Possibilities of Using Fuel Cells for Energy Generation in Agricultural Greenhouses: A Case Study in Crete, Greece
Bibliographic record
Abstract
The possibility of using fuel cells powered by solar hydrogen for energy generation in greenhouses with reference to the island of Crete, Greece has been examined. Change of fossil fuels used in greenhouses with renewable energies and sustainable energy technologies is very important for mitigation of climate change. Various renewable energy sources and low carbon emission technologies including geothermal energy, biomass, solar photovoltaics and co-generation systems have been used so far. Use of solar photovoltaics for generating electricity consumed in water electrolysis for hydrogen production has been investigated. Hydrogen feeding a proton exchange membrane fuel cell co-generating electricity and heat was used in a greenhouse located in Crete, Greece. The system could be useful in a stand-alone greenhouse with annual specific energy consumption at 150 KWh/m2. A solar photovoltaic system with nominal power at 33.33 KWp powering an electrolytic cell at 5.71 KW could produce annually 2,083 kg hydrogen. The hydrogen could feed a fuel cell at 1.71 KWel generating annually all the electricity required in a greenhouse of 1,000 m2. Co-produced heat could also cover 11.11% of the annual heat requirements in the greenhouse. It was found though that the overall electric efficiency of the system was very low at 4.5%. The low overall efficiency and the size of the solar-PV required indicate that the abovementioned energy system is not suitable in commercial agricultural 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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".