Validation Field-Measured Data Through Design-Builder Simulation Software of Indoor Air Temperature in A Modern Residential Building in Erbil, Iraq
Bibliographic record
Abstract
Examining discrepancies between Design-Builder's thermal performance simulations and field-measured data in Erbil City underscores fundamental shortcomings in the existing modeling frameworks.These inconsistencies reveal technical limitations and a deeper disconnect between generalized simulation algorithms and the specific environmental, cultural, and architectural nuances of rapidly urbanizing regions like Erbil.To bridge this gap, it becomes imperative to recalibrate thermal modeling methodologies that are contextsensitive and adaptable, aligning computational predictions with on-ground realities.This recalibration is not merely a technical enhancement but a critical step toward fostering truly sustainable residential designs that prioritize both thermal conditions and occupant comfort.This study explores the comparative analysis of inside air temperature data obtained through field measurements and simulation outputs derived from Design-Builder modelling software.The second objective from this research was to generate the dataset for the thermal conditions of the case study.As such, the process was divided into two main parts.Field measurements were first conducted on the selected case study (Ashty House /modern house).The second part involved a simulation analysis process, which was conducted on the selected case study (Ashty House), The thermal simulation results were then validated to estimate their accuracy which required determining the average values of the identified significant parameters.The generated dataset as well as the various designs explored and optimum design solutions for Ashty House were saved in a Microsoft Excel file and could be selected manually.All the result for the indoor air temperature deviation percentage is less than 10% that will be a sign of good archiving of compatibility between field measurement and simulation data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".