Occupancy detection in a residential building using sensor fusion dataand machine learning algorithms
Bibliographic record
Abstract
This research implemented sensor fusion to predict the occupancy status of a residential building.A room in a residential building located in Edmonton, Alberta was used as the testbed for this study.To predict the occupancy, data including room temperature, relative humidity, CO2 concentration, and day of the week were collected from March to April 2022.The actual occupancy status of the room was collected using a Passive Infrared (PIR) motion sensor and the data sheets filled by the occupants.The study considered four different machine learning algorithms including K-nearest neighbors (KNN), Gaussian Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Tree (DT) to predict the state of occupancy and compared the accuracy of these methods.The results showed KNN method outperforms the other methods by reaching the Geometric Mean (GM) accuracy of 94% for occupancy prediction.In addition, it investigated the sufficiency of temperature and humidity sensors for occupancy detection and studied the importance of using recent data for occupancy prediction in residential buildings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".