Best tilt of PV system in Canada: Effect of the sky radiation model and climate conditions
Bibliographic record
Abstract
This paper focuses on the best tilt angle of PV applications, to be related to the latitude and a latitude correction factor here presented. The analysis includes a series of 19 cities across Canada: latitude and local weather conditions are considered to define a correction angle correlation. This correction is expressed as a function of latitude, average annual weather conditions, and yearly climate variability, demonstrating strong alignment with “exact” outputs (correlation coefficient equal to 0.98 for different sky models). To ensure broad geographic coverage, Typical Meteorological Year hourly data were obtained from the Canadian Weather Year for Energy Calculation portal. The validity of the correction was assessed against various approaches and web tools results. Results were then compared with those from European cities at similar latitudes. Findings indicate that determining the optimum tilt angle requires accounting for latitude and site-specific climatic conditions, including snow cover: snowy regions benefit from higher tilts, emphasizing the relevance of considering accurate albedo in photovoltaic system design. Results suggest that this precise tilt calculation can yield annual insolation gains of up to 3.5% with respect to rule-of-thumb angles (i.e. tilt equal to latitude), even at lower latitudes, with variations in best tilts until 13°.
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 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".