Effects of surroundings snow coverage and solar tracking on photovoltaic systems operating in Canada
Bibliographic record
Abstract
This paper deals with demonstrating the energy performance of solar tracking photovoltaic (PV) systems in Canada. In this study, a grid connected stand-alone PV system has been designed and coupled with four different tracking systems: fixed horizontal, fixed tilted, single-axis tracking, and dual-axis tracking. The performance analysis of the systems focuses on the variation of array irradiance, electricity generation, and efficiency without considerations for economic impacts at this stage. The simulation results show that the dual-axis tracking array provides the best performance over a year. It receives 33% more solar radiation and generates 36% more electricity than the tilted system. On clear winter days, compared to the tilted system, the dual-axis tracking system produces 32% and 29% more electricity in high albedo and low albedo conditions, respectively. High albedo due to surroundings snow coverage has been found to cause an increase of 3.1%, 5.8%, and 7.9% in electricity production of the tilted, single-axis tracking, and dual-axis tracking system respectively, over a winter. The results of this research support the idea that tracking the sun is effective on clear days and could be counterproductive on overcast days. Therefore, in high albedo conditions, it is recommended to track the sun and stay fixed once the sky becomes overcast.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".