A Low Cost Method of Snow Detection on Solar Panels and Sending Alerts
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
Photovoltaic systems are often installed in climates with considerable amount of snowfall and freezing rain in winter. It has been observed that the snow accumulation on a solar panel affects its performance and decreases the energy output. Snow on solar panels should be cleared as soon as possible to generate the maximum power. A low cost method of snow detection on solar panels found on field tests is proposed in this paper. The designed system is based on a low cost open-source Arduino Uno microcontroller that measures voltage and current output of a solar panel, and output of a LDR representing the irradiance. Arduino is also connected to a WIFI network and can send messages over the internet. Based on the sensors data, an algorithm is designed to accurately detect snow on solar panels and notify the owner via twitter about the current status. The designed low cost and very low power system has been tested in St. John's, Newfoundland, Canada (4734'28.9"N 5244'07.8"W) for three months of winter 2014. This paper presents details of the designed low cost alert system, algorithm and its performance results.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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