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$(\boldsymbol{45}^{\circ}\mathbf{N})$and Cambridge Bay, Nunavut$(\boldsymbol{69}^{\circ}\mathbf{N})$, 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%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.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".