Characteristics of Residential Tracker Accuracy in Quantified Direct Beam Irradiance and Global Horizontal Irradiance
Bibliographic record
Abstract
An accurate solar tracker matches array angles with solar angles throughout the day. Many studies have used the power produced by a tracked PV array as a proxy to characterize a tracker’s accuracy. However, it is difficult to decouple the effects of tracker performance from other effects on power output. It was not found in the literature reviewed that there are studies that determine the accuracy of solar trackers by directly measuring the tracker angles. This study was an experiment to determine the accuracy of two small commercially available non-algorithm based solar trackers: the Zomeworks UTR-020 passive one axis solar tracker, and the Wattsun AZ-225 active electro-optical two axis solar tracker. Accuracy of the trackers was determined by measuring the tracking angles under varying conditions including direct beam irradiation (DBI) and global horizontal irradiance (GHI), and comparing to calculated sun angles. The results showed that the average azimuth angle accuracy of the Zomeworks UTR-020 is 75%, the average azimuth angle accuracy of the Wattsun AZ-225 is 88%, and the average elevation angle accuracy of the Wattsun is 89%. In addition, the results showed a weak correlation between the azimuth accuracy of the Zomeworks and DBF, a strong correlation between the azimuth accuracy of the Wattsun and DBF, and a moderate correlation between the elevation accuracy of the Wattsun and DBF. Moreover, the azimuth accuracy of the Wattsun was always higher than that of the Zomeworks under all DBF and GHI conditions.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".