Ordering of Solar Photovoltaic Panels using the MEREC-SPOTIS Hybrid Analytical Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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