OPERATIONAL ENERGY SAVING IN BUILDINGS: A COMPARISON OF GREEN VS CONVENTIONAL WALL
Bibliographic record
Abstract
The green wall concept has been introduced as one of the solutions to reduce energy demand for ventilation requirements while improving the natural vegetation in dense urban areas. Past studies revealed that the energy-saving of green walls can vary substantially, from 35% to 90% across countries such as United Kingdom (UK), Canada, Russia, Greece, China, Saudi Arabia, India, and Brazil. Given these differences in energy saving of green walls due to climatic conditions and other reasons, direct application of such findings to the Sri Lankan context is questionable. Therefore, this study aimed to assess the thermal performance of green wall applications in Sri Lanka through a case study analysis of an indirect green façade with a comparative conventional wall. The required data were extracted through on-site temperature measurements from different points of both the exterior and interior wall surfaces of each building in different time intervals per day for a period of fourteen days spanning from October to November. The analysis shows that the green walls contribute to 21% - 36% of temperature difference compared to the conventional wall. Eventually, this results in 0.06 kWh of energy-saving per m2 of wall area, and thereby green walls contribute to the 80% energy saving for ventilation requirements. Hence, the study recommends that the use of green walls can be considered as one of the energy efficiency solutions while improving natural vegetation in tropical climatic cities and absorbing other benefits of green walls.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".