THERMAL ENVIRONMENT MODELLING OF THE MONO-SLOPE SOLAR GREENHOUSE FOR COLD REGIONS
Bibliographic record
Abstract
The extremely cold outdoor temperatures in winter continue to be a barrier for the greenhouse growers. In Saskatoon, for example, it is less than -31.5℃ for 1% of the year (ASHRAE, 2013). This limits the growth of the greenhouse industry in Saskatchewan which has around 250 billion square meters of farmland, and accounts for 38.5% Canada’s farm area (Statistics Canada, 2016). Due to this fact, most traditional Canadian greenhouses in the Canadian Prairies shut down during the coldest months (from November to February) because of heavy heating bills. However, the local demand for food in the winter has been increasing in Saskatchewan due to a rise in population and consciousness of healthy food. If compare traditional local greenhouses with other greenhouse production techniques, Chinese mono-slope solar greenhouses do not primarily rely on supplemental heating. They rely on solar energy to maintain the indoor temperature. Fortunately, Saskatchewan has the most hours of sunshine annually in Canada which theoretically provides a favorable environment for the establishment and development of mono-slope solar greenhouses (Environment Canada, 2017). This also greatly reduces heating costs.\nThe objective of this study was to evaluate the thermal environment and predict the energy consumption of solar greenhouse production in Saskatchewan. This was done using an existing simulation model RGWSRHJ that was developed by Chengwei Ma in China (Ma, 2015). Several modifications were made to make the model SOGREEN that is suitable for the cold climate in Saskatchewan. These modifications included meteorological year data invoking, advanced front roof covering, summer solar screen, and so on. Later, the modified simulation model SOGREEN was validated using field data that were collected in a solar greenhouse in Elie, Manitoba. Solar greenhouse production was simulated under the weather conditions in Saskatoon, Saskatchewan. Finally, the energy consumption was analyzed using the simulated data to select the most suitable and economical energy resource for solar greenhouse production in cold regions.\nFrom the validation results, there were 9.6% and 13.7% discrepancies in the model’s predictions of indoor temperature and relative humidity, respectively. This has demonstrated that the modified model could simulate the thermal environment of a solar greenhouse with a relatively high accuracy. While the simulation results confirmed that a large amount of energy was used for supplying heat from November to March, there was almost no supplemental heat needed between April and August. This illustrated that solar greenhouses can fully utilize the solar energy, dramatically reducing the annual energy consumption. \nFrom an energy cost analysis, $26378.56, $2498.51 and $2610.00 was spent for supplemental heat with electricity, natural gas, and coal. Therefore, among these three energy resources, natural gas was the most affordable and most environmentally friendly option for greenhouse production. Compared with the natural gas expenses of Grandora Gardens, vegetable production in a solar greenhouse can save as much as 83.6% in energy costs. This demonstrates that solar greenhouse production in Saskatchewan is in fact economical for the Canadian Prairies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".