Dynamic simulation of a hydrogen-fueled system for zero-energy buildings using TRNSYS software
Bibliographic record
Abstract
As a result of global warming and environmental pollution over the past few decades, life on Earth has been adversely affected. For this reason, large-scale zero-energy buildings have garnered considerable attention for utilizing clean energy resources. Hydrogen is a green and sustainable fuel with remarkable features of having high efficiency, higher energy content than diesel and gasoline, and producing only water as waste. Hydrogen can be integrated with a hybrid renewable energy system as safe and reliable energy storage for a longer time in net zero energy buildings compared to batteries with short-time energy storage capability. The focus of this study is to find the optimum design for a hydrogen storage system to isolate a small lab building from grid power by providing its hourly energy needs with renewable resources located in Toronto, Canada. Hence, a model using TRNSYS software is developed to study the behaviour of an energy system that could supply electricity to the lab building. To conduct a case study, TRNSYS is used to extract the solar irradiance during one year for climate data of Toronto. The system mainly comprises solar panels, an electrolyzer, a fuel cell, and a hydrogen storage tank. According to the results, renewable energy system reliability can be increased throughout the entire year period, and grid dependency reduced by adding a hydrogen storage system. Based on the optimized simulation results the system can supply the load demands of the lab in a year with the solar panel electricity production and the hydrogen storage unit without requiring grid power.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".