Development of a New Dynamic Energy Model for Commercial and Small-scale Greenhouses: Validation and Practical Applications
Bibliographic record
Abstract
A transient lumped capacitance model capable of predicting the greenhouse microclimate based on external weather conditions and site properties was developed and extensively validated using data from several sites in southern Ontario. Both large, complex commercial operations, and small-scale, passive greenhouses were considered in the validation cases. The model considers conductive, convective and radiative heat transfer between internal greenhouse layers, as well as latent heat transfer from crop evapotranspiration. The impact of environmental control technology commonly used at commercial greenhouses, such as supplemental heating and lighting, forced and natural ventilation, dehumidification technology, and curtains (energy and light abatement) are included in the model. To assess the predictive accuracy, the root-mean squared error (RMSE) and mean absolute error (MAE) between measured and simulated values were calculated for each test site. Once validated, the model was used to predict energy savings from running dehumidification technology and switching from HPS to LED lights in the southern Ontario context. A scoping study was conducted to estimate the annual greenhouse heating load at locations across Canada using typical meteorological year (TMY) data, with an emphasis on identifying suitable areas for seasonal energy storage. The impact on greenhouse heating and cooling loads that can be expected by using an earth air heat exchanger (EAHE) at a commercial-size greenhouse was also examined at locations across Canada using TMY data.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".