Exploring the prediction of wind turbine audibility and sound annoyance with A-weighted sound levels

TitleExploring the prediction of wind turbine audibility and sound annoyance with A-weighted sound levels
Publication TypeJournal Article
Year of PublicationIn Press
AuthorsHaac, Ryan, Landis, Matt, Kaliski, Ken, Ben Hoen, Joseph Rand, Jeremy Firestone, Gundula Hübner, Johannes Pohl, Debi Elliott
Abstract

As part of Lawrence Berkeley National Laboratory’s national cross-sectional study of individuals who live within 5 miles of a modern, utility-scale wind turbine, 15 wind power projects were selected as case studies and over-sampled. The same 15 wind power projects were modeled to estimate the sound levels at each respondent’s home. Also, a representation of background sound level for each respondent was extracted from a national dataset. Statistical analyses were conducted to estimate the acoustical contributions to one’s propensity for annoyance, and how these were affected by non-acoustic factors (e.g., project compensation, prior attitude toward the project, visibility, etc.). The results demonstrate that considering the interaction of a project’s modeled sound levels and the existing background sound levels improves the prediction of reported wind turbine audibility over only using modeled sound levels. Additionally, the sound-level drivers (modeled wind turbine sound level and background sound level) are poor predictors of very annoyed responses; one’s prior support for or opposition to a local project is the strongest predictor of very annoyed responses in the regression model.