In this study, the performances of various regression models are compared for predicting the wind turbine generation power using SCADA data from a wind-turbine in Turkey. The main objective of the study is to evaluate different machine learning algorithms to accurately predict the active power generation of the turbine under the influence of different parameters such as wind speed, wind direction, theoretical power curve. For this purpose, a total of 10 different regression models including CatBoost, XGB, Extra Trees, Random Forest, Decision Tree, Gradient Boosting, XGBRF, Linear, SVR and AdaBoost are applied. The success of the models was evaluated by performance metrics such as R² score and RMSE. The results revealed that the CatBoost Regressor model provided the highest accuracy with an R² score of 98.25%, while the XGB Regressor and Extra Trees Regressor models performed well with R² scores of 98.14% and 97.76%, respectively. Furthermore, the weekly analysis revealed that wind speed and the theoretical power curve have a significant impact on the active power generation of the turbine, while wind-direction shows a lower relationship. These findings show that power forecasting for wind-turbines can be made more efficient using machine learning techniques and that such models have significant potential in power generation forecasting.
Çetin et al. (Mon,) studied this question.