Enhancing efficiency through integration of geothermal and photovoltaic in heating systems of a greenhouse for sustainable agriculture
Bibliographic record
Abstract
This study aims to calculate and analyze the energy requirement and improve the efficiency and sustainability of greenhouse (GH) agriculture by integrating renewable energy sources (RES) and advanced control systems into a hybrid solar-GH heating and cooling solution. The proposed system incorporates both geothermal and photovoltaic (PV) energy to optimize performance, particularly during the colder seasons. The analysis focuses on three widely used double-layer polyethylene materials in Canadian GHs, Thermax, SolaWrap, and SunSaver, evaluating their thermal insulation and light management properties. For a practical test, a typical 1000 GH is designed to calculate the energy requirements for heating and cooling, specifically tailored to the climatic conditions of Regina, using the three types of polyethylene. An energy and cost analysis is performed considering various energy sources to meet these demands. The research calculates and compares the power generation from building-integrated photovoltaics (BIPV) with traditional maximum power point tracking (MPPT) PV systems and integrates a 5-stage controller with HVAC damper control to further optimize energy consumption under different scenarios. The simulations performed using TRNSYS and SketchUp provide detailed monthly energy consumption and cost breakdowns for each polyethylene type. Additional insights include monthly power generation from both PV and BIPV systems, as well as contributions from geothermal energy . The findings present a comprehensive energy analysis, highlight potential cost savings and system efficiencies, and offer valuable information to advance sustainable GH agriculture in cold climates.
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".