Multi-Objective Energy-Noise Wind Farm Layout Optimization Under Land Use Constraints
Bibliographic record
Abstract
Recently, the environmental impact of wind farms has been receiving increasing attention. As land is more extensively exploited for onshore wind farms, they are more likely to be in proximity with human dwellings, infrastructure (e.g. roads, transmission lines) and environmental features (e.g. rivers, lakes, forests). As a result of regulatory constraints, this proximity causes significant portions of the wind farm terrain to become unusable for turbine placement. In this work, we present a constrained, continuous-variable model for layout optimization that takes noise and energy as objective functions, based on Jensen’s wake model and ISO-9613-2 noise calculations. A multi-objective genetic algorithm (NSGA-II) is used to solve the optimization problem, considering a set of land use constraints, which are handled with static and dynamic penalty functions. A set of test cases with different number of turbines and percentages of land availability are solved. Results from this bi-objective optimization model illustrate how the severity of the land use constraints affects the trade-off between energy generation and noise production.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".