Dynamic simulation of a hydrogen-fueled system for zero-energy buildings using TRNSYS software
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it