Measuring and modeling the effect of snow on photovoltaic system performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Today's demanding project financing climate requires developers to hone annual photovoltaic (PV) energy estimates with unprecedented accuracy - and to back the estimates with meaningful long-term performance guarantees. With some snowy locales in the U.S. and southern Canada becoming increasingly popular for MW-scale PV systems, lenders are now requiring that snow losses be estimated as part of their energy simulations. The literature is exceptionally thin on this subject - we have been unable to find even a single side by side study that directly quantifies the difference between an always-clean array versus an identical one left to naturally accumulate and shed snow. This paper describes the design and reports results for a side by side PV test bed installed in California near Lake Tahoe in December 2009. It has been designed to gauge the energy loss due to snow for three common tilt angles. Results from the first winter are presented, with insights for future model development and ongoing measurements.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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