Exploring the untapped potential of solar photovoltaic energy at a smart campus: Shadow and cloud analyses
Bibliographic record
Abstract
Solar energy is abundant, and technological advances have made solar energy systems more affordable than ever before. Using photovoltaic (PV) systems could significantly reduce our reliance on fossil fuels, and facilitate sustainable energy uses. Solar power utilities, such as self-compacting disposal bins could be used to enhance waste management processes. This is particularly important in Canada, where $3.3 billion was spent on waste management systems in 2016. In this study, solar irradiance and climatic conditions at eight locations on a University campus in Regina, Saskatchewan, are studied. Results suggest that solar utilities with automatically adjusting PV receivers could increase energy capture between 18.7 – 27.5%. Temporally, solar irradiance was similar in June and July, but lower in August. Statistical analysis found that some locations tended to be more susceptible to shadow effects. The results highlight the importance of spatial allocations of these small smart disposal bin systems. Regression analysis found that temperature was the most significant factor when relating climate to solar irradiance. The use of smart disposal bins fits well with the University’s 2020–2025 Strategic Plan of reduction in ecological footprint.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".