Plants as Distributed Energy Resources? A New Taxonomy for Energy Management in Indoor Greenhouses
Bibliographic record
Abstract
Indoor vertical 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, indoor farming requires the deployment of costly and energy intensive supplemental lighting systems, thus posing challenges with respect to 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 not only to reduce costs and increase energy efficiency but also to optimize plant growth and productivity. Motivated by these reasons, this review paper aims to bring these lighting strategies to the fore while reporting on both positive and negative effects on plant growth, as well as resultant cost and implications for indoor greenhouses. We propose a novel taxonomy for mapping different lighting strategies to distributed energy resources, thus positioning indoor greenhouses as microgrids to improve energy management. In this context, the proposed taxonomy provides a basis for reviewing prior studies that simultaneously improve energy efficiency and plant production, which leads to the proposal of new avenues of research that address the needs of the power and agricultural sectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".