Predicting Audibility of and Annoyance to Wind Project Sounds Using Modeled Sound

Predicting Audibility of and Annoyance to Wind Project Sounds Using Modeled Sound

February 27, 2018

Third in a four-part series on results from a Berkeley Lab-led effort to characterize and help explain attitudes toward local wind power projects in the US. Data were collected randomly from 1,705 homeowners living within five miles of 250 U.S. wind power projects across 24 states with a focus on individuals within close proximity of the turbines (e.g., < 1 mile) who often evade data collection because they are so few in number. These data represent the first nationwide survey of wind power project neighbors in the United States and the largest such survey conducted in the world to-date.

This webinar will focus on: an investigation of various predictors of reported ability to hear turbines and stated sound annoyance, including modeled project sound levels, local background sound levels, objective measures of people and place, and self-reported subjective descriptors.

For this analysis, 15 wind power projects were over-sampled and 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.