Ordering of Solar Photovoltaic Panels using the MEREC-SPOTIS Hybrid Analytical Model
Bibliographic record
Abstract
In the quest for renewable energy sources to replace fossil fuels, solar energy has been gaining prominence on a global scale. Furthermore, the increasingly lower prices of solar panels make solar energy more competitive, thereby increasing the interest in the installation of photovoltaic systems in homes and businesses. This study aimed to rank alternatives for photovoltaic panels, employing the Method for Eliminating Effects on Criteria (MEREC) method combined with the Stable Preference Ordering Towards Ideal Solution (SPOTIS) method, both of which are advanced Multi-Criteria Decision-Making (MCDM) methods. These methods were applied to evaluate solar panels based on criteria such as Power (W), Price (R$), Weight (Kg), Operating Temperature (°C), and Warranty (years). As a result, the SPOTIS method, using the weights generated by the MEREC method, ranked the brands in the following order: 1st - Shinefar; 2nd - JA Solar; 3rd - Canadian Solar; 4th - Amerisolar. This article made a significant contribution to society and scientific research in the field of Operations Research, as the methodology applied is adaptable to various commercial and industrial contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".