Energy-Efficient Supplemental LED Lighting Control for a Proof-of-Concept Greenhouse System
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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".