Wind power forecasting using random forests
The present thesis investigated using the random forest machine learning algorithm for wind power forecasting. Meteorological prognoses for wind speed, wind direction, gust winds, and humidity were used. For historical data, wind minimum and temperature was also included. The results were evaluated using root means square error (RMSE), mean absolute error (MAE), normalized mean absolute error (NMA
