Advancement of Eco-Friendly Building through Hybrid Renewable Energy and Green Hydrogen Implementation Scenarios
Bibliographic record
Abstract
The energy transition involves moving away from conventional fossil-fuel-based energy systems, like coal, oil, and natural gas, toward renewable energy sources such as wind, solar, hydro, green hydrogen, and biomass, while also enhancing energy efficiency. This shift is motivated by the necessity to lower greenhouse gas emissions, address climate change, and secure sustainable energy for the future. For Canada, this transition is crucial to fulfilling its climate obligations under the Paris Agreement and ensuring a resilient and sustainable energy future. The potential for renewable energy and clean hydrogen to enhance the environmental sustainability of the transportation, buildings, and mining sectors arises from its ability to serve as an alternative to fossil fuels. Currently, heating represents 80% of the energy consumed in Canadian buildings, and hydrogen can be blended with LPG to help lower emissions. The paper focuses on two scenarios for a building case study, examining both the economic and greenhouse gas (GHG) emissions impacts. The first scenario involves using a hybrid renewable energy system to meet two different electrical demands, with 36% allocated to lighting and 54% to equipment loads. The second scenario explores the use of hydrogen mixed with LPG as fuel for thermal loads, covering 15% and 25% of the total demand.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".