A comprehensive comparative investigation on solar heating and cooling technologies from a thermo‐economic viewpoint—A dynamic simulation
Bibliographic record
Abstract
Abstract The yearly thermo‐economic performance is dynamically investigated for three solar heating and cooling systems: solar heating and absorption cooling (SHAC), solar heating and ejector cooling (SHEC), and heating and solar vapor compression cooling (HSVC). First, the effects of important design parameters on the thermo‐economic performance of the systems to supply the heating and cooling loads of the building are evaluated. The systems are parametrically analyzed with the weather conditions of Tehran, Iran. The results show that the life cycle costs (LCC) of the SHAC and HSVC systems are alike and much lower than those of the SHEC system. The HSVC system exhibits the best performance from exergetic and solar fraction viewpoints. The comparative analysis shows that the energy efficiencies of the SHAC and SHEC systems are higher in colder climatic conditions. However, the collector efficiency of the HSVC system declines in colder climates, mainly due to the lower solar intensities relative to in hotter climates. Further, the solar fraction of the SHAC system is higher than the SHEC technology under all climatic conditions. Moreover, higher values of solar fractions are obtained under colder weather conditions for the SHEC and HSVC systems. The best economic performance is observed for the SHAC and HSVC technologies, having significantly lower LCCs than the SHEC system. These lower LCCs under colder climatic conditions are due to the lower cost of supplying the heating load compared to the cooling load. Furthermore, all systems exhibit enhanced exergetic performance in colder weather conditions. The yearly thermo‐economic performance is dynamically investigated for three solar heating and cooling systems: SHAC, SHEC, and HSVC. In addition, the effects of important design parameters on the thermo‐economic performance of the systems to supply the heating and cooling loads of the building are evaluated.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".