Prediction of Energy Efficiency in a Thermal Storage Wall System by the Modified Model
Bibliographic record
Abstract
The Trombe wall system is a paradigmatic example of passive solar construction, providing thermal control of internal spaces through the efficient utilization of solar radiation. The temperature variability within the air channel is a critical parameter determining the system's functional effectiveness. This research introduces a refined thermal model to estimate the energy efficiency of a traditional Trombe wall, based on variables including incident solar radiation, ambient temperature near the wall face, and conditions at the glazing near the upper end of the channel. Furthermore, it employs the k-nearest neighbors (KNN), linear regression, random forest, and decision tree algorithms to predict system efficiency based on the aforementioned temperature metrics. Empirical results indicate that the KNN and random forest models achieved zero error in the initial test simulation, in stark contrast to the linear regression and decision tree methods, which exhibited errors of 0.2785 and 0.2291, respectively. Additionally, the modified thermal model demonstrated a strong agreement with experimental data, showing a deviation of less than 5% for room temperatures.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".