Smart Grid Enabled Indoor Farming: A New Recipe for Energy Management Using Lighting Control
Bibliographic record
Abstract
Indoor farming allows for year-round food production, however, its reliance on supplementary artificial lighting significantly strains the grid by increasing energy demand, peak load, and resultant energy costs. Recent research shows that plants can tolerate interruptions in light, thus enabling control mechanisms to strategically schedule lighting as a function of time varying energy prices. These schedules are known as lighting “recipes” with a duration of 24 hours, which can be aligned with day-ahead pricing to optimally schedule lighting intensity to achieve energy cost savings and improve load flexibility. This paper proposes an optimal lighting control strategy that generates a daily lighting recipe with the objectives of reducing daily energy costs and monthly peak demand charges. Plant health considerations, such as minimum light intake and adequate dark/lighting intervals, are formulated as mathematical constraints. A model predictive control approach is used to solve for the optimal lighting recipe. Comprehensive simulations for a one-hectare greenhouse using real-world electricity prices from the Ontario system operator reveal an annual energy cost reduction of ${\$}$ 281,000(22.6%) and a peak load reduction of 850 kW (18.4%). The results indicate the potential for indoor farming operations to become flexible resources within the smart grid paradigm.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".