Applicability of Multivariate Linear Regression in Building Energy Demand Estimation
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
The vision of the research project is to find an energy optimal building configuration, suitable for specified requirements and restrictions. The first step on this way is to create a measure to compare building configurations, faster than explicit energetic simulations. The current study examines the applicability of multivariate linear regression to support the solution of building optimization problems. During the study, multivariate linear regression models were created to estimate the expected annual heating energy demand of building configurations and examined their accuracy Between examinations, the models were modified so that the complexity was increased only to such an extent that the approximation was still sufficiently accurate. The result was a multivariate linear model that estimated the expected output for unknown descriptive variables with a 0% relative error and a 1.6% standard deviation. The R2 point of the estimates was 0.9884. Based on these, the model was considered applicable in the search space defined by the training patterns.
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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 | 0.000 |
| 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