Advances in hospital energy systems: Genetic algorithm optimization of a hybrid solar and hydrogen fuel cell combined heat and power
Bibliographic record
Abstract
This paper presents an innovative Fuel Cell Combined Heat and Power (FC–CHP) system designed to enhance energy efficiency in hospital settings. The system primarily utilizes solar energy, captured through photovoltaic (PV) panels, for electricity generation . Excess electricity is directed to an electrolyzer for water electrolysis , producing hydrogen which is stored in high-pressure tanks. This hydrogen serves a dual purpose: it fuels a boiler for heating and hot water needs and powers a fuel cell for additional electricity when solar production is low. The system also features an intelligent energy management system that dynamically allocates electrical energy between immediate consumption, hydrogen production , and storage, while also managing hydrogen release for energy production. This study focuses on optimization using genetic algorithms to optimize key components, including the peak power of photovoltaic panels , the nominal power of the electrolyzer, fuel cell, and storage tank sizes. The objective function minimizes the sum of investment, and electricity costs from the grid, considering a penalty coefficient. This approach ensures optimal use of renewable energy sources , contributing to energy efficiency and sustainability in healthcare facilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".