Evaluation of PV Snow Loss Models in the East Coast of Canada Using AI Computer Vision
Bibliographic record
Abstract
Comprehensive understanding of the impact of snow cover on energy generation losses in photovoltaic arrays is needed to ensure realistic generation values are considered when developing, operating, or investing in solar systems in areas of colder climates. The current study uses an AI computer vision algorithm to process images capturing snow coverage on a 109 kW fixed tilt photovoltaic array in North Cape, PE, Canada. The algorithm calculates fractional snow coverage on the panels and is used in combination with site data to determine production losses due to snow cover. WEICan's results are compared to two predictive snow loss models prominent in the industry — the Townsend and the NREL models. It was observed that the absolute snow loss values on a monthly and annual basis were overestimated in both cases but could be significantly improved by using site specific model coefficients. The image analysis used in this study demonstrates how two prominent snow loss models perform using default and site specific model parameters and can also be used to indicate the snow losses that will be incurred for a windy coastal site in a northern latitude.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".