Application of Thermal Batteries in Greenhouses
Bibliographic record
Abstract
One of the key issues confronting modern greenhouses is the need to supply the necessary energy in an environmentally friendly manner to facilitate heating and cooling processes within greenhouses. Solar radiation entering the greenhouse during the day can sometimes be more than the energy demand of the greenhouse. In contrast, there are cases where the greenhouse must dissipate a significant amount of heat, absorbed over a long period, either naturally or forcibly, during the cooling process. Moreover, the system’s efficiency could be enhanced if there is a mechanism capable of capturing heat expelled during greenhouse cooling and redistributing it on demand. Employing thermal energy storage is critical for maintaining stable temperatures, assuring energy efficiency, encouraging sustainability, and enabling year-round production. This technique ensures a safe environment for crops and eliminates temperature fluctuations inside the greenhouse. Nocturnal thermal energy storage, storing thermal energy during the daytime for later use at night, is essential to managing a contemporary greenhouse because it promotes consistent crop growth, sustainability, and profitability, particularly in areas with severe winters and significant day-to-night temperature variations. This work reviews various types of thermal energy storage systems employed in previous works focusing on greenhouse applications by researchers and categorizes them based on efficient factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".