On adaptive occupant-learning window blind and lighting controls
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Occupants have a significant impact upon building energy use, e.g. through the actuation of window blinds and switching off lights. Automation systems with fixed set points for controlling blinds and lights have been used in some applications as an attempt to mitigate the impact of occupant behaviour upon energy consumption. A conceptual framework of an alternative control method is presented, one in which the control system adapts control set points in real time to each occupant's preferences. The potential of this hypothesis is demonstrated through a simulation-based study focused on a hypothetical south-facing office with existing empirical models that predict occupant behaviour regarding the control of window blinds and lights. The performance of a proposed adaptive automation system is simulated, one in which window-blind and lighting control set points are adapted in real time to learn the modelled occupant preferences using a Kalman filter. The performance of this alternative occupant-learning method of control is contrasted to that of two conventional control methods, one in which occupants have manual control over window blinds and lights, and the other that employs an automation system with fixed set points. The simulation results indicate that such an adaptive occupant-learning control method could lead to substantial energy savings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it