Promoting Wind Energy by Robust Wind Speed Forecasting Using Machine Learning Algorithms Optimization

e-tuning of ten Machine Learning (ML) models to obtain the best short-term wind speed forecasting model by evaluating the Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), Correlation, and runtime. The Random Forest (RF) and gradient-boosted tree (GBT) had the best overall performance; however, RF has a much longer training time than GBT. This paper's findings can assist researchers and practitioners in developing the most effective data-driven methods for wind speed and power-generated forecasting. Keywords: data mining; hyper parameter; RapidMiner