Optimization of Hybrid Renewable Energy Systems for Hydrogen Refueling Stations with Machine Learning-Based Cost Classification
機械学習ベースのコスト分類を用いた水素充填ステーションのためのハイブリッド再生可能エネルギーシステムの最適化 (AI 翻訳)
D. Kumari, Dr.P.S.Manoharan, Dr.P.Deepamangai, D. Kumar
🤖 gxceed AI 要約
日本語
本研究は、250kWの電解槽を備えたグリッド接続のPV-風力水素充填ステーションを設計し、Homerソフトウェアを用いて最適なシステム構成とコストを算出。さらに、機械学習モデル（決定木、KNN）でコスト分類を行い、決定木が90.4%の精度で最適なコスト予測を実現した。水素モビリティの脱炭素化に貢献する技術的・経済的解析を提供する。
English
This study designs a grid-connected PV-Wind hydrogen refueling station with a 250kW electrolyzer producing 100kg H2/day, optimized via Homer software for sizing and cost. Machine learning classifiers (decision tree, KNN) further classify cost levels, with decision tree achieving 90.4% accuracy. Contributes technical and economic insights for decarbonizing transport hydrogen.
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
日本政府は水素基本戦略で水素ステーション普及を掲げており、本件のような再エネ由来水素の経済性最適化手法は実務に直結する。ただし、日本の電力系統や補助金制度を考慮した追加検討が必要。
In the global GX context
As hydrogen refueling infrastructure expands globally (e.g., Europe's Hydrogen Backbone, US H2Hubs), this optimization approach combining renewables with ML-driven cost analysis offers a replicable framework for project developers and policymakers seeking cost-effective, low-carbon hydrogen deployment.
👥 読者別の含意
🔬研究者:Provides a methodology integrating Homer simulation with machine learning for cost classification in hydrogen systems, useful for further optimization studies.
🏢実務担当者:Offers a practical design framework for hybrid renewable hydrogen stations with cost prediction tools, aiding project feasibility assessments.
🏛政策担当者:Demonstrates techno-economic viability of renewable hydrogen for transport, supporting policy incentives for hydrogen infrastructure.
📄 Abstract（原文）
As the necessity for transportation and industry development emerges increasingly owing to the rise of hydrogen-fuelled vehicles. The hydrogen refuelling stations powered by renewable energy sources serve a key role in the decarbonization of transportation. The proposed work deals with framing a grid-connected PV-Wind powered hydrogen refuelling station designed to serve a 250kW electrolyzer, producing approximately 100Kg of hydrogen per day. This station is technically and economically designed using Homer Software to obtain optimal sizing and minimal cost parameters governing the system. To further enhance the cost analysis, machine learning classification models are applied to distinguish the lowest cost, to the highest cost of the various architecture simulated. In deeper classification, the Decision tree algorithm achieved the highest accuracy of 90.4%, outperforming the KNN algorithm and providing effective cost predictive classification.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/icmsci67830.2026.11469180first seen 2026-05-15 19:25:06
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