Optimizing Greenhouse Sustainability: A Comprehensive Thermal Model for Assessing Alternative Covering Materials and Energy Efficiency
Bibliographic record
Abstract
<b><sc>Abstract.</sc></b> This study aims to compare new greenhouse covering materials and construction shape to propose an optimal combination for small to mid-scale greenhouse producers in cold regions. Small single-span greenhouses commonly use polyethylene, resulting in significant plastic waste due to the need for replacement every three to five years. To address this issue and minimize heating and cooling loads in cold regions, new covering materials with improved durability and energy efficiency are being developed. These materials, impacting both heat transfer and luminosity, necessitate a comparison of spectral and thermal properties through a thermal model. Unlike previous models that use constant parameters or have lengthy computation times, a fast, comprehensive, and license-free model was needed. Hence, a year-round model has been developed with Python to predict the hourly heating and cooling loads for both conventional and alternative greenhouse constructions. This model takes detailed parameters into account, including crops, construction and covering materials, greenhouse configurations, and localization. It uses hourly weather data readily available throughout North America, including temperature, humidity, atmospheric pressure, cloud cover, wind speed, and solar irradiance. The model calculates heat losses and gains through the roof, walls, perimeter, and ground, considering longwave and shortwave radiations, conduction/convection, infiltration, and energy sinks/sources induced by plant evapotranspiration or environmental control systems. Preliminary results indicate that the model effectively predicts the heating and cooling loads of a twin wall polycarbonate greenhouse located in the province of Quebec, Canada. Measurements were conducted during one month with thermocouples, pyranometers pyrgeometers and climate collected was controlled with a Maximus greenhouse automation system.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".