Detection of Abnormal Behaviour of Wireless Sensors in School Buildings Using Dynamic Time Warping
Bibliographic record
Abstract
Abstract An anomaly is an observation that highly deviates from other observations. These anomalies create abnormal time series that are different from a collection of other time series. Data collected using wireless sensors, such as temperature and humidity, can provide insight into a building's heating, ventilation, and air conditioning (HVAC) system. When using sensors that are properly designed, installed and calibrated the indoor environmental quality of a building can be measured. This will allow anomalies to be identified through sensor measurements, which point to areas with poor design or insufficient maintenance. Identifying these can improve both thermal comfort and energy efficiency and improve building performance. In this study, we applied the Dynamic Time Warping (DTW) based anomaly detection method to identify anomalies and introduced a scoring method to identify abnormal sensors. The number of anomalies, vertical distance to an anomaly point, and DTW distance was considered to identify abnormal sensors. Then we used high-resolution temperature measurements from two school buildings using wireless sensors to evaluate the performance of the developed scoring method. Based on the results, visually we could observe that the method accurately detects the abnormal sensors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".