Estimation of wind speed: A data-driven approach

A method for prediction of wind speed at a selected location based on the data collected at neighborhood locations is presented. The affinity of wind speeds measured at different locations is defined by Pearson’s correlation coefficient. Five turbines with similar wind conditions are selected among 30 wind turbines for in-depth analysis. The wind data from these turbines are used to predict wind speed at a selected location. A neural network ensemble is used to predict the value of wind speed at the turbine of interest. The models have been tested and the computational results are discussed. The results demonstrate that a higher Pearson’s correlation coefficient between the wind speeds measured at different turbines has produced better prediction accuracy for the same training and test scenario.