Optimization-based scheme for nearly-zero energy multi-storied residential buildings with architectural design elements
Bibliographic record
Abstract
Abstract Global energy consumption has significantly increased as a result of the rise in appliance and equipment usage, which has been driven by technological improvements and economic expansion, particularly noticeable in the infrastructure and building industries. Among these, residential construction emerges as a prominent energy consumer in infrastructure development. Therefore, architects and engineers must prioritize the adoption of energy-efficient strategies in both planning and execution to create buildings that achieve net-zero or nearly-zero energy consumption levels. This study focuses on unconditioned residential building located in a warm–humid Indian climate. Energy consumption is minimized through a metamodel based genetic algorithm optimization framework that fine-tunes envelope parameters like orientation, window-to-wall ratio (WWR), shading depth, and shading angle to enhance daylight penetration, reduce artificial lighting requirements, and mitigate internal and solar heat gains. Here, a parametric model is created utilizing four architectural design variables: orientation, WWR, shading depth, and shading angle that generates a large number of options for the analysis of the building’s energy consumption. The outcomes of the case study demonstrate a significant decrease of 34.63% in energy consumption compared to the reference design, achieved through the optimal selection of design variables.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".