Greenhouse applications of solar photovoltaic driven heat pumps in northern environments
Bibliographic record
Abstract
Greenhouses play a crucial role in food production and economic growth in northern regions but contribute significantly to energy consumption and carbon emissions . To address this challenge and enhance food production sustainably, there is a growing need for efficient and renewable energy solutions. Low-carbon heating in greenhouses will be achievable by using heat pumps powered by cost-effective renewable energy sources such as photovoltaic systems. This study introduces an open-source quasi-steady-state thermal model for greenhouses, non-ideal air-source heat pumps (ASHPs), and ground-source heat pumps (GSHPs) with both vertical (V) and horizontal (H) ground heat exchangers . Additionally, a ventilation sub-model is provided to manage cooling loads for residential, semi-commercial, and commercial greenhouses. Furthermore, an open-source SAM-Python-based photovoltaic system model is developed to size photovoltaic arrays for powering the heat pumps. The study reveals a nonlinear relationship between greenhouse size and annual thermal loads . It also demonstrates that ASHPs exhibit the lowest efficiency (COP h = 2.52, EER c = 9.00), followed by VGSHPs (COP h = 3.68, EER c = 19.88), with HGSHPs being the most efficient (COP h = 3.79, EER c = 19.48) for the Canadian case study . The required on-grid photovoltaic ratings to power HGSHPs, VGSHPs, and ASHPs respectively are 2.16, 2.17, and 2.64 kW for residential, 103, 104, and 128 kW for semi-commercial, and 827, 831, and 1,028 kW for commercial greenhouses. Self-consumption of designed photovoltaic systems ranges from 23.5 % to 25.1 %, with self-sufficiency varying between 23.7 % and 26.0 %. The size of the photovoltaic system is competitive with similar scenarios; however, future studies are needed to conduct an economic analysis while simulating the dynamic loads of greenhouses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".