Optimal Strategy for Energy-Efficient Management of Greenhouse Climate Control
Bibliographic record
Abstract
In this research, we present a multi-objective optimization model for efficient energy management of greenhouse (GH) microclimate control systems. The study aims to minimize energy costs and peak demand while controlling the GH temperature, humidity, andCO2levels within a predefined range. A mathematical model is developed to quantify the power consumption of GH components, including lighting, heating, cooling,CO2injection, and ventilation systems, considering electricity costs based on time-of-use (ToU) tariffs distinguishing peak and offpeak periods. Multi-objective convex optimization, implemented in MATLAB, optimizes control decisions for these systems over 24 hours in 15-minute intervals. The decision variables, ranging from 0 (off) to 1 (fully on), determine the operational states of each system. The model uses historical weather data from Regina, Canada, on July 1st (as a sample day), applied to a 1000m2GH in the simulation. To verify the feasibility and test the effectiveness of the proposed control and optimization method, also to provide a sensitivity analysis based on changes in weather elements, including temperature, solar radiation, and wind speed, we perform the proposed strategy over the first day of each month for every 12 months of the year. Tomato cultivation is chosen for GH due to its compatibility with the controlled environment (CO2, relative humidity (RH), and temperature). The findings reveal a remarkable up to 43.13% energy savings a day with the optimized strategy compared to the base scenario of the GH control system. The results illustrate optimal activation patterns for GH systems throughout the day, offering a practical tool for smart GH energy management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".