Optimal Energy Management of Greenhouses in Smart Grids
Bibliographic record
Abstract
This paper presents a novel hierarchical control approach and new mathematical optimization models of greenhouses, which can be readily incorporated into energy hub management systems (EHMSs) in the context of smart grids to optimize the operation of their energy systems. In greenhouses, artificial lighting, CO2 production, and climate control systems consume considerable energy; thus, a mathematical model of greenhouses appropriate for their optimal operation is proposed, so that it can be implemented as a supervisory control in existing greenhouse control systems. The objective is to minimize total energy costs and demand charges while considering important parameters of greenhouses; in particular, inside temperature and humidity, CO2 concentration, and lighting levels should be kept within acceptable ranges. Therefore, the proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing control systems in greenhouses. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied through Monte Carlo simulations. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".