Zone-Level Anomaly Detection in VAV Terminal Units Using an Unsupervised Learning Approach
Bibliographic record
Abstract
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. On the other hand, conventional fault detection and diagnosis techniques mostly depend on pre-established lists of fault alarms resulting in the potential for anomalous occurrences that defy these fault classifications. This study presents a novel unsupervised approach for detecting anomalous zones in variable air volume (VAV) air handling units (AHUs). The method utilizes autoencoders (AE) and principal component analysis (PCA), based on zone-level trend logs. To evaluate the effectiveness of the proposed approach, a case study was conducted using data collected from a 71-zone VAV AHU system of an educational building in Ottawa, Canada. The proposed PCA-AE method successfully identified four anomalous zones by considering four zone-level trend logs. The findings demonstrated great adaptability for detecting a wide range of zone anomalies in VAV AHUs giving operators valuable insights about the system and notify of potential faults at early stage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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 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".