Deep Learning-Driven Dual-Axis Tilting Solar Panel System for Enhanced Energy Efficiency and Sustainability
Bibliographic record
Abstract
The accelerating global push toward decarbonization has amplified the need for intelligent, efficient, and cost-effective renewable energy technologies. This research presents the design, development, and experimental validation of a deep learning-based dual-axis solar tracking system to maximize solar energy capture and improve power conversion efficiency. Conducted in London, Ontario, Canada, the experimental study demonstrates how predictive modelling, informed by solar trajectory data, enables real-time adjustments of both tilt and azimuth angles to optimize irradiance absorption. Compared to conventional fixed- and other dual-axis systems, the proposed model achieved a $10.45 \%$ increase in voltage output and a significant reduction in the mean square error in solar position prediction. By eliminating the need for complex sensor arrays and simplifying circuit design, the system also reduces implementation costs and operational power consumption. These enhancements position the system as a viable alternative to traditional tracking solutions. Overall, the study underscores the transformative potential of artificial intelligence in enabling smart and scalable solar energy systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".