordinate renewable generation, battery storage as well as grid interaction. The objective of the proposed approach is to reduce RE intermittency and energy loss, as well as guaranteeing the electrolyzer continuous operation.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/iraset68627.2026.11538747first seen 2026-06-12 05:52:21 · last seen 2026-08-02 06:21:30
🔔 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。