POST-OCCUPANCY EVALUATION AND PARTIAL-CALIBRATION OF 18 DESIGN-PHASE ENERGY MODELS
Bibliographic record
Abstract
This paper evaluates the accuracy of 18 design-phase building energy models, used for documentation for LEED Canada certification, and analyzes the effectiveness of simple model calibration steps applied to these models. The calibration steps included inputting actual weather data, adding unregulated loads, revising process loads (often with submetered data), and updating a minimal number of other inputs. In net, the design-phase energy models under-predicted the total measured energy consumption by 36%. Following the above outlined calibration steps, this error was reduced to a net 7% under-prediction. For the monthly Energy Use Intensity (EUI) the coefficient of variation of the root mean square error improved from 45% to 24%. Revising the process loads was particularly important in these cases. This step alone increased the EUI by 32% on average (15% median) in the models. This impact far exceeded that of calibrating the weather data, even in a sensitivity test using extreme weather years. These results suggest that although compliancetype energy models can be poor predictors of actual energy use, practitioners may be able to make initial strides toward calibration with relatively little effort.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".