Optimal design ofenergy-saving renovation oriented for ultra-low energy housings and carbon emission in cold region
Bibliographic record
Abstract
In recent years, buildings oriented for ultra-low-energy and near-zero-energy targets have received increasing attentions. With the energy-saving targets shifting to the existing building stock, the energy-saving renovation has shown great potential. Taking a typical residential building in cold region as the case study, the energy-saving renovation design oriented for ultra-low energy consumption along with the carbon emission calculation were conducted in this paper. Firstly, 30 groups of exterior wall insulation schemes, 30 groups of roof insulation schemes and 4 groups of exterior window schemes were simulated, as well as the sensitivity analysis for selecting the optimized transformation design scheme. When compared with the benchmark building and the building before renovation, the energy-saving rates were 57.1% and 72.1% respectively. Secondly, the carbon emission factor method was adopted to measure the carbon emissions as a key factor. Based on the results, it can be concluded that, when compare with the optimized scheme, the carbon emission of the building before renovation can be reduced by 83.1%, and the design scheme can be further optimized, so as to provide theoretical and practical references for the energy-saving transformation oriented for ultra-low energy residential buildings in cold region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".