Prophet model for forecasting occupancy presence in indoor spaces using non-intrusive sensors
Bibliographic record
Abstract
Abstract. The Internet of Things is a multi-sensor technology with the unique advantage of supporting non-intrusive and non-device occupancy detection, while also allowing us to explore new forecasting occupancy models. However, forecasting occupancy presence is not a trivial task, since it is still unknown the main criteria in selecting a forecasting modelling approach according to a non-intrusive sensing strategy. Towards this challenge, this paper proposes an analytical workflow developed to support the Prophet model for forecasting occupancy presence in indoor spaces throughout the tasks of sensing, processing, and analysing event triggered data generated from ten non-intrusive sensors, including motion, temperature, luminosity, CO2, TVOC, sound, pressure, accelerometer, gyroscope, and humidity sensors. The usefulness of this analytical workflow is demonstrated with the implementation of an IoT platform for an experiment operating non-intrusive sensing in a classroom. The assessment is made at different time intervals and the results confirm that there is a relationship between the event-count and occupancy presence in such a way that the larger the number of events triggered in an indoor space, the higher the probability of an indoor space being occupied.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".