Wind Turbine Noise Prediction Using Random Forest Regression

Wind Turbine Noise Issues

Wind turbine noise.

The evidence for adequate sleep as a prerequisite for human health, particularly child health, is overwhelming. Governments have recently paidmuch attention to the effects of environmental noise on sleep duration and quality, and to how to reduce such noise. However, governments have also imposed noise from industrial wind turbines on large swathes of peaceful countryside. The impact of road, r...

Investigation of Random Subspace and Random Forest Regression Models Using Data with Injected Noise

The ensemble machine learning methods incorporating random subspace and random forest employing genetic fuzzy rule-based systems as base learning algorithms were developed in Matlab environment. The methods were applied to the real-world regression problem of predicting the prices of residential premises based on historical data of sales/purchase transactions. The accuracy of ensembles generate...

Wind turbine rotor acceleration: identification using gaussian regression

Gaussian processes prior model methods for data analysis are applied to wind turbine time series data to identify both rotor speed and rotor acceleration from a poor measurement of rotor speed. In so doing, two issues are addressed. Firstly, the rotor speed is extracted from a combined rotor speed and generator speed measurement. A novel adaptation of Gaussian process regression based on two in...

Predicting Annoyance by Wind Turbine Noise

While wind turbines have beneficial effects for the environment, they inevitably generate environmental noise. In order to protect residents against unacceptable levels of noise, exposure-response relationships are needed to predict the expected percentage of people annoyed or highly annoyed at a given level of wind turbine noise. Exposure-response relationships for wind turbine noise were deri...