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Data-Driven Solar and Wind Resource Classification for Site-Specific Hydrogen Production in Arid Regions

乾燥地域におけるサイト固有の水素生産のためのデータ駆動型太陽光および風力資源分類 (AI 翻訳)

Khadersab Adamsab, N. A. Haddabi, Adam Ali Mohammed Al Nasseri, Vinayak V Kulkarni, Rahila Begum Gadi

Impact学会2026-02-14#水素
DOI: 10.1109/impact68503.2026.11468313
原典: https://doi.org/10.1109/impact68503.2026.11468313

🤖 gxceed AI 要約

日本語

本論文は、オマーンにおける月別太陽放射と風力データを用いて、再生可能エネルギー源を分類し水素生産量を予測するデータ駆動型モデルを提案。線形回帰が最高精度（R²=0.9976）を示し、マシラ、ドファール、アル・ウスタが最適地点と判明。太陽光-風力ハイブリッドによる変動緩和効果も実証。

English

This paper presents a data-driven model to classify solar and wind resources for hydrogen production in arid regions, using data from Oman. Linear regression achieves high predictive accuracy (R²=0.9976). Masirah, Dhofar, and Al Wusta are identified as optimal locations. The study supports strategic planning for green hydrogen infrastructure and shows hybridization mitigates variability.

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

日本は水素戦略を推進しており、本手法はオマーンなどの乾燥地域からの水素輸入や国内適地選定に応用可能。データ駆動型アプローチは、再エネ資源評価の効率化に寄与する。

In the global GX context

This study advances the global green hydrogen literature by offering a data-driven method for resource classification in arid regions, which is crucial for scaling up hydrogen production in sunbelt countries and supporting the energy transition.

👥 読者別の含意

🔬研究者:Provides a reproducible methodology integrating machine learning for renewable resource classification and hydrogen yield prediction.

🏢実務担当者:Useful for feasibility studies and site selection for green hydrogen projects in arid climates.

🏛政策担当者:Supports strategic planning of hydrogen infrastructure by identifying optimal locations based on resource data.

📄 Abstract（原文）

This paper presents a data-driven synthetic model designed to classify renewable energy sources for site-specific hydrogen production. A synthetic yet realistic dataset of monthly solar radiation and wind energy data for 2023-2024 in selected governorates of Oman was designed. Energy was divided into low, medium, and high levels, and that was used to predict hydrogen production by two energy sources in a simple linear equation. Machine learning methods like XGBoost, random forest and linear regression were evaluated. As linear regression obtains the highest level of predictive accuracy is about 0.9976. The findings show the seasonal variation and reveal that Masirah, Dhofar, and Al Wusta could be the best locations for the sustained design for hydrogen generation. The methodology is a useful tool for decision-makers to facilitate strategic planning of green hydrogen infrastructure and promote sustainable energy transition in arid and semi-arid areas. The hybridization of solar-wind systems is demonstrated to mitigate variability and improve production reliability.

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