Abstract
AbstractThe ability and accuracy of machine learning techniques have been investigated for static modeling of the new-style wind turbine. The main aim of this study is to predict the electrical power (MP) of the new-style Savonius rotor as a function of aspect ratio, overlap ratio, number of the blade, wind speed, and rotational speed. In this paper, the EP of the proposed rotors was evaluated through Multilayer Feed-Forward Neural Network (MFFNN), and Cascade Feed-forward Neural Network (CFFNN) and Elman neural network (ENN) based on experimental data. Additionally, the proposed models were compared with previous models used in Ref. [6] to show the ability and accuracy of the proposed models. The results indicated that the ENN model has higher predictive accuracy compared to other models.KeywordsMachine learning modelsMechanical powerMultiple linear regressionsSavonius turbineNew-style
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