Prediction of the Performance of a Sun Tracking Photovoltaic System using different Artificial Intelligence Techniques: Case Study in Zarqa, Jordan
Why this work is in the frame
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Bibliographic record
Abstract
The article proposes the use of Artificial Intelligence (AI) models to predict the performance of a sun tracking Photovoltaic (PV) system built-in Zarqa City, Jordan.The system is off-grid with various Azimuth angles and tilt angles.The study involved taking various measurements over a 6-month period.The prediction models employed Artificial Neural Networks (ANN) with five different prediction classifiers, namely, random forest, forest tree, multilayer perceptron (MLP), BPF regression, and linear regression, to predict the performance of the sun-tracking PV system using experimental data.Different metrics are used to demonstrate and validate the accuracy of the proposed models.It is found that all proposed prediction models are of great accuracy.The best prediction classifier is found to be a forest tree classifier with an R2 value of 99.79% and a minimum absolute relative error of 2.36%.Moreover, the least accurate prediction classifier is found to be the linear regression with an R2 of 95.27% and an absolute relative error of 25.71%.
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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 | 0.000 |
| 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 it