Retraining surrogate models in increasingly restricted design spaces: a novel building energy model calibration method
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
Surrogate (i.e. meta) models can approximate building energy models (BEMs) accurately and quickly, hence they have been widely used in BEM calibration studies. Typically, the surrogate models are trained a single time over the entire unknown building parameter space with a design such as Latin hypercube sampling. In this article, a multiple polynomial regression surrogate model is, instead, retrained with increasingly restricted designs. In each training repetition, the bounds of the design narrow around the unknown building parameter values that minimize the error between the surrogate model’s predictions and the measured energy. This ‘cascading surrogate’ calibration method finds CVRMSE values that are much lower than those of a powerful black box optimizer in a case study with simulated ‘measured’ data. However, the method has similar performance to the black box optimizer in a case study with real hourly measured energy, probably since the BEM was not configured accurately enough.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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