An integrated computational platform for AI-driven forecasting, dynamic simulation and techno-economic optimization of green hydrogen production in Brazil
ブラジルにおけるグリーン水素生産のためのAI駆動型予測、動的シミュレーション、および技術経済的最適化の統合計算プラットフォーム (AI 翻訳)
Alex Pereira da Cunha
🤖 gxceed AI 要約
日本語
ブラジル向けに、AI再生可能エネルギー予測、電気化学モデリング、動的水素生産シミュレーション、多目的技術経済的最適化を統合したプラットフォームを開発。LSTM等による予測は高精度で、北東ブラジルでのLCOHは3.5～5.0 USD/kg H₂と推定。感度分析で電解槽CAPEX等が主要因子と判明。
English
This study develops a computational platform integrating AI-based renewable forecasting, electrochemical modeling, dynamic hydrogen production simulation, and techno-economic optimization for green hydrogen in Brazil. Forecasts achieved R² >0.90. Regional LCOH estimates are 3.5-5.0 USD/kg H₂. Key drivers are electrolyzer CAPEX, renewable electricity cost, and degradation rate.
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
本論文はブラジル向けだが、AI予測と動的シミュレーションを統合した手法は、日本の水素サプライチェーン設計や投資判断にも応用可能。特に電解槽劣化や再エネ変動を考慮した最適化は、日本の水素戦略においても重要である。
In the global GX context
This paper presents a comprehensive decision-support tool for green hydrogen that integrates AI forecasting with electrochemical simulation and optimization. While focused on Brazil, the methodology is transferable and offers a template for integrated assessment of hydrogen projects globally.
👥 読者別の含意
🔬研究者:This study provides a validated integrated modeling framework for green hydrogen production, useful for researchers working on hydrogen systems optimization and AI applications in energy.
🏢実務担当者:Corporate sustainability teams can use the platform's approach for feasibility studies and investment analysis of green hydrogen projects.
🏛政策担当者:Policymakers can leverage the LCOH estimates and sensitivity analysis to design supportive policies for hydrogen deployment.
📄 Abstract（原文）
The large-scale deployment of green hydrogen requires integrated assessment tools capable of capturing renewable intermittency, electrolyzer electrochemical behavior, operational dynamics and economic performance. This study develops and validates an integrated computational platform combining artificial intelligence–based renewable forecasting, electrochemical modeling, dynamic hydrogen production simulation and multi-objective techno-economic optimization, tailored to the Brazilian energy context. Semi-empirical electrochemical models for alkaline and PEM electrolyzers, including dynamic degradation effects, were validated against experimental and pilot-scale data. Short-term solar and wind forecasting using LSTM, GRU and XGBoost models achieved coefficients of determination (R²) above 0.90. Forecast outputs were coupled to a dynamic hydrogen production simulator to quantify impacts on operational performance and levelized cost of hydrogen (LCOH). Regional assessments for northeastern Brazil indicate LCOH values between 3.5 and 5.0 USD kg⁻¹ H₂ under 2025 cost assumptions. Sensitivity analysis identified electrolyzer CAPEX, renewable electricity cost and degradation rate as dominant drivers, while multi-objective optimization revealed hybrid wind–solar systems with moderate storage as the most robust configurations. The proposed platform provides a comprehensive decision-support tool for system design, investment analysis and policy formulation.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.54033/cadpedv23n1-085first seen 2026-05-15 19:34:39
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