Lessons Learned from Building Energy Modelling and Hybrid Calibration: Institutional Building Case Study
Bibliographic record
Abstract
This thesis presents a comprehensive calibration workflow for the University of Toronto Exam Centre, aiming to bridge the persistent gap between simulated and measured building energy performance. A high-resolution white-box model was developed in DesignBuilder and exported to EnergyPlus, with calibration executed through three iterative layers: manual tuning of control logic and internal gains, data-driven Bayesian optimization using HyperOpt, and a final two-stage linear regression wrapper to correct systematic residual bias. Calibration focused on zone temperature and sensible cooling for two representative thermal zones—Lobby and North Perimeter—selected for their distinctive behavior and sensor data. Results demonstrate that combining physical reasoning with statistical correction significantly improves predictive fidelity while preserving model interpretability. The final digital twin supports real-time diagnostics and energy analysis. This work offers a transparent, replicable methodology for calibrating complex institutional buildings and contributes insights into the limitations and strengths of hybrid calibration strategies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".