Optimization of photovoltaic panel tilt angle for short periods of time or multiple reorientations
Bibliographic record
Abstract
Photovoltaic installations typically use fixed-mount photovoltaics (PV) panels with a constant orientation throughout the year. However, this does not maximize the energy output since the irradiance received by the panels depends on the sun position and the weather. This paper presents a novel approach to maximize the energy produced by fixed-mount PV panels for short-term and for permanent PV installations. For permanent installations, we considered a multiple-tilt scenario where the panel orientation is modified throughout the year. We developed a bi-layer algorithm to optimize the angles and timing of adjustments. Our method has been implemented in an open-source software, allowing optimal orientations and dates to be calculated for any installation. The optimal dates and the optimal angles have been successfully calculated for PV panels located at Reykjavik (Iceland), Sherbrooke (Canada), Quito (Ecuador), and Brasília (Brazil). We found that two reorientations per year were the most suitable option for all locations, resulting in a 3 % to 4.8 % gain in annual energy production compared to no reorientation. For short-term installations, using optimal orientation can improve energy production, with for instance 13 % improvement for a monthly installation in Brasilia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".