Boosting Solar Sustainability: Performance Assessment of Roof-Mounted PV Arrays Under Snow Considering Various Module Interconnection Schemes
Bibliographic record
Abstract
The transition to renewable energy sources is vital for achieving sustainability, and photovoltaic (PV) systems play a key role in this shift. However, their performance can be significantly affected in snowy conditions, where the irradiation and energy production are limited. This study addresses a critical gap in the literature by developing a MATLAB/Simulink model that considers the impacts of snow layering and removal on roof-mounted photovoltaic arrays. This study investigates various module interconnection schemes—including Series, Series-Parallel, Total-Cross-Tied, Bridge-Linked, and Honey-Comb—to determine their impact on energy efficiency in snowy environments. Based on the results, when the modules are fully covered by uniform snow, the power losses can increase from 38.9% to 93.2% for all interconnection schemes by increasing the accumulated snow from 1 cm to 5 cm. When the modules are covered by nonuniform snow and the snow removal is considered the TCT scheme has the minimum power losses and the maximum efficiency, depending on the accumulated snow pattern. For the worst scenario, the power loss is 70.1% for TCT, 71.7% for SP, 72% for HC, 72.3% for BL, and 76.7% for series interconnection. For the other scenarios, almost a similar trend can be observed where the TCT interconnection has the maximum efficiency, and the series interconnection has the minimum efficiency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".