Investigating building information model to building energy model data transfer integrity and simulation results
Bibliographic record
Abstract
Traditional energy modeling methods are usually time-consuming and labour-intensive, so energy simulation is rarely performed early in building design. If a Building Energy Model (BEM) can be seamlessly generated from a Building Information Modeling (BIM) model, the energy simulation process can be much more efficient and better integrated in design. The concerns about BIM to BEM data transfer integrity and the reliability of simulation results are preventing wider adoption of BIM-based energy simulation. This study aimed to address these two obstacles and increase energy modelers’ confidence in using BIM for energy analysis. Green Building Studio (GBS) was used to simulate energy use and generate eQuest and EnergyPlus input files. Two building types were modeled in Revit with various iterations and BEM input files downloaded from GBS were compared line by line to identify and classify discrepancies. Simulation results from BIM-based and traditional modeling were compared to test reliability and showed unexpectedly good agreement across methods.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".