Best combinations of energy-efficiency measures in greenhouses considering energy consumption, yield, and costs: Comparison between two cold climate cities
Bibliographic record
Abstract
Greenhouse agriculture is enjoying a surge in popularity to increase food security and use resources efficiently. Less known is that greenhouses consume enormous amounts of energy for heating and lighting. Energy efficiency is paramount in greenhouse production, but choosing the best measures is challenging and depends on climate and energy tariffs. The novelty of this study is to investigate and compare multiple practices and their cumulative impacts in high-latitude greenhouses from energy, cost, and yield points of view. It focuses on simulating the energy consumption and yields in greenhouses under 31 energy-saving scenarios and in two different locations, Copenhagen (Denmark) and Montreal (Quebec, Canada). Various lighting and energy-saving techniques are explored, including high-pressure sodium (HPS) and light-emitting diode (LED) lighting, canopy interlighting, thermal screens, additional envelope insulation, and a heat harvesting system. Greenhouses in Copenhagen consume more energy due to artificial lighting to compensate for low solar radiation in winter. Energy costs are, on average, 77 % higher than in Montreal, partly due to high energy prices. The best scenario regarding energy operational cost per yield for Montreal is LED toplights with thermal screens and envelope insulation, and for Copenhagen it is LED toplights with thermal screens and a heat harvesting system. However, if growers wanted to implement only one measure, the results showed that LED toplights is the best measure to implement for both locations due to its high energy efficiency and minimal impact on yield. These results provide insight into the best energy efficiency measures tailored to specific locations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".