Modeling of Snow-Covered Photovoltaic Modules
Bibliographic record
Abstract
Accurate modeling of photovoltaic (PV) modules is required to predict performance of PV systems in various climatic conditions, often far different than manufacturer specifications. Snowfall during cold months reduces output of PV modules. As the application of PV systems is increasing in cold areas, it is vitally important to address this issue through an appropriate method capable of estimating PV performance due to snow effect. This paper proposes a novel PV modeling approach that can represent instantaneous electrical characteristics of PV modules in the presence of uniform snow coverage. The proposed model utilizes the Bouguer-Lambert Law to estimate the level of insolation reaching surface of snow-covered PV cells. This is achieved by introducing an extinction coefficient which depends on the snow properties. To study the efficiency of PV cells at low insolation levels, a two-diode equivalent circuit model is employed. The simulation results of the proposed model are validated with experimental measurements from field tests for different commercial PV modules as well as real data collected by the SCADA system of a 12-MW grid-connected PV farm. Good agreement was observed between power generation results estimated from the proposed model and those obtained experimentally on snow-covered PV systems. This model would be helpful for researchers and PV systems developers in cold regions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".