Thermal performance and energy efficiency of PCM integrated buildings in eight cities located in snow, fully humid with warm summer climate region
Bibliographic record
Abstract
Phase change materials have been applied into building framework to reduce energy and fossil fuel consumption as well as make building sector more sustainable.. In this study, the energy consumption assessment of lightweight twostorey PCM-enhanced residential house placed at different eight locations (Helsinki, Kiev, Saint-Petersburg, Moscow, Stockholm, Toronto, Montreal and Kiev) restricted by Dfb climate region will be evaluated. The number of iterations were performed by DesignBuilder software combined with Energy Plus engine by applying eleven melting temperature ranges of PCM. The output shows that the optimal PCMs have reduced the temperature swings up to 2.4 ℃. The performance of PCM is not constant for the monthly assessment basis, hence every month different PCM have performed efficiently. The optimal PCM variance is between the thermal comfort zone (20-26℃) and depends on the geographical parameters. For the indicator cities, the energy consumption varies from 2,81% to 5,72%. The volumetric assessment may be shows that the efficient performance of PCM increases with expansion of surface area combined with reduction of thickness. Overall, the enhancement of PCM into building framework in residential building located in Dfb climate region is feasible option.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".