Estimating Snow-Related Losses for Solar Photovoltaic Systems in Northern Territories of Canada
Bibliographic record
Abstract
Photovoltaic (PV) systems have recently gained significant attention in the northern provinces and territories of Canada. With the long daylight hours in summer, these regions benefit from an increase in PV power production. However, heavy snowfall during winter can cover the panels for extended periods and reduce or fully disrupt the performance of the systems. Therefore, having an accurate estimation of snow-related losses, known as snow loss, is essential to better understand the true potential of PV systems in these areas. In this paper, the power production data from four ground-mount utility-scale PV systems located in Yukon and the Northwest Territories, covering a combined period of nine years, are analyzed. A method to estimate the monthly ratios of snow loss is developed for each system based on the available data. The results show that snow can impact the system performance in these regions for 6 to 8 months each year. These estimated snow loss ratios can be used to adjust the monthly soiling rates in PV system design tools such as Helioscope and PVsyst.
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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 | 0.001 | 0.002 |
| 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".