Joint window shading and space conditioning controls using data-driven linear predictive control techniques
Bibliographic record
Abstract
Considering the prevalence of large-glazed buildings in major metropolitan areas and emerging smart building systems, this research focuses on the design and evaluation of window shading controls and evaluates opportunities for the joint control of window shading and space conditioning to achieve both thermal comfort and energy demand reductions. The study proposed three predictive control techniques: a simple rule-based predictive, a rollout approach derived from linear programming, and a mixed-integer linear programming (MILP). With practicality in mind, the selection of exogenous and actuation variables only consists of what can be obtained from a connected residential thermostat: heating or cooling runtime and air temperature. Through illustrative simulations, this study demonstrated that the predictive controls of the space conditioning system and window shading system are capable of maintaining a setpoint temperature during occupied periods while the energy decreasing the heating energy demand by 10% during the shoulder season, especially in April, with negligible savings on cooling energy. The illustrative simulations also showed that the rollout algorithm out-performed a more complex MILP approach, which suggest that a simpler, human-interpretable approach can sometimes be sufficient for delivering an optimal result, and that complexity does not always translate to optimality.
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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 | 0.001 |
| 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".