Gray-box virtual sensor of the supply air temperature of air handling units
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
Building automation system uses several networks of sensors for continuous monitoring of building control systems for energy-efficient operation. Physical sensors are costly and need frequent calibration. The accurate measurement of supply air temperature from the air handling units (AHUs) has an important effect on the control of cooling coil, supply air temperature and supply airflow rate delivered to rooms. In the case when such a sensor gives erroneous measurements, a virtual sensor can replace temporarily the faulty sensor, and it can also be used for automated fault detection of HVAC systems. This article proposes two different gray-box (models A and B) for predicting the supply air temperature of air handling units that were developed and tested using the measurements from two buildings. The models require the measurement of three variables (mixed air temperature, cooling coil valve signal, and chilled water inlet temperature). The results of both models are discussed and compared. A sliding window approach for cross-validation of the models was also carried out. The developed gray-box models A and B could be integrated into BAS for virtual measurement, virtual calibration, and fault detection in HVAC systems.
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 | 0.000 | 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 | 0.001 |
| 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