Evaluation of the Application of Computational Model Machine Learning Methods to Simulate Wind Speed in Predicting the Production Capacity of the Swiss Basel Wind Farm

Abstract

The potential of machine learning algorithms to recognize complex process patterns has been shown in several recent studies that effectively used machine learning approaches. Several machine learning techniques were utilized to anticip ate the wind approach, which may enhance the stability and dependability of wind power facilities. Basel air wind speed (WS) is being modeled and predicted using an ensemble of light gradient enhancing machines and supplementary trees. In both instructional and experimental datasets, the three techniques were used to compare the accuracy of their predictions. There was a significant difference in performance between the Ensemble (light gradient boosting machine and an extra tree) and the other two techniques in terms of the assessment criterion measures, such as the mean absolute error (MAE) and the mean fundamental error percentage (MSE).