Adaptive Prediction of the Performance of a Photovoltaic Solar Integrated System
Bibliographic record
Abstract
The performance of an experimental photovoltaic (PV) solar system is predicted using adaptive artificial neural networks (ANNs). The performance of the system is represented by its important efficiencies. An ANN model that predicts these efficiencies from relevant measurements exits in the literature. Adaptive online techniques are applied to the existing ANN model for the PV solar integrated system. The on-line ANN uses the error between the ANN predicted efficiency and the efficiency measurement from the appropriately selected sensors and efficiency laws to update the network's parameters recursively. The adaptation scheme is based on the Kaczmarz's algorithm and improves the ANN prediction accuracy when the PV solar system parts degrade, the date within the year changes and in the presence of modeling errors. Thus, the ANN prediction capability improves especially over the long time horizon. The adaptive model for the PV solar system can be used to estimate precisely the system parameters which will produce maximum efficiencies and consequently will enable the best design for the PV solar system.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".