A Novel Computational Intelligence-Based Parameter Extraction of UAV-Integrated Photovoltaic System
Bibliographic record
Abstract
The precise modeling of unmanned aerial vehicle (UAV)-integrated photovoltaic (PV) systems is central for their implementation on UAVs, especially due to their continuous movement. Accurate modeling is essential for developing control methods, conducting performance studies, and designing the whole system considering PV system parameters. This original study presents a novel modeling procedure for comprehensive modeling of UAV-integrated PV modules using multi-input multi-output (MIMO) and multi-input single-output (MISO) machine learning models. These models are developed based on a historical environmental dataset and a randomly generated dataset representing flight conditions. Both datasets get processed during the preparation phase. In this phase, two sets of empirically derived group of modified equations (GME) of the PV module are utilized. In addition, the Whale Optimization Algorithm (WOA) is employed to optimize one set of GME containing PV system five unknown parameters. Moreover, the Dogleg Trust Region Algorithm (DTRA) is used as an unconstrained problem solver in each loop of WOA to solve a system of equations consisting of the unknown parameters. Finally, the MIMO and MISO models will be trained with the processed dataset. The proposed approach predicts UAV-integrated PV module behavior under diverse flight conditions, interprets panel movements, and is validated through experiments.
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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 | 0.000 | 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 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".