ArticleScientific reports2026
AI-driven optimization of integrated solar systems for hydrogen production and building energy supply.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents an integrated photovoltaic-hydrogen energy system designed to supply continuous electricity and on-site medical oxygen for a hospital in Beijing. The novelty of this work lies in the integration of transient TRNSYS simulations with a neural network-based multi-objective optimization framework and a newly developed supervisory control algorithm for real-time management of power flows among photovoltaic panels, the electrolyzer, hydrogen storage, fuel cell, and the utility grid. To address the lack of built-in optimization capabilities in TRNSYS, an artificial neural network surrogate model was trained using annual simulation data, and a genetic algorithm was employed to identify the optimal system configuration in terms of cost, CO
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.