An Analytic Hierarchy Process based approach for assessing the performance of photovoltaic solar power plants
Bibliographic record
Abstract
The key performance indicators are crucial for monitoring the performance of photovoltaic solar plants, thus significantly enhancing their overall efficiency. This critical role underscores the importance of making informed decisions regarding the evaluation criteria of these indicators, which would streamline the PV installations evaluation process. Despite the significant importance of this role, the weighting and prioritization of KPI selection criteria in the specific field of solar plants have not been addressed in the literature. The article aims to address this gap by developing a methodology that identifies the most relevant aspects for evaluating the performance of photovoltaic plants, while taking into account specific needs and evaluation contexts, integrating a hierarchical decomposition tree of criteria, which serves as a framework to guide criterion prioritization through collaborative weighting with the thematic team. This methodology incorporates the Analytic Hierarchy Process, a multicriteria analysis approach designed to assist decision-makers in addressing their specific needs, ensuring optimal alignment with the characteristics of the optimization problem, whether it involves single or multiple objectives.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".