Feasibility of Small Wind Turbines in Ontario: Integrating Power Curves with Wind Trends
Bibliographic record
Abstract
Micro-scale/small wind turbines, unlike larger utility-scale turbines, produce electricity at a rate of 300 W to 10 kW at their rated wind speed and are typically below 30 m in hub-height. These wind turbines have much more flexibility in their costs, maintenance and siting, owing to their size, and can provided wind energy in areas much less suited for direct supply to the grid system. In the future under climate change, the energy landscape will likely shift from the present centralized electricity generation and delivery system to a more distributed and locally-generated electricity and delivery system. In the new system configuration, the role of relatively small sustainable electricity generators like small wind turbines will likely become more prominent. However, the small wind industry has been substantially slow to progress in Ontario, Canada, and there is much debate over its viability in a growing energy dependent economy. This study seeks to demonstrate the performance of a small wind turbine, and speculate on its potential power output and trend over Ontario historically over the last 33 years using the North American Regional Reanalysis (NARR) data. We assessed the efficiency of a Bergey Excel 1 kW wind turbine at the pre-established Kortright Centre for Conservation test site, located north of Toronto. Using a novel approach, the Bergey optimized power curve was incorporated with reanalysis data to establish power output across Ontario at three-hour resolution. Small turbine-based wind power around the Great Lakes and eastern James Bay increased during winter and fall, contributing up to 10% of the annual electricity demand in some regions in Ontario. We purport that increases in power output are driven by long-term reductions in sea and lake ice concentrations affecting atmospheric stability in surrounding regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".