Impact of Snow Depth on Single-Axis Tracked Bifacial Photovoltaic System Performance
Bibliographic record
Abstract
Photovoltaic (PV) capacity is rapidly expanding in mid-to-high latitude jurisdictions due to drastic decreases in PV system cost and global decarbonization efforts. However, uncertainty around performance impacts of latitude-specific conditions, such as ground-accumulated snow, contribute to investment risk. In this work, we employed DUET, our custom bifacial PV modelling tool, to study variable ground clearance resulting from snow accumulation. We model the impact of snow depth on a module in four generic, 2-in-portait single-axis tracked (SAT) systems with baseline ground clearances ranging from 1.6-2.8 m. Over the snowy season in Ottawa, Ontario <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\boldsymbol{45}^{\circ}\mathbf{N})$</tex> and Cambridge Bay, Nunavut <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\boldsymbol{69}^{\circ}\mathbf{N})$</tex> , Canada, rear insolation and energy yield decrease by 3.4-9.6% and 0.36-0.69%, respectively - comparable to annual structure shading and electrical mismatch loss factors. The average daily energy yield loss in both locations is 0.034 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> per centimeter of accumulated snow. When hourly energy yield loss throughout the snowy season is binned by hour of day, hourly averages peak at 1.2-1.4% loss, suggesting implications for real-time and short-term forecasting.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".