Unsupervised identification of zone-level anomalies in VAV terminal units utilizing autoencoders and PCA
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 effective operation of HVAC systems is crucial to minimize energy inefficiencies and occupant discomfort. However, these systems can experience various problems, including hardware and software-related anomalies. In contrast to most existing fault detection and diagnostic approaches, which rely on simple rules and alarms, this study introduces novel unsupervised approaches for detecting zone anomalies in variable air volume (VAV) air handling units (AHUs). The methods utilize autoencoders (AE) and principal component analysis (PCA). To evaluate the effectiveness of the proposed methods, both a synthetic dataset and measured data from a 28-zone VAV AHU system were investigated. The proposed method successfully detected several zone temperature and airflow anomalies using the AE-based method, and several zone anomalies were also identified using the PCA-AE approach by considering four commonly available zone-level trend logs in VAV AHUs namely temperature, airflow, airflow set-point, and VAV terminal damper position. The findings demonstrated the great adaptability of the proposed methods in detecting a wide range of zone anomalies in any modern building equipped with VAV AHUs, giving operators valuable insights about the system and notifying them of potential faults at an early stage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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