An unsupervised method for predicting photovoltaic potential in Canada
Bibliographic record
Abstract
To mitigate the effects of global climate change caused by fossil fuel emissions, Canada needs to reach net-zero emissions as soon as possible. However, for a country that relies heavily on non-renewable resources to heat homes, fuel transportation, and support industries, renewable alternatives must be reliable, efficient, and effective. One of the front-runners in sustainable energy solutions is solar power. Our team analyzed the photovoltaic (PV) potential of geographical sites across the country using data from the Canadian Weather Energy and Engineering Datasets (CWEEDS). Using k-means clustering, an unsupervised machine learning model, we placed 564 locations into 5 clusters and then predicted the PV potential for each cluster using a range of irradiance and radiation variables. Through plotting our results on scatter graphs, we concluded that the PV potential in most of Canada is much higher than the world average (4.11-6.96 kWh/m2). Furthermore, the province of Alberta—known for its tar sands and oil production—has the highest PV potential in the country. The province has the potential to become the leader in solar energy production in Canada. These findings can aid governments in optimizing their shift towards solar power. By identifying solar power as a strong alternative to fossil fuels, administrations can start working towards setting up solar farms in places where they would optimally serve Canadians in order to take the first step in decreasing our national carbon footprint.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".