A multiple learning moth flame optimization algorithm with probability-based chaotic strategy for the parameters estimation of photovoltaic models
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
The parameters of the photovoltaic (PV) models affect the accuracy in the evaluation and control of PV systems. To estimate the parameters of various PV models accurately and reliably, we propose a multiple learning moth flame optimization algorithm with a probability-based chaotic strategy (MLMFO-PBCS). In MLMFO-PBCS, the multiple learning strategy effectively combines the information of flame and moth population in different stages of iteration, providing more chances for moths to update and supplying eminent exploration and exploitation capabilities. Moreover, a probability-based chaotic strategy is introduced to the global optimal solution on each iteration so that a promising solution can be established to update the worst moth, avoiding premature and enhancing the exploitation ability. The proposed MLMFO-PBCS has been used to evaluate parameters of different PV models including single diode, double diode, and PV module. Comprehensive experimental results indicate that MLMFO-PBCS is highly competitive on parameter estimations of PV models in accuracy, reliability, and convergence speed, compared with all compared algorithms.
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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 | 0.000 | 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 it