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

Abstract

Accurate, efficient, and stable wind prediction systems for wind turbines are critical to ensuring the operational safety and optimum design of power systems. This study deliberated hyperparameter fine-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