A New Taxonomy for Energy Management in Indoor Greenhouses: Modeling Plants as Distributed Energy Resources
Bibliographic record
Abstract
Indoor farming in controlled greenhouses is becoming increasingly widespread due to the urgent global need for food and its ability to address challenges posed by climate change and extreme environmental conditions. However, it requires costly, energy-intensive supplemental lighting, raising concerns about economic feasibility and increased energy demand from power systems. To address these concerns, recent studies have explored lighting strategies that manipulate different lighting factors, such as light quantity and spectra, aiming to reduce costs, increase energy efficiency, and optimize plant growth and productivity. This review highlights these lighting strategies while reporting on both positive and negative effects on plant growth, as well as resultant cost and implications for indoor greenhouses. The reviewed studies indicate that advanced lighting strategies can reduce energy consumption and costs without negatively affecting plant health, achieving reductions of up to 52% in settings with no natural light and up to 92% when sunlight is incorporated. Additionally, we propose a novel taxonomy for mapping different lighting strategies to distributed energy resources, thus positioning indoor greenhouses as microgrids to improve energy management. This taxonomy serves as a foundation for reviewing previous studies that making this review a valuable reference for comparing a broad range of lighting strategies. Furthermore, the proposed mapping aids in translating plant requirements into power system concepts. This framework supports the development of advanced lighting strategies and opens up new research avenues of research that address the needs of the power and agricultural sectors.
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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 | 0.000 | 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) | 0.000 | 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".