On Formulation and Training of Grey-box Thermal Model for Low-rise Residential Buildings
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
This paper details the methodologies for creating and training grey-box thermal models for low-rise residential buildings. This paper covers different aspects of model development such as model vectorization, cost function definition and parameter estimation. Different computing strategies such as GPU accelerated calculation are also explored. This paper also briefly demonstrates the possibility of rearranging the model equation for predictive control purposes. The performance of an example grey-box model is tested against common datadriven machine learning models using a dataset of over 500 dwellings. Overall the grey-box model achieved good temperature prediction accuracy at a 4-hour forecast horizon and produced meaningful insights from the estimated parameters. The possibilities of using grey-box models for more advanced applications, such as model predictive control and remote auditing are also discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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