THERMAL ENVIRONMENT MODELING AND OPTIMIZATION OF GREENHOUSE IN COLD REGIONS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thermal simulation models for the time-dependent heating requirement of greenhouses are very important for the evaluation of various energy-saving technologies, and energy-efficient design of greenhouses based on local climates. A quasi-steady state thermal model “GREENHEAT” was developed using the programing language MATLAB for simulation heating requirement in conventional greenhouses. The model could predict the hourly heating requirement based on the input of hourly weather data, indoor environmental parameters, and physical and thermal properties of greenhouse building materials. The model was validated with measured data from a commercial greenhouse located in Saskatoon, Canada, and the monthly average error in prediction was found to be less than 5.0%. This study also reviewed various energy-saving technologies used in greenhouses in cold climate, and the GREENHEAT model allowed selections of commonly used ones in the simulation. The GREENHEAT model was used for evaluating the impact of various geometrical parameters on the heating requirement of the single span and multiple-span conventional greenhouses located in Saskatoon. Results showed that the east-west oriented gable roof greenhouse could be more energy-efficient for the multi-span gutter connected greenhouse whereas quonset shape as a free-standing single span greenhouse. The large span width could be beneficial for the single-span greenhouses, but the impact of increased span width could be negligible on the heating demand of multi-span greenhouses. The model was also used for an economic feasibility analysis of year-round vegetable production (tomato, cucumber, and pepper) in northern Saskatchewan, and tomato was found to be the most economical vegetable as compared to the cucumber and pepper.\nAnother heating simulation model CSGHEAT was developed to estimate of the supplemental heating requirement of mono-slope Chinese-style solar greenhouses (CSGs). This model is also a quasi-steady state thermal model using the programming language MATLAB, and it can simulates the hourly heating requirement of CSGs. The model was validated with experimental data from a CSG located in Winnipeg, Manitoba. The average error for prediction of the hourly heating requirement was found to be less than 8.7%. The model sensitivities to various geometrical and thermal parameters were studied. The results indicated that the thermal properties of cover, thermal blanket, and parameter insulation were the most important design parameters in CSGs. \nFinally, the heating requirement in CSGs was modeled using TRNSYS simulation tool, and the predictions were compared with that of CSGHEAT. The result indicated that TRNSYS had serious limitations for modeling of greenhouse thermal environment, thereby high uncertainties could occur, thus was not suitable for greenhouse simulation.
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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 it