Greenhouse applications of solar photovoltaic driven heat pumps in northern environments
Why this work is in the frame
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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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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 | 0.001 |
| 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