Factors Affecting Photovoltaic System Output in a Sub-Arctic Climate
Bibliographic record
Abstract
Photovoltaic arrays in the Arctic have been observed to produce power at values higher than their rated capacity. A solar photovoltaic (PV) array’s efficiency depends on the PV cell temperature, which is based on the balance between solar isolation and heat loss. Two PV arrays in Iqaluit, Nunavut, Canada were studied to estimate the possible effects of panel cooling and albedo on the array efficiency. PV power (W) output data from the inverter and ambient temperature and wind speed data from Environment Canada from 2017 were used to estimate the effect of ambient temperature and wind speed on the solar PV array efficiency. These data were then used to estimate the horizontal solar irradiance (G) at the locations in Iqaluit. The first array has a PV panel reference efficiency of 15.89%, but performed at efficiencies of 16.1% to 18.8%. The efficiencies for the second array on the same days were 16.4% to 19.1% versus the PV panel reference efficiency of 16.16 %. Considering an energy-weighted average of the efficiency enhancements for one clear and sunny day in each month, designers can expect the mean annual power output to be 4% to 7% above the rated output. On selected clear and sunny winter, spring and summer days, during the period when both arrays were not affected by shading, the average difference in back calculated G between the arrays was 6 W/m² on the winter day while for the spring and summer day it was 6 W/m² and 28 W/m². For the spring and summer, these represents deviations of 1% and 5%, respectively.
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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 | 0.000 | 0.001 |
| 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".