Analysis and Modeling of CPV Performance Loss Factors in Humid Continental Climate
Why this work is in the frame
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Bibliographic record
Abstract
Local climate and environmental conditions can impact the performance of concentrator photovoltaic (CPV) systems. There is a lack of experimental performance analysis of CPV systems, especially in the region with high snowfall and very low temperature in winters. In this article, we present first a CPV system performance in humid continental climate and identify snow and frost as sources of losses that are not considered in conventional predictive models. We propose then a method to account for the negative effect of snow and frost on the system, by adding monthly soiling factors in the predictive model. The monthly soiling factors are modeled based on average monthly snow fall and ambient temperature. Applying this method, decrease in root-mean-square error (RMSE) between predicted and actual energy production from 24.51 to 5.07% validates our model in humid continental climate for CPV systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it