An Experimental Platform for Quantifying the Impact of Snow Accumulation on Pyranometer Measurements for PV Applications in Eastern Canada
Bibliographic record
Abstract
The measurement of irradiance is essential for evaluating weather conditions and its various applications in fields such as hydrology, energy, and particularly for the estimation of photovoltaic system performance. In snowy environments, the reliability of irradiance measurements performed by pyranometers is even more critical, as it can be affected by snow accumulation on the domes of the measurement instruments. In this paper, we present the experimental platform of the 3IT Institute, located in Quebec in eastern region of Canada, used to quantify the impact of accumulated snow on four pyranometers from EKO: three unheated MS-60S models and one heated MS-80SH model, positioned differently around a photovoltaic power plant. The total irradiance (kWh/m2) measured per day by the four pyranometers was analyzed over 17 days from December 2024 to January 2025, while a laser-based snow depth sensor (SDMS-40) simultaneously measured the amount of snow accumulated on the ground. For snow depth values ranging from 0 to 14 cm, an average measurement discrepancy ranging from 11.8% to 73.2% was quantified by comparing the measurements made by the different pyranometers to the heated one, used as the reference value. The details of the experimentation and the measurement results presented in this paper could help in making decisions regarding technological choices and the positioning of instruments for irradiance measurement in snowy environments.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".