Time-Series vs Typical Meteorological Year Data: Verification of PV String Sizing and Design
Bibliographic record
Abstract
High-resolution weather data is needed for accurate photovoltaic (PV) system design. This study utilizes two commonly used datasets, Typical Meteorological Year (TMY) and Time Series (TS), both provided by the satellite-based solar model of Solargis. TMY datasets, derived from long-term historical time-series, simplify climate representation but do not necessarily account for short-term variability and uncommon weather events, which can result in inaccuracies in system design and performance predictions. In contrast, TS datasets include actual fluctuations and extreme conditions which are critical for precise system sizing and operational safety. Using Solargis Evaluate for PV design and simulation, case studies illustrate the significance of TS data in addressing extreme weather scenarios. For instance, during Storm Uri in Texas, TS data recorded an unprecedented low daytime temperature of-17.1 °C, which was not represented in the TMY dataset. This can lead to differences in the verification of design (string sizing) necessary for ensuring system operation during extremes. In addition, summer conditions in the same region showed TS data capturing higher temperatures compared to TMY.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".