Noise Emission Assessment of a Utility-Scale Wind Power Plant: Case Study of a 90 MW Wind Power Plant in Mukdaharn Province, Northeastern Thailand
Bibliographic record
Abstract
The noise impact of wind power plants is one of the major reasons for social opposition to wind energy development. The complex noise model (ISO 9613) was employed to model the noise generated by 15 GW165-6.0 MW wind turbine generator units based on the manufacturer-defined acoustic profile. The wind resource at a hub height of the wind turbine generators (144 m agl) was first predicted based on computational fluid dynamics flow modeling. The model noise levels were mapped using ArcGIS and twenty-two receptors comprising houses, temples, and other places at varying distances within the project boundary. Likewise, to compare the noise levels of the wind turbine generators with different noise levels, the ambient noise was measured at selected four receptors. The results showed that the predicted noise was less than 70 dB(A) in the vicinity of the wind turbine generators, decreased down to 40-45 dB(A) within the project boundary, and was in the range of 35-40 dB(A) in the community area. The compared results showed that the ambient noise exceeds the noise levels from the wind turbine generators at all four receptor sites. Hence, wind power plants would not cause any additional noise pollution. Such studies are vital to providing awareness to the public based on proven scientific evidence to gain the public's trust and mitigate social opposition to wind power plants.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".