A multi-year analysis of Canadian Arctic historical weather data in support of solar and wind renewable energy deployment
Bibliographic record
Abstract
This work explores the importance of renewable resource temporal distribution for solar and wind energy deployment in Arctic communities to meet building and ancillary loads. An analysis of ten years of historic weather data was performed for six locations in the Canadian Arctic to assess renewable resource variation. Simulations of similar capacity solar and wind generation systems were then coupled with the historic data to compare and contrast generation potential. This analysis highlighted the importance of considering hourly, daily, monthly, and year-to-year renewable generation when deploying solar and wind to the Arctic. As many northern communities in Canada have local electricity generation and distribution systems, and no connection to the continental grid, managing grid interactions effectively is crucial to the success of deployment, integration, and operation. The results for the solar energy analysis showed high consistency of production year-to-year. The results for the wind energy analysis showed that the annual outputs have significantly less variation than the year-to-year output of individual months for all the locations under study. For the high latitude locations studied, solar energy can still provide useful electricity generation output, but the more pronounced bias of the annual output to the summer months can leave several months with little or no output. The use of additional renewable sources is crucial in beginning to transition some electricity generating capacity within Arctic communities from being solely reliant on fossil fuels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".