Sensitivity Analysis of Designs of Row House Planning Influencing on Local Microclimate and Building’s Cooling Energy Consumption in A Tropical City
Bibliographic record
Abstract
Abstract This study aims to investigate the impact of design characteristics of row house projects on outdoor thermal conditions and the building’s cooling energy consumption. The studied parameters comprise the design combinations of four street canyon orientations, building-two block shapes, four street canyon’s aspect ratios (H/W), and two window to wall ratios (WWR). The study firstly performs the simulations of air temperature and mean radiant temperature across the street canyons via using ENVI-met modeling. The simulated air temperature is used as input in energy modeling to calculate cooling energy consumption in a residential unit. The Standardized Regression Coefficient (SRC) obtained from the multiple regression analysis is used to determine the significant design parameters influencing on outdoor air temperature, mean radiant temperatures, and building’s cooling energy consumption. It is found that the increase of H/W has a positive effect on both outdoor conditions and building energy consumption. At the same time, the impact of street canyon’s orientation and building-block shape on those issues shows an invert direction. Future studies should investigate how to optimize the design for achieving better outdoor thermal conditions and building energy efficiency.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".