Daylight Harvesting with Nonlinear Neural Network Control for Supplemental Lighting Systems with Saturated Inputs*
Bibliographic record
Abstract
This paper presents a nonlinear neural network-based control technique for daylight harvesting in a simulated greenhouse environment using light sensors, dimmable LED lights, and sunlight. The proposed controller can be applied to a system consisting of Internet-of-Things (IoT)-enabled sensors and light fixtures that can communicate with a central controller in a distributed computer network. The main objective of the lighting system is to achieve desired amounts of light in the photosynthesis spectrum by utilizing minimum supplemental electrical energy through intelligent daylight harvesting. To this end, we present a two-layer, nonlinear neural network controller with dynamic adaptive weights that achieve small errors and a stable closed-loop system in the presence of disturbances and modeling uncertainties. The novel aspect of the proposed controller is its inherent ability to address prevalent saturation of power drivers in LED lighting sensor-based feedback systems. The stability of the closed-loop system is further analyzed and simulation results are presented to validate the performance of the daylighting control approach.
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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 | 0.001 | 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 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".