Designing Energy Efficient and Carbon Friendly Buildings In a Cold Climate
Bibliographic record
Abstract
Various sectors of the building industry fail to communicate and optimize energy use of buildings. Developing an overall strategy to reduce energy consumption and environmental impact of buildings in all stages is of vital importance. This is completed by applying various Energy Conservation Measures onto buildings and comparing the results. Utilizing 14 eQuest models and an extensive Excel spreadsheet, various combinations of building envelope parameters and HVAC systems are analyzed, in order to aim for TGS Tier 3 targets and ASHRAE 90.1-2013 targets. The results analyzed include energy consumption, space heating/cooling loads, upfront construction costs, life cycle costs and others. These results will create trendlines and a basis to address which building or HVAC system parameters show the best payback in terms of thermal performance, reducing emissions and overall cost. Employing a certain combination of building and HVAC system parameters created Model C-2 that meets TGS Tier 3 – surpassing ASHRAE-90.1-2013 EUI by 70.4%, TEDI by 88.0% and GHGI by 73.0% at only marginal cost differences. Further improvement methods and analysis are discussed beyond the results of the models.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".