Feasibility of Wind‐Powered Green Hydrogen Production via a Hybrid Graph Neural Network‐Transformer Forecasting Model
ハイブリッドグラフニューラルネットワーク・トランスフォーマー予測モデルによる風力発電グリーン水素製造の実現可能性 (AI 翻訳)
Iman Baghaei, Mojtaba Mirhosseini, Alireza Zahedi
🤖 gxceed AI 要約
日本語
本研究は、グリーン水素施設の計画に重要な長期風速予測を向上させるため、グラフニューラルネットワークとトランスフォーマーを組み合わせたハイブリッド深層学習モデルを提案。NASAの気候データを用いた評価では、GNN-Transformerが標準モデルより30%以上精度向上し、GNN-GRUがさらに優れた性能を示した。これにより、長期予測の精度向上と実用的なモデル選択に貢献する。
English
This study proposes a hybrid GNN-Transformer model for long-term wind speed forecasting critical for green hydrogen site planning. Using NASA climate data, the GNN-Transformer improved MAE by 30% over standard Transformers, while a GNN-GRU variant achieved even better accuracy (MAE 0.44 m/s). The work offers both methodological advances and practical insights for renewable energy infrastructure planning.
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
日本でもグリーン水素戦略が進む中、風力発電の立地評価に必要な長期予測技術は重要。本研究はグローバルデータを使用するが、日本の気象条件への適用可能性を示唆する。
In the global GX context
This paper advances long-term wind forecasting for green hydrogen, a key GX strategy globally. While not country-specific, the hybrid model's improved accuracy can directly support site selection and investment decisions for wind-powered hydrogen projects worldwide.
👥 読者別の含意
🔬研究者:Provides a novel hybrid deep learning framework for long-term wind speed forecasting that outperforms standard models, with implications for green hydrogen feasibility studies.
🏢実務担当者:Demonstrates a practical forecasting tool (GNN-GRU) that can improve site selection and investment planning for wind-powered hydrogen production.
📄 Abstract（原文）
Accurate long‐term wind speed forecasting is pivotal for the strategic planning of renewable energy infrastructure, particularly for assessing the techno‐economic feasibility of wind‐powered green hydrogen facilities. However, capturing the complex spatiotemporal dependencies in climate data remains a significant challenge. This study proposes a hybrid deep learning framework designed to enhance 1‐ to 10‐year wind speed forecasts. The proposed architecture integrates graph neural networks (GNN) to extract inter‐variable correlations and feature‐space dynamics among meteorological parameters, coupled with advanced sequence modeling layers to capture temporal patterns. We rigorously evaluated the framework using multi‐variable climate data from NASA's Power Data Access Viewer, comparing a GNN‐Transformer model against a GNN‐GRU variant, as well as standard baselines (LSTM, CNN) and state‐of‐the‐art hybrids (e.g., MST‐GNN). The results demonstrate that the proposed hybrid framework significantly outperforms standalone models. Specifically, the GNN‐Transformer achieved a Mean Absolute Error (MAE) of 0.53 m/s for 10‐year forecasts, representing a 30.27% improvement over a standard Transformer. Furthermore, our comparative analysis reveals that the GNN‐GRU variant achieved superior practical performance with an MAE of 0.44 m/s. These findings provide two key contributions: (1) establishing a robust GNN‐based framework that advances long‐term forecasting accuracy for green hydrogen site planning, and (2) offering empirical evidence that while Transformers offer theoretical complexity, simpler recurrent architectures like GRU may yield better stability in specific long‐term climatological tasks.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1002/ese3.70541first seen 2026-05-17 07:45:54 · last seen 2026-05-20 05:34:23
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