Using Model Calibration To Improve Urban Modeling
Bibliographic record
Abstract
Urban Energy Modeling (UBEM) often relies on "typical" buildings, called archetypes, to represent the modeled building stock.These archetypes include basic assumptions on parameters that play a significant role on energy use, such as thermal characteristics of the building envelope and occupancy-related parameters (e.g.setpoints and internal gains).These parameters are then combined to a geometrical approximation of the buildings to create a complete Building Performance Simulation (BPS) model.The increasing availability of measured data opens interesting perspectives for calibration but also new challenges.This paper compares different urbanlevel models of selected buildings (residential and commercial) with measured data.Detailed, calibrated models are also considered in the comparison, to represent "best achievable models".Results show that calibrated parameters from detailed models do not necessarily improve the accuracy of urban-level models.The simplifications in the selected urban-level archetypes have a significant impact in some cases, but a reasonable accuracy can be achieved for annual energy use.The dynamic profile, which would be required for energy flexibility and grid interaction studies, and for district energy assessment, is poorly represented by the selected urban-level models.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".