Exploring Hydrogen-Based Energy Storage Systems for Canadian Residential Buildings: An Energy Evaluation Methodology
Bibliographic record
Abstract
In recent years, integrating solar energy systems and hydrogen-based energy storage systems into residential buildings has shown promise in reducing urban greenhouse gas emissions and achieving clean energy supply. However, there is a lack of evaluation on the application potential of hydrogen-based energy storage systems in urban residential buildings. Therefore, a comprehensive energy evaluation method that considers urban building energy differences was implemented in 20 Canadian cities to evaluate the net-zero energy building status. The simulations were based on a typical residential building in North America. The results indicate that, for selected cities, the hydrogen-based energy storage system effectively addresses the seasonal energy mismatch and improves the energy self-sufficiency rate of urban residential buildings. These cities are classified as net-zero energy cities, nearly zero-energy cities, and non-net-zero energy cities based on their energy self-sufficiency rate. It is recommended to adopt hydrogen-only energy storage systems, hydrogen-electricity energy storage systems, and diverse renewable energy resources as integrated solutions to achieve net-zero emission buildings. The proposed energy analysis method can provide technical references for Canadian planners to plan a reasonable hydrogen roadmap for urban residential buildings.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".