Formulation and Data-Driven Optimization for Maximizing the Photovoltaic Power with Tilt Angle Adjustment
Bibliographic record
Abstract
This paper reports on how the trade-off between the incident solar irradiance and conversion efficiency of a photovoltaic panel affects its power production. A neural network was developed through statistical analysis and a data-driven approach to accurately calculate the photovoltaic panel’s power output. Although the incident beam irradiance at a specified location directly relates to the tilt angle, the diffusion irradiance and energy conversion efficiency are nonlinearly dependent on a number of operating parameters, including cell temperature, wind speed, humidity, etc. A mathematical model was implemented to examine and cross-validate the physics of the neural network. Through simulation and comparison of the optimized results for different time horizons, it was found that hourly optimization can increase the energy generated from the photovoltaic panel by up to 42.07%. Additionally, compared to the base scenario, annually, monthly, and hourly optimization can result in 9.7%, 12.74%, and 24.78% more power, respectively. This study confirms the data-driven approach is an effective tool for optimizing solar power. It recommends adjusting the tilt angle of photovoltaic panels hourly, during the daily operation of maximizing the energy output and reducing solar costs.
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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".