Model-based predictive control of office window shades
Bibliographic record
Abstract
In the automation of interior window shading devices, a control system that relies on a prediction of environmental conditions and a building's thermal response can provide savings to space-conditioning loads beyond what can be achieved using a reactive approach. The development of these control strategies can be difficult because of the uniqueness of each building. A simplified model-based predictive control (MPC) method for window shades is proposed. To this end, a control-oriented model representing the heat transfer problem in a perimeter office space was developed. The parameters of the model were estimated using the ensemble Kalman filter (EnKF). The energy-savings potential of the EnKF-based MPC approach for window shades was investigated using EnergyPlus simulations. This was accomplished by implementing the control-oriented model into the energy management system application of EnergyPlus. Simulations were conducted to assess the energy saving potential of using the EnKF-based MPC for roller blinds in a south-facing perimeter office space in Ottawa, Canada. The simulation-based results indicate the potential for about 35% reduction in electricity usage for space conditioning over manually operated interior roller blinds.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".