Estimating Urban Wind Speeds and Wind Power Potentials Based on Machine Learning with City Fast Fluid Dynamics Training Data
Why this work is in the frame
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Bibliographic record
Abstract
Wind power is known as a major renewable and eco-friendly power generation source. As a clean and cost-effective energy source, wind power utilization has grown rapidly worldwide. A roof-mounted wind turbine is a wind power system that lowers energy transmission costs and benefits from wind power potential in urban areas. However, predicting wind power potential is a complex problem because of unpredictable wind patterns, particularly in urban areas. In this study, by using computational fluid dynamics (CFD) and the concept of nondimensionality, with the help of machine learning techniques, we demonstrate a new method for predicting the wind power potential of a cluster of roof-mounted wind turbines over an actual urban area in Montreal, Canada. CFD simulations are achieved using city fast fluid dynamics (CityFFD), developed for urban microclimate simulations. The random forest model trains data generated by CityFFD for wind prediction. The accuracy of CityFFD is investigated by modeling an actual urban area and comparing the numerical data with measured data from a local weather station. The proposed technique is demonstrated by estimating the wind power potential in the downtown area with more than 250 buildings for a long-term period (2020–2049).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it