Energy Efficiency of Residential Buildings in Cold and Dry Climates using Simulation-based Comparative Analysis: Case Study in Tabriz
Bibliographic record
Abstract
The purpose of this research is to analyze the energy of a residential building in the city of Tabriz with a cold and dry climate using energy simulation to provide a model to minimize energy consumption. A comparative model of energy consumption analysis in a three-story building unit with dimensions of 181 square meters is presented using 5 layout modes in the wall, floor, ceiling, window and door. The number of 5 designs with different arrangement of rooms and different number of windows were compared in terms of energy conservation in 51 different diagrams and the optimal energy saving design is selected. In the next step, according to the obtained results, the design of the building in the desired site is discussed. At the end, in order to check the amount of energy absorbed in the building, energy diagrams will be obtained for the thermal region of the coldest day of the year. The results show that the most optimal energy consumption of the residential building is related to the design of plan B with the fabric gains value of 41767 Wh. After that, the designed plan A show the most optimal energy consumption in the building with fabric gains value of 41028 Wh in the month of July. The results of this research are useful for energy efficiency of residential buildings and environmental management in future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".