Experimental Evaluation of Hybrid Renewable and Thermal Energy Storage Systems for a Net-Zero Energy Greenhouse: A Case Study of Yeoju-Si
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Bibliographic record
Abstract
The implementation of renewable energy systems (RESs) in the agricultural sector has significant potential to mitigate the negative effects of fossil fuel-based products on the global climate, reduce operational costs, and enhance crop production. However, the intermittent nature of RESs poses a major challenge to realizing these benefits. To address this, thermal energy storage (TES) and hybrid heat pump (HHP) systems are integrated with RESs to balance the mismatch between thermal energy production and demand. In pursuit of clean energy solutions in the agricultural sector, a 3942 m2 greenhouse in Yeoju-si, South Korea, is equipped with 231 solar thermal (ST) collectors, 117 photovoltaic thermal (PVT) collectors, four HHPs, two ground-source heat pumps (GSHPs), a 28,500 m3 borehole TES (BTES) unit, a 1040 m3 tank TES (TTES) unit, and three short-term TES units with capacities of 150 m3, 30 m3, and 30 m3. This study evaluates the long-term performance of the integrated hybrid renewable energy and thermal energy storage systems (HRETESSs) in meeting the greenhouse’s heating and cooling demands. Results indicate that the annual system performance efficiencies range from 25.3% to 68.5% for ST collectors and 31.9% to 72.2% for PVT collectors. The coefficient of performance (COP) during the heating season is 3.3 for GSHPs, 2.5 for HHPs using BTES as a source, and 3.6 for HHPs using TTES as a source. During the cooling season, the COP ranges from 5.3 to 5.7 for GSHPs and 1.84 to 2.83 for ASHPs. Notably, the HRETESS supplied 3.4% of its total heating energy directly from solar energy, 89.3% indirectly via heat pump utilization, and 7.3% is provided by auxiliary heating. This study provides valuable insights into the integration of HRETESSs to maximize greenhouse energy efficiency and supports the development of sustainable agricultural energy solutions, contributing to reduced greenhouse gas emissions and operational costs.
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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 | 0.000 |
| 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) | 0.000 | 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