The effectiveness of the use of solar panels in hotels of Durres area
Bibliographic record
Abstract
In the present study is analyzing the case of the application of solar energy with vacuum tube collectors for heating water for sanitary needs in hotels of Durres areas. In recent years there has been a special interest by the leading authorities to the EU countries and the U.S., for the use of renewable energies in general and solar energy in particular. This is mainly related with the reduction of environmental pollution from gas CO2, which directly influences the greenhouse effect and global warming. Unlike other forms of renewable energy, like wind turbines, photovoltaic panels and biomass usages, the use of solar panels for hot water, constitutes a reliable source in Albania. In the study were analyzed the types of solar panels used in our country and the world. Collector with vacuum tubes is selected for heating water, which constitute a new technology with an efficiency 40% higher than other types. For this case study were obtained tourism hotels in the coastal area of Durrës with a high level of solar intensity, which are calculated the cost for water heating with electric boiler (traditional method) and with solar panels. Results show a significant reduction of annual expenses up 3 times, compared with the use of electricity. While with interest it is the reduction of the amount of gas CO2, to achieve reduction of environmental pollution, and the reduction of global warming in general.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".