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Record W7062278216

THERMAL ENVIRONMENT MODELLING OF THE MONO-SLOPE SOLAR GREENHOUSE FOR COLD REGIONS

2018· dissertation· en· W7062278216 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2018
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouseSolar energyPopulationChinaGreenhouse gasClimate changeSolar greenhouse
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.562

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.012
GPT teacher head0.162
Teacher spread0.150 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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