Improving clean energy greenhouse heating with solar thermal energy storage and phase change materials
Bibliographic record
Abstract
Abstract Greenhouses consume a great deal of energy to heat their building envelopes. The strategic integration of solar energy and thermal energy storage (TES) can help to boost energy performance and reduce the carbon emission in the sector. In this paper, the benefits of adding phase change materials (PCM) to the water tank of a solar heating system have been evaluated using the Transient System Simulation (TRNSYS) program. Initially, the hourly heating load of a reference greenhouse was evaluated using TRNSYS software. The results were validated with natural gas consumption data. The validated simulation was then used to investigate the impact of PCM on the performance of a large‐scale solar energy system. Four system configurations were evaluated; no PCM materials in the tank, then 20%, 40%, and 60% of the water tank volume occupied by PCM. Energy performance improvements of 10% to 14% were observed by increasing the proportion of PCM amounts over the baseline conventional system. Finally, an economic study was conducted to investigate the cost feasibility of different PCM concentrations. It was shown that PCM price, cost of natural gas, and carbon tax are the principal influence factors on the payback period.
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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 | 0.000 | 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".