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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 fin...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続きを見る
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