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Wind is a renewable energy resource but its intermittent nature poses some issues for transmission system operators, wind farms, and the electricity market. To address these issues, wind power forecasting is crucial. In this study, the data were obtained from a turbine anemometer in the 1-year time horizon of 10 min for wind power forecasting. Models are designed with univariate long short-term memory, multivariate long short-term memory, multilayer perceptron (MLP), extreme gradient boosting (XGBOOST), convolutional neural networks (CNNs), long short-term memory, gated recurrent unit (GRU), and random forest (RF) algorithms. Outliers in the data are cleaned with the isolation forest algorithm. Hyperparameter optimization of the models, designed with machine learning algorithms for a selected month from each season (January, April, July, and November) was made with Bayesian optimization. The success of the models was measured by mean absolute error, root mean square error, and determination coefficient. Among the designed models, for January, April, and November, the multivariate long short-term memory model gave the most successful results with 0.926, 0.961, and 0.946 determination coefficient values, respectively. For July, the XGBOOST model provided the most successful result with 0.916 value of the determination coefficient
Yelgeç et al. (Mon,) studied this question.