Building Energy Performance Analysis: An Experimental Validation of an In-House Dynamic Simulation Tool through a Real Test Room
Why this work is in the frame
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Bibliographic record
Abstract
This paper focuses on the experimental validation of a building energy performance simulation tool by means of a comparative analysis between numerical results and measurements obtained on a real test room. The empirical tests were carried out for several months under variable weather conditions and in free-floating indoor temperature regime (switched off HVAC system). Measurements were exploited for validating an in-house simulation tool, implemented in MatLab and called DETECt, developed for dynamically assessing the energy performance of buildings. Results show that simulated indoor air and surface room temperatures resulted in very good agreement with the corresponding experimental data; the detected differences were often lower than 0.5 °C and almost always lower than 1 °C. Very low mean absolute and percentage errors were always achieved. In order to show the capabilities of the developed simulation tool, a suitable case study focused on innovative solar radiation high-reflective coatings, and infrared low-emissivity materials is also presented. The performance of these coatings and materials was investigated through a comparative analysis conducted to evaluate their heating and cooling energy saving potentials. Simulation results, obtained for the real test cell considered as equipped with such innovative coatings and material, show that for the weather zone of Naples a 5% saving is obtained both in summer and in winter by simultaneously adopting a high-reflectance coating and a low- emissivity plaster for roof/external walls and interior walls, respectively.
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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