Green Urban Mobility: Optimizing Hydrogen Public Transport from Demand to Energy Supply
グリーンアーバンモビリティ：需要からエネルギー供給までの水素公共交通の最適化 (AI 翻訳)
Sofia Polymeni, M. Fotopoulou, Georgios Spanos, Quentin Matthewson, M. B. Moussa, Laurent Helfer, Bruno Rabaste, D. Rakopoulos, Antonios Lalas, Konstantinos Votis, Dimitrios Tzovaras
🤖 gxceed AI 要約
日本語
本論文は、公共交通における水素需要予測と生産スケジューリングを統合した枠組みを提案。季節性を考慮した予測アルゴリズムと生産最適化モデルを組み合わせ、ジュネーブ州の実データで検証。1台のバスは完全にグリーン水素で賄えるが、3台に拡大すると最大45%を系統電力に依存することを示し、フリート展開の課題を浮き彫りにした。
English
This paper proposes a framework integrating hydrogen demand forecasting and production scheduling for public transport. Using seasonal forecasting and optimization, it predicts daily demand with <3% error and optimizes green hydrogen from PV and grid. Results show a single bus can run on green hydrogen, but scaling to three buses requires up to 45% grid energy, highlighting fleet deployment challenges.
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
日本では水素社会実現に向けた取り組みが進むが、公共交通への水素導入はまだ初期段階。本枠組みは、地域の再生可能エネルギーと水素需要を結びつける実践的な評価ツールとして、自治体や交通事業者が導入検討を行う際の参考になる。
In the global GX context
Globally, hydrogen public transport is gaining attention as a decarbonization pathway. This framework provides a realistic tool for assessing techno-economic and environmental feasibility, relevant for cities planning hydrogen bus fleets and for aligning with ISSB/TCFD disclosure on transition risks.
👥 読者別の含意
🔬研究者:Provides a validated demand-supply framework for hydrogen transport, useful for further research on scaling and optimization.
🏢実務担当者:Offers a practical tool for evaluating hydrogen bus fleet feasibility, including energy mix and grid dependency.
🏛政策担当者:Highlights the operational challenges of scaling hydrogen fleets, informing infrastructure and grid planning.
📄 Abstract（原文）
Hydrogen is a promising energy carrier for decarbonizing public transport, but its practical implementation is challenged by the need to align variable renewable energy supply with fluctuating vehicle demand. This paper addresses the existing gap between hydrogen consumption forecasting and production scheduling by introducing a demand and supply framework, specifically tailored to public transportation scenarios, that combines the consumption predictions extracted by a seasonal traditional forecasting algorithm with a production optimization model. Trained on real-world booking and traffic data from the Geneva Canton region, the forecasting algorithm accurately predicts daily hydrogen demand for a year, achieving a normalized root mean squared error less than 3%. This forecast is then used as input to the production model that optimizes the daily mix of green hydrogen from surplus photovoltaic energy and supplementary grid power. Results for a sample week show that while a single vehicle can be powered entirely by green hydrogen, scaling the fleet to just three buses requires up to 45% of its energy from the main grid, highlighting the operational challenges of fleet deployment. The proposed holistic framework provides a realistic tool for evaluating the techno-economic and environmental feasibility of hydrogen-based public transport systems.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.5281/zenodo.21278373first seen 2026-08-14 05:32:46
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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。