Specification of a greenhouse in cold climate condition, mathematical model and optimization
Bibliographic record
Abstract
In order to optimize the energy requirements (heating/cooling) of a multi-zone greenhouse, and investigate its heat recovery potential, a mathematical, dynamic energy model, coded in the MATLAB/Simulink platform, is developed. For validation, a case study in cold climate conditions is evaluated. This dynamic model, based on both energy and water vapor mass balances, was able to calculate the year-round monthly energy demand for the case study. The model calculations were compared with actual energy consumption data and were shown to have an accuracy between 6% and 15.5% for different months. The results highlighted the potential of applying a heat recovery strategy, whether with a Phase Change Material (PCM) or a Heat Recovery Ventilator (HRV). It is shown that using a HRV can reduce the energy demand of the greenhouse by 5% for January and 4% for December. Regarding the greenhouse radiation performance, the south roof contributes the most to solar heat gain in winter and summer, while the north wall makes the minimum contribution. Consequently, it is proposed to increase the area of the south roof and insulate the north wall. Thus, an asymmetrical roof configuration can receive 6% more solar radiation. Calculations show that an east-west greenhouse orientation lowers energy demand by 3%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".