Online Unsupervised Occupancy Anticipation System Applied to Residential Heat Load Management
Bibliographic record
Abstract
Human preferences and lifestyles significantly impact buildings' energy consumption. Consequently, a better understanding of occupants' behavior is crucial to decrease energy consumption and maintain occupants' comfort. Occupant-centric control (OCC) strategies are effective approaches to fulfil such a purpose. As such, occupancy detection and prediction are of prime importance, particularly to manage Electric Space Heating (ESH) systems, due to the relatively slow dynamics of the temperature in dwellings. This paper proposes an Explicit Duration Hidden Markov Model (EDHMM) for unsupervised online presence detection and a hazard-based approach for occupancy prediction. Moreover, a control strategy using a cost function, weighted by occupancy predictions, and a load-shifting strategy based on time-varying electricity price are put forward. This work initially validates the consistency of the proposed approach by using synthetic data generated by a Monte Carlo simulation. Subsequently, the performance of our framework is compared with previous methods presented in the literature through experimental validation. Results demonstrate that the proposed EDHMM approach is efficient in detecting occupancy states. Besides, the results of the field implementation show the potential of the proposed control strategy to preserve occupants' thermal comfort while decreasing the heating energy consumption.
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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 | 0.000 |
| 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 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".