Research on multi-parameter cooperative control of smart opening and closing windows based on feed-forward neural network
Bibliographic record
Abstract
Control techniques of Smart windows using Multi-parameter neural feedforward systems as a control strategy shows great potential in improving not only the energy ef iciency geometrically but also the building's indoor environmental quality.In this study, a new smart window control is developed that is based on neural networks which are able to implement multi control strategies in various conditions with regard to temperature, humidity, light and air quality.This allows for a further development of the system: irstly, it thoroughly presents the model, which facilitates the understanding of the mathematic modeling of windows' dynamic position and, at the same time, shows how the neural network works.The structure comprises a perception layer, which provides perception of the environment, processing layer for analysis and decision making on the input data, and the last action layer that performs windows' actuation and gives feedback on the action implemented.In terms of the system's control ef iciency, timing, energy consumption and seeking users' satisfaction, the performance of this control system outperforms other existing systems in empirical application.The control accuracy attained in the proposed system is 97.8%.What is more interesting about this approach is the energy ef iciency which stands at 94.3%, this is only the bare minimum, estimation says it surpasses the rest by a great deal.The successful realization of this control system is an important step toward the development of smart buildings that can be relied on for excellent results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".