Improved calibration of building models using approximate Bayesian calibration and neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deep energy retrofits of buildings are crucial to meeting climate targets and depend on calibrated energy models for investor confidence. Bayesian inference can improve the rigour in standard practice and improve confidence in calibrated energy models. Approximate Bayesian computation (ABC) methods using neural networks present an opportunity to calibrate energy models while inherently accounting for parameter uncertainty, and face less computational burden than the current standard process for Bayesian calibration. A case study for a large, complex building is presented to demonstrate the applicability of ABC and parameter sensitivity screening is found to result in over-confidence in the resulting inference by between 14% and 85%. Finally, the presentation of posterior distributions as independent distributions may be misleading, which can misattribute the true likelihood of parameters.Highlights Implementation of an Approximate Bayesian Computation method incorporating the Sequential Monte Carlo algorithm with a neural network surrogate model.A comparison of Bayesian inference with standard practice.An investigation of sensitivity screening for parameter selection on the inference results.Application to a complex multi-zone dynamic energy model of a large retail building.
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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 | 0.003 | 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 | 0.002 |
| 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 it