Energy-Efficient Supplemental LED Lighting Control for a Proof-of-Concept Greenhouse System
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a multiple-input multiple-output control system integrated with daylight harvesting is presented as a proof-of-concept system for energy-efficient greenhouse lighting. The control objective is to regulate the intensity of dimmable multispectrum light-emitting diode (LED) fixtures for achieving desired spectral irradiance levels and color ratios while utilizing the natural sunlight as much as possible. To ensure stability and improved dynamic performance, a Smith predictor is utilized to compensate for the delay introduced into the system by the communication hardware. A stability analysis of the closed-loop system is presented considering system delay and modeling uncertainties. The proposed control system was experimentally tested in a grow-tent environment consisting of dimmable halogen lights that emulate daylight variation, multispectrum dimmable LED fixtures, and low-cost light sensors. The results indicate that about 30% energy savings can be achieved by using the proposed automated lighting control system.
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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 it